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Brodsky V, Ullah E, Bychkov A, Song AH, Walk EE, Louis P, Rasool G, Singh RS, Mahmood F, Bui MM, Parwani AV. Generative Artificial Intelligence in Anatomic Pathology. Arch Pathol Lab Med 2025; 149:298-318. [PMID: 39836377 DOI: 10.5858/arpa.2024-0215-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2024] [Indexed: 01/22/2025]
Abstract
CONTEXT.— Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. OBJECTIVE.— To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. DATA SOURCES.— A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. CONCLUSIONS.— Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.
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Affiliation(s)
- Victor Brodsky
- From the Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri (Brodsky)
| | - Ehsan Ullah
- the Department of Surgery, Health New Zealand, Counties Manukau, New Zealand (Ullah)
| | - Andrey Bychkov
- the Department of Pathology, Kameda Medical Center, Kamogawa City, Chiba Prefecture, Japan (Bychkov)
- the Department of Pathology, Nagasaki University, Nagasaki, Japan (Bychkov)
| | - Andrew H Song
- the Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (Song, Mahmood)
| | - Eric E Walk
- Office of the Chief Medical Officer, PathAI, Boston, Massachusetts (Walk)
| | - Peter Louis
- the Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey (Louis)
| | - Ghulam Rasool
- the Department of Oncologic Sciences, Morsani College of Medicine and Department of Electrical Engineering, University of South Florida, Tampa (Rasool)
- the Department of Machine Learning, Moffitt Cancer Center and Research Institute, Tampa, Florida (Rasool)
- Department of Machine Learning, Neuro-Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida (Rasool)
| | - Rajendra S Singh
- Dermatopathology and Digital Pathology, Summit Health, Berkley Heights, New Jersey (Singh)
| | - Faisal Mahmood
- the Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (Song, Mahmood)
| | - Marilyn M Bui
- Department of Machine Learning, Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida (Bui)
| | - Anil V Parwani
- the Department of Pathology, The Ohio State University, Columbus (Parwani)
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Goel I, Bhaskar Y, Kumar N, Singh S, Amanullah M, Dhar R, Karmakar S. Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation. Front Digit Health 2025; 7:1550407. [PMID: 40103737 PMCID: PMC11913822 DOI: 10.3389/fdgth.2025.1550407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Accepted: 02/17/2025] [Indexed: 03/20/2025] Open
Abstract
Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.
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Affiliation(s)
- Isha Goel
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), New Delhi, India
- Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Yogendra Bhaskar
- ICMR Computational Genomics Centre, Indian Council of Medical Research (ICMR), New Delhi, India
| | - Nand Kumar
- Department of Psychiatry, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Sunil Singh
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Mohammed Amanullah
- Department of Clinical Biochemistry, College of Medicine, King Khalid University, Abha, Saudi Arabia
| | - Ruby Dhar
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Subhradip Karmakar
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), New Delhi, India
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Altobelli E, Angeletti PM, Ciancaglini M, Petrocelli R. The Future of Breast Cancer Organized Screening Program Through Artificial Intelligence: A Scoping Review. Healthcare (Basel) 2025; 13:378. [PMID: 39997253 PMCID: PMC11855082 DOI: 10.3390/healthcare13040378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2025] [Revised: 02/01/2025] [Accepted: 02/05/2025] [Indexed: 02/26/2025] Open
Abstract
Objective: The aim of this scoping review was to evaluate whether artificial intelligence integrated into breast cancer screening work strategies could help resolve some diagnostic issues that still remain. Methods: PubMed, Web of Science, and Scopus were consulted. The literature research was updated to 28 May 2024. The PRISMA method of selecting articles was used. The articles were classified according to the type of publication (meta-analysis, trial, prospective, and retrospective studies); moreover, retrospective studies were based on citizen recruitment (organized screening vs. spontaneous screening and a combination of both). Results: Meta-analyses showed that AI had an effective reduction in the radiologists' reading time of radiological images, with a variation from 17 to 91%. Furthermore, they highlighted how the use of artificial intelligence software improved the diagnostic accuracy. Systematic review speculated that AI could reduce false negatives and positives and detect subtle abnormalities missed by human observers. DR with AI results from organized screening showed a higher recall rate, specificity, and PPV. Data from opportunistic screening found that AI could reduce interval cancer with a corresponding reduction in serious outcome. Nevertheless, the analysis of this review suggests that the study of breast density and interval cancer still requires numerous applications. Conclusions: Artificial intelligence appears to be a promising technology for health, with consequences that can have a major impact on healthcare systems. Where screening is opportunistic and involves only one human reader, the use of AI can increase diagnostic performance enough to equal that of double human reading.
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Affiliation(s)
- Emma Altobelli
- Department of Life, Health and Environmental Sciences, Section of Epidemiology and Biostatistics Unit, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Paolo Matteo Angeletti
- Department of Life, Health and Environmental Sciences, Section of Epidemiology and Biostatistics Unit, University of L’Aquila, 67100 L’Aquila, Italy;
- Cardiovascular Department, UO of Cardiac Anesthesia of the IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Marco Ciancaglini
- Department of Life, Health and Environmental Sciences, Section of Clinical and Molecular Medicine, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Reimondo Petrocelli
- Public Health Unit, Azienda Sanitaria Regionale Molise, 86100 Campobasso, Italy;
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van Winkel SL, Samperna R, Loehrer EA, Kroes J, Rodriguez-Ruiz A, Mann RM. Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI. Radiology 2025; 314:e233067. [PMID: 39903070 DOI: 10.1148/radiol.233067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2025]
Abstract
Background Combined mammography and MRI screening is not universally accessible for women with intermediate breast cancer risk due to limited MRI resources. Selecting women for MRI by assessing their mammogram may enable more resource-effective screening. Purpose To explore the feasibility of using a commercial artificial intelligence (AI) system at mammography to stratify women with intermediate risk for supplemental MRI or no MRI. Materials and Methods This retrospective study included consecutive women with intermediate risk screened with mammography and MRI between January 2003 and January 2020 at a Dutch university medical center. An AI system was used to independently evaluate all mammograms, providing a case-based score that ranked the likelihood of a malignancy on a scale of 1-10. Different AI thresholds for supplemental MRI screening were tested, balancing cancer detection against the number of women needing to undergo MRI. Univariate analyses were used to explore associations between personal factors (age, breast density, and duration of screening participation) and AI results. Results In 760 women (mean age, 48.9 years ± 10.5 [SD]), 2819 combined screening examinations were performed, and 37 breast cancers were detected. Use of AI at mammography achieved an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.63, 0.81) for the entire intermediate-risk population and 0.81 (95% CI: 0.69, 0.93) for women with prior breast cancer. Using a threshold score of 5, 31 of 37 (84%) breast cancers were detected, including 13 of 19 (68%) mammographically occult cancers, at a supplemental breast MRI rate of 50% (1409 of 2819 examinations). No significant association between breast density or age and the probability of a false-negative AI result was found. Conclusion Using AI at mammography to select women for supplemental MRI effectively identified women with higher breast cancer risk in an intermediate-risk population, including women with mammographically occult cancers. AI selection of women with intermediate risk for supplemental MRI screening has the potential to reduce screening burden and costs, while maintaining a high cancer detection rate. © RSNA, 2025.
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Affiliation(s)
- Suzanne L van Winkel
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Riccardo Samperna
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Elizabeth A Loehrer
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Jaap Kroes
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Alejandro Rodriguez-Ruiz
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
| | - Ritse M Mann
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.)
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Adapa K, Gupta A, Singh S, Kaur H, Trikha A, Sharma A, Rahul K. A real world evaluation of an innovative artificial intelligence tool for population-level breast cancer screening. NPJ Digit Med 2025; 8:2. [PMID: 39748126 PMCID: PMC11696541 DOI: 10.1038/s41746-024-01368-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Accepted: 12/02/2024] [Indexed: 01/04/2025] Open
Abstract
In resource-constrained countries like India, mammography-based breast screening is challenging to implement. This state-wide study, funded by the Government of Punjab, evaluated the use of Thermalytix, a low-cost, radiation-free AI tool, for breast cancer screening. Community health workers, trained to raise awareness, mobilized women aged 30 and above for screening. Thermalytix triaged women into five risk categories based on thermal images, with high-risk women recalled for diagnostic imaging. Over 18 months, 15,069 women were screened across 183 locations in Punjab. The median age was 41 years, and 69.9% were asymptomatic. Of 460 women testing positive (recall rate 3.1%), 268 underwent follow-up imaging, and 27 were confirmed with breast cancer, yielding a detection rate of 0.18%. The positive predictive value of biopsy performed was 81.81%, and the median diagnostic interval was 21 days, with therapy initiation within 30 days. The study demonstrates the potential of Thermalytix for effective population-level breast cancer screening in low-resource settings.
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Affiliation(s)
- Karthik Adapa
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India.
- Department of Health Systems Development, World Health Organization-South East Asia Regional Office, Delhi, India.
| | - Ashu Gupta
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India
| | - Sandeep Singh
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India
| | - Hitinder Kaur
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India
| | - Abhinav Trikha
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India
| | - Ajoy Sharma
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India
| | - Kumar Rahul
- Department of Health and Family Welfare, Government of Punjab, Chandigarh, India
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Núñez L, Ferreira C, Mojtahed A, Lamb H, Cappio S, Husainy MA, Dennis A, Pansini M. Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study. Abdom Radiol (NY) 2024; 49:4264-4272. [PMID: 39123052 PMCID: PMC11522103 DOI: 10.1007/s00261-024-04507-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 07/16/2024] [Accepted: 07/21/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Currently this is performed manually, generally by trained radiologists, who are seeing an inexorable growth in their workload. Integrating artificial intelligence (AI) and non-radiologist personnel into the workflow potentially addresses the increasing workload without sacrificing accuracy. This study evaluated the accuracy of non-radiologist technicians in liver cancer imaging compared with radiologists, both assisted by AI. METHODS Non-contrast T1-weighted MRI data from 18 colorectal liver metastasis patients were analyzed using an AI-enabled decision support tool that enables non-radiology trained technicians to perform key liver measurements. Three non-radiologist, experienced operators and three radiologists performed whole liver segmentation, Couinaud segment segmentation, and the detection and measurements of lesions aided by AI-generated delineations. Agreement between radiologists and non-radiologists was assessed using the intraclass correlation coefficient (ICC). Two additional radiologists adjudicated any lesion detection discrepancies. RESULTS Whole liver volume showed high levels of agreement between the non-radiologist and radiologist groups (ICC = 0.99). The Couinaud segment volumetry ICC range was 0.77-0.96. Both groups identified the same 41 lesions. As well, the non-radiologist group identified seven more structures which were also confirmed as lesions by the adjudicators. Lesion diameter categorization agreement was 90%, Couinaud localization 91.9%. Within-group variability was comparable for lesion measurements. CONCLUSION With AI assistance, non-radiologist experienced operators showed good agreement with radiologists for quantifying whole liver volume, Couinaud segment volume, and the detection and measurement of lesions in patients with known liver cancer. This AI-assisted non-radiologist approach has potential to reduce the stress on radiologists without compromising accuracy.
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Affiliation(s)
- Luis Núñez
- Perspectum Ltd., Gemini One, 5520 John Smith Drive, Oxford, OX4 2LL, UK.
| | - Carlos Ferreira
- Perspectum Ltd., Gemini One, 5520 John Smith Drive, Oxford, OX4 2LL, UK
| | - Amirkasra Mojtahed
- Division of Abdominal Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Hildo Lamb
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Stefano Cappio
- Clinica Di Radiologia EOC, Istituto Di Imaging Della Svizzera Italiana (IIMSI), Lugano, Switzerland
| | - Mohammad Ali Husainy
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Andrea Dennis
- Perspectum Ltd., Gemini One, 5520 John Smith Drive, Oxford, OX4 2LL, UK
| | - Michele Pansini
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Clinica Di Radiologia EOC, Istituto Di Imaging Della Svizzera Italiana (IIMSI), Lugano, Switzerland
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Branco PESC, Franco AHS, de Oliveira AP, Carneiro IMC, de Carvalho LMC, de Souza JIN, Leandro DR, Cândido EB. Artificial intelligence in mammography: a systematic review of the external validation. REVISTA BRASILEIRA DE GINECOLOGIA E OBSTETRÍCIA 2024; 46:e-rbgo71. [PMID: 39380589 PMCID: PMC11460423 DOI: 10.61622/rbgo/2024rbgo71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/27/2024] [Indexed: 10/10/2024] Open
Abstract
Objective To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Data source Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms "Artificial Intelligence," "Mammography," and their respective MeSH terms. We filtered publications from the past ten years (2014 - 2024) and in English. Study selection A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work. Data collection The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity. Data synthesis It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone. Conclusion AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.
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Affiliation(s)
| | - Adriane Helena Silva Franco
- Faculdade de Medicina Faculdade de Minas Belo HorizonteMG Brazil Faculdade de Medicina, Faculdade de Minas, Belo Horizonte, MG, Brazil
| | - Amanda Prates de Oliveira
- Faculdade de Medicina Faculdade de Minas Belo HorizonteMG Brazil Faculdade de Medicina, Faculdade de Minas, Belo Horizonte, MG, Brazil
| | - Isabela Maurício Costa Carneiro
- Faculdade de Medicina Faculdade de Minas Belo HorizonteMG Brazil Faculdade de Medicina, Faculdade de Minas, Belo Horizonte, MG, Brazil
| | | | - Jonathan Igor Nunes de Souza
- Faculdade de Medicina Universidade Federal dos Vales do Jequitinhonha e Mucuri DiamantinaMG Brazil Faculdade de Medicina, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, MG, Brazil
| | - Danniel Rodrigo Leandro
- Universidade Federal de Minas Gerais Belo HorizonteMG Brazil Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Eduardo Batista Cândido
- Universidade Federal de Minas Gerais Belo HorizonteMG Brazil Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
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Condon JJJ, Trinh V, Hall KA, Reintals M, Holmes AS, Oakden-Rayner L, Palmer LJ. Impact of Transfer Learning Using Local Data on Performance of a Deep Learning Model for Screening Mammography. Radiol Artif Intell 2024; 6:e230383. [PMID: 38717291 PMCID: PMC11294949 DOI: 10.1148/ryai.230383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 03/25/2024] [Accepted: 04/24/2024] [Indexed: 06/21/2024]
Abstract
Purpose To investigate the issues of generalizability and replication of deep learning models by assessing performance of a screening mammography deep learning system developed at New York University (NYU) on a local Australian dataset. Materials and Methods In this retrospective study, all individuals with biopsy or surgical pathology-proven lesions and age-matched controls were identified from a South Australian public mammography screening program (January 2010 to December 2016). The primary outcome was deep learning system performance-measured with area under the receiver operating characteristic curve (AUC)-in classifying invasive breast cancer or ductal carcinoma in situ (n = 425) versus no malignancy (n = 490) or benign lesions (n = 44). The NYU system, including models without (NYU1) and with (NYU2) heatmaps, was tested in its original form, after training from scratch (without transfer learning), and after retraining with transfer learning. Results The local test set comprised 959 individuals (mean age, 62.5 years ± 8.5 [SD]; all female). The original AUCs for the NYU1 and NYU2 models were 0.83 (95% CI: 0.82, 0.84) and 0.89 (95% CI: 0.88, 0.89), respectively. When NYU1 and NYU2 were applied in their original form to the local test set, the AUCs were 0.76 (95% CI: 0.73, 0.79) and 0.84 (95% CI: 0.82, 0.87), respectively. After local training without transfer learning, the AUCs were 0.66 (95% CI: 0.62, 0.69) and 0.86 (95% CI: 0.84, 0.88). After retraining with transfer learning, the AUCs were 0.82 (95% CI: 0.80, 0.85) and 0.86 (95% CI: 0.84, 0.88). Conclusion A deep learning system developed using a U.S. dataset showed reduced performance when applied "out of the box" to an Australian dataset. Local retraining with transfer learning using available model weights improved model performance. Keywords: Screening Mammography, Convolutional Neural Network (CNN), Deep Learning Algorithms, Breast Cancer Supplemental material is available for this article. © RSNA, 2024 See also commentary by Cadrin-Chênevert in this issue.
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Affiliation(s)
- James J. J. Condon
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
| | - Vincent Trinh
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
| | - Kelly A. Hall
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
| | - Michelle Reintals
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
| | - Andrew S. Holmes
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
| | - Lauren Oakden-Rayner
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
| | - Lyle J. Palmer
- From the Australian Institute for Machine Learning (J.J.J.C., V.T.,
L.O.R., L.J.P.) and School of Public Health (J.J.J.C., V.T., K.A.H., L.O.R.,
L.J.P.), University of Adelaide, N Terrace, Adelaide, South Australia 5005,
Australia; and BreastScreen SA, Adelaide, South Australia, Australia (M.R.,
A.S.H.)
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9
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Bhalla D, Rangarajan K, Chandra T, Banerjee S, Arora C. Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature. Indian J Radiol Imaging 2024; 34:469-487. [PMID: 38912238 PMCID: PMC11188703 DOI: 10.1055/s-0043-1775737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
Background Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. Purpose To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. Methods The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. Results A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. Conclusion While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
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Affiliation(s)
- Deeksha Bhalla
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Krithika Rangarajan
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Tany Chandra
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Subhashis Banerjee
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India
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10
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Elías-Cabot E, Romero-Martín S, Raya-Povedano JL, Brehl AK, Álvarez-Benito M. Impact of real-life use of artificial intelligence as support for human reading in a population-based breast cancer screening program with mammography and tomosynthesis. Eur Radiol 2024; 34:3958-3966. [PMID: 37975920 PMCID: PMC11166767 DOI: 10.1007/s00330-023-10426-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/11/2023] [Accepted: 10/01/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES To evaluate the impact of using an artificial intelligence (AI) system as support for human double reading in a real-life scenario of a breast cancer screening program with digital mammography (DM) or digital breast tomosynthesis (DBT). MATERIAL AND METHODS We analyzed the performance of double reading screening with mammography and tomosynthesis after implementarion of AI as decision support. The study group consisted of a consecutive cohort of 1 year screening between March 2021 and March 2022 where double reading was performed with concurrent AI support that automatically detects and highlights lesions suspicious of breast cancer in mammography and tomosynthesis. Screening performance was measured as cancer detection rate (CDR), recall rate (RR), and positive predictive value (PPV) of recalls. Performance in the study group was compared using a McNemar test to a control group that included a screening cohort of the same size, recorded just prior to the implementation of AI. RESULTS A total of 11,998 women (mean age 57.59 years ± 5.8 [sd]) were included in the study group (5049 DM and 6949 DBT). Comparing global results (including DM and DBT) of double reading with vs. without AI support, we observed an increase in CDR, PPV, and RR by 3.2/‰ (5.8 vs. 9; p < 0.001), 4% (10.6 vs. 14.6; p < 0.001), and 0.7% (5.4 vs. 6.1; p < 0.001) respectively. CONCLUSION AI used as support for human double reading in a real-life breast cancer screening program with DM and DBT increases CDR and PPV of the recalled women. CLINICAL RELEVANCE STATEMENT Artificial intelligence as support for human double reading improves accuracy in a real-life breast cancer screening program both in digital mammography and digital breast tomosynthesis. KEY POINTS • AI systems based on deep learning technology offer potential for improving breast cancer screening programs. • Using artificial intelligence as support for reading improves radiologists' performance in breast cancer screening programs with mammography or tomosynthesis. • Artificial intelligence used concurrently with human reading in clinical screening practice increases breast cancer detection rate and positive predictive value of the recalled women.
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Affiliation(s)
- Esperanza Elías-Cabot
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain.
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Menéndez Pidal Avenue s/n, 14004, Córdoba, Spain.
- University of Córdoba, Córdoba, Spain.
| | - Sara Romero-Martín
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Menéndez Pidal Avenue s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - José Luis Raya-Povedano
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Menéndez Pidal Avenue s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - A-K Brehl
- ScreenPoint Medical BV, Toernooiveld 300, 6525 EC, Nijmegen, The Netherlands
| | - Marina Álvarez-Benito
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Menéndez Pidal Avenue s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
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11
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Guenoun D, Zins M, Champsaur P, Thomassin-Naggara I. French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative). Diagn Interv Imaging 2024; 105:74-81. [PMID: 37749026 DOI: 10.1016/j.diii.2023.09.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology. MATERIALS AND METHODS The RAND-UCLA Appropriateness Method was chosen by expert radiologists from the DRIM France IA group for this statement paper. The study, initiated by the radiology community, involved seven steps including literature review, template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting. RESULTS The panel consisted of seven software vendors, three for bone fracture detection using conventional radiology and four for breast cancer detection using mammography. A consensus was reached on various aspects, including general target, main objective, certification marking, integration, expression of results, forensic aspects and cybersecurity, performance and scientific validation, description of the company and economic details, possible usage scenarios in the clinical workflow, database, specific objectives and targets of the AI tool. CONCLUSION The study validates a descriptive and analytical grid for radiological AI solutions consisting of ten items, using breast cancer and bone fracture as an experimental guide. This grid would assist radiologists in selecting relevant and validated AI solutions. Further developments of the grid are needed to include other organs and tasks.
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Affiliation(s)
- Daphné Guenoun
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, 13009, Marseille, France; Aix Marseille Univ, CNRS, ISM, Inst Movement Sci, 13009, Marseille, France.
| | - Marc Zins
- Department of Radiology and Medical Imaging, Saint-Joseph Hospital, 75014, Paris, France
| | - Pierre Champsaur
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, 13009, Marseille, France; Aix Marseille Univ, CNRS, ISM, Inst Movement Sci, 13009, Marseille, France
| | - Isabelle Thomassin-Naggara
- Sorbonne Université, 75005, Paris, France; Department of Diagnostic and Interventional Imaging, Assistance Publique-Hôpitaux de Paris, Hôpital Tenon, 75020 Paris, France
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12
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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13
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Çelik L, Güner DC, Özçağlayan Ö, Çubuk R, Arıbal ME. Diagnostic performance of two versions of an artificial intelligence system in interval breast cancer detection. Acta Radiol 2023; 64:2891-2897. [PMID: 37722761 DOI: 10.1177/02841851231200785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/20/2023]
Abstract
BACKGROUND Various versions of artificial intelligence (AI) have been used as a diagnostic tool aid in the diagnosis of breast cancer. One of the most important problems in breast screening progmrams is interval breast cancer (IBC). PURPOSE To compare the diagnostic performance of Transpara v1.6 and v1.7 in the detection of IBC. MATERIAL AND METHODS Reports of screening mammograms of a total 2,248,665 of women were evaluated retrospectively. Of 2,129,486 mammograms reported as Breast Imaging Reporting and Data System (BIRADS) 1 and 2, the IBC group consisted of 323 cases who were diagnosed as having cancer on mammography and were correlated with pathology in second mammogram taken >30 days after first mammogram. Four hundred and forty-one were defined as the control group because they did not change over 2 years. Cancer risk scores of both groups were determined from 1 to 10 with Tranpara v1.6 and v1.7. Diagnostic performances of both versions were evaluated by the receiver operating characteristic curve. RESULTS Cancer risk scores 1 and 10 in v1.7 increased compared to v1.6 (P < 0.001). In all cases, sensitivity for v1.6 was 56.6%, specificity was 90%, and, for v1.7, sensitivity was 65.9% and specificity was 90%, respectively. In all cases, area under the curve values were 0.812 for v1.6 and 0.856 for v1.7, which was higher in v1.7 (P < 0.001). Diagnostic performance of v1.7 was higher than v1.6 at the 7-12-month period (P < 0.001). CONCLUSION The present study showed that Tranpara v1.7 has a higher specificity, sensitivity and diagnostic performance in IBC determination than v1.6. AI systems can be used in breast screening as a secondary or third reader in screening programs.
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Affiliation(s)
- Levent Çelik
- Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey
| | - Davut Can Güner
- Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey
| | - Ömer Özçağlayan
- Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey
| | - Rahmi Çubuk
- Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey
| | - Mustafa Erkin Arıbal
- Deparment of Radiology, Acıbadem University, School of Medicine, Istanbul, Turkey
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14
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Fujioka T, Kubota K, Hsu JF, Chang RF, Sawada T, Ide Y, Taruno K, Hankyo M, Kurita T, Nakamura S, Tateishi U, Takei H. Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound. J Med Ultrason (2001) 2023; 50:511-520. [PMID: 37400724 PMCID: PMC10556122 DOI: 10.1007/s10396-023-01332-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/03/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. METHODS The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. RESULT The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). CONCLUSION The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-Koshigaya, Koshigaya, Saitama, 343-8555, Japan.
| | - Jen Feng Hsu
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, ROC
| | - Ruey Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, ROC
| | - Terumasa Sawada
- Department of Breast Surgery, NTT Medical Center Tokyo, 5-9-22 Higashi-Gotanda, Shinagawa-ku, Tokyo, 141-8625, Japan
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Yoshimi Ide
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
- Department of Breast Oncology, Kikuna Memorial Hospital, 4-4-27 Kikuna, Kohoku-ku, Yokohama, 222-0011, Japan
| | - Kanae Taruno
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Meishi Hankyo
- Department of Breast Surgical Oncology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan
| | - Tomoko Kurita
- Department of Breast Surgical Oncology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan
| | - Seigo Nakamura
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Hiroyuki Takei
- Department of Breast Surgical Oncology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan
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15
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Lauritzen AD, von Euler-Chelpin MC, Lynge E, Vejborg I, Nielsen M, Karssemeijer N, Lillholm M. Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture. Radiology 2023; 308:e230227. [PMID: 37642571 DOI: 10.1148/radiol.230227] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.
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Affiliation(s)
- Andreas D Lauritzen
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
| | - My C von Euler-Chelpin
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
| | - Elsebeth Lynge
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
| | - Ilse Vejborg
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
| | - Mads Nielsen
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
| | - Nico Karssemeijer
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
| | - Martin Lillholm
- From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.)
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16
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Yoon JH, Strand F, Baltzer PAT, Conant EF, Gilbert FJ, Lehman CD, Morris EA, Mullen LA, Nishikawa RM, Sharma N, Vejborg I, Moy L, Mann RM. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology 2023; 307:e222639. [PMID: 37219445 PMCID: PMC10315526 DOI: 10.1148/radiol.222639] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/23/2023] [Accepted: 04/03/2023] [Indexed: 05/24/2023]
Abstract
Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.
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Affiliation(s)
- Jung Hyun Yoon
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Fredrik Strand
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Pascal A. T. Baltzer
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Emily F. Conant
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Fiona J. Gilbert
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Constance D. Lehman
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Elizabeth A. Morris
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Lisa A. Mullen
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Robert M. Nishikawa
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Nisha Sharma
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
| | - Ilse Vejborg
- From the Department of Radiology, Severance Hospital, Research
Institute of Radiological Science, Yonsei University, College of Medicine, 50
Yonsei-ro, Seodaemun-gu, 03722 Seoul, Korea (J.H.Y.); Department of Oncology and
Pathology, Karolinska Institute, Stockholm, Sweden (F.S.); Department of
Radiology, Unit of Breast Imaging, Karolinska University Hospital, Stockholm,
Sweden (F.S.); Department of Biomedical Imaging and Image-guided Therapy,
Medical University of Vienna, Vienna, Austria (P.A.T.B.); Department of
Radiology, University of Pennsylvania, Philadelphia, Pa (E.F.C.); Department of
Radiology, University of Cambridge, Cambridge, UK (F.J.G.); Department of
Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, Mass
(C.D.L.); Department of Radiology, University of California Davis, Davis, Calif
(E.A.M.); Department of Radiology, Breast Imaging Division, Johns Hopkins
Medicine, Baltimore, Md (L.A.M.); Department of Radiology, University of
Pittsburgh, UPMC Magee-Womens Hospital, Pittsburgh, Pa (R.M.N.); Department of
Radiology, St James Hospital, Leeds, UK (N.S.); Department of Breast
Examinations, Copenhagen University Hospital Herlev-Gentofte, Copenhagen,
Denmark (I.V.); Department of Radiology, Laura and Isaac Perlmutter Cancer
Center, Center for Biomedical Imaging, Center for Advanced Imaging Innovation
and Research, New York University Grossman School of Medicine, New York, NY
(L.M.); Department of Medical Imaging, Radboud University Medical Center,
Nijmegen, the Netherlands (R.M.M.); and Department of Radiology, Netherlands
Cancer Institute, Amsterdam, the Netherlands (R.M.M.)
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Oiwa M, Suda N, Morita T, Takahashi Y, Sato Y, Hayashi T, Kato A, Nishimura R, Ichihara S, Endo T. Validity of computed mean compressed fibroglandular tissue thickness and breast composition for stratification of masking risk in Japanese women. Breast Cancer 2023:10.1007/s12282-023-01444-7. [PMID: 36920730 DOI: 10.1007/s12282-023-01444-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The volumetric measurement system for mammographic breast density is a high-precision objective method for evaluating the percentage of fibroglandular tissue volume (FG%). Nonetheless, FG% does not precisely correlate with subjective visual estimation (SVE) and shows poor evaluation performance regarding masking risk in patients with comparatively thin compressed breast thickness (CBT), commonly found in Japanese women. We considered that the mean compressed fibroglandular tissue thickness (mCGT), which incorporates the CBT element into the evaluation of breast density, may better predict masking risk. METHODS Volumetric measurements and SVEs were performed on mammograms of 108 breast cancer patients from our center. mCGT was calculated as the product of CBT and FG%. SVE was classified using the Breast Imaging-Reporting and Data System classification, 5th edition. Subsequently, the performance of mCGT, SVE, and FG% in predicting masking risk was estimated using the AUC. RESULTS The AUC values of mCGT and SVE were 0.84 (95% confidence interval, 0.71-0.92) and 0.78 (0.66-0.86), respectively (P = 0.16). The AUC of the FG% was 0.65 (0.52-0.77), which was significantly lower than that of mCGT (P < 0.001). The sensitivity and specificity of mCGT in predicting negative detection were 89% and 71%, respectively; of SVE 83% and 61% (versus 72% and 57% with FG%), suggesting that mCGT was superior to FG% in both sensitivity and specificity, and comparable with SVE. CONCLUSIONS Objective mCGT calculated from the volumetric measurement system will highly likely be useful in evaluating breast density and supporting visual assessment for masking risk stratification.
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Affiliation(s)
- Mikinao Oiwa
- Department of Radiology, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan.
| | - Namiko Suda
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Takako Morita
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Yuko Takahashi
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Yasuyuki Sato
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Takako Hayashi
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Aya Kato
- Department of Breast Surgery, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Rieko Nishimura
- Department of Advanced Diagnosis, National Hospital Organization Division of Pathology, Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Shu Ichihara
- Department of Advanced Diagnosis, National Hospital Organization Division of Pathology, Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
| | - Tokiko Endo
- Department of Radiology, National Hospital Organization Nagoya Medical Center, 4-1-1 Sannomaru, Naka-ku, Nagoya, 460-0001, Japan
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18
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Yuba M, Iwasaki K. Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States. PLOS DIGITAL HEALTH 2023; 2:e0000209. [PMID: 36888573 PMCID: PMC9994700 DOI: 10.1371/journal.pdig.0000209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 02/07/2023] [Indexed: 03/09/2023]
Abstract
Computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided simple triage (CAST), which incorporate artificial intelligence (AI) and machine learning (ML), are continually undergoing post-market improvement. Therefore, understanding the evaluation and approval process of improved products is important. This study intended to conduct a comprehensive survey of AI/ML-based CAD products approved by the U.S. Food and Drug Administration (FDA) that had been improved post-market to gain insights into the efficacy and safety required for market approval. A survey of the product code database published by the FDA identified eight products that were improved post-market. The methods used to evaluate the performance of improvements were analysed, and post-market improvements were approved with retrospective data. Reader study testing (RT) or software standalone testing (SA) procedures were conducted retrospectively. Six RT procedures were conducted because of modifications to the intended use. An average of 17.3 readers (minimum 14, maximum 24) participated, and the area under the curve (AUC) was considered the primary endpoint. The addition of study learning data that did not change the intended use and changes in the analysis algorithm were evaluated by SA. The average sensitivity, specificity, and AUC were 93% (minimum 91.1, maximum 97), 89.6% (minimum 85.9, maximum 96), and 0.96 (minimum 0.96, maximum 0.97), respectively. The average interval between applications was 348 days (minimum -18, maximum 975), which showed that the improvements were implemented within approximately one year. This is the first comprehensive study on AI/ML-based CAD products that have been improved post-market to elucidate evaluation points for post-market improvements. The findings will be informative for the industry and academia in developing and improving AI/ML-based CAD.
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Affiliation(s)
- Mitsuru Yuba
- Cooperative Major in Advanced Biomedical Sciences, Joint Graduate School of Tokyo Women’s Medical University and Waseda University, Waseda University, Tokyo, Japan
| | - Kiyotaka Iwasaki
- Cooperative Major in Advanced Biomedical Sciences, Joint Graduate School of Tokyo Women’s Medical University and Waseda University, Waseda University, Tokyo, Japan
- Department of Modern Mechanical Engineering, School of Creative Science and Engineering, Waseda University, Tokyo, Japan
- Department of Integrative Bioscience and Biomedical Engineering, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
- Institute for Medical Regulatory Science, Waseda University, Tokyo, Japan
- * E-mail:
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19
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Loizidou K, Elia R, Pitris C. Computer-aided breast cancer detection and classification in mammography: A comprehensive review. Comput Biol Med 2023; 153:106554. [PMID: 36646021 DOI: 10.1016/j.compbiomed.2023.106554] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/13/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.
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Affiliation(s)
- Kosmia Loizidou
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Rafaella Elia
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Costas Pitris
- KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
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20
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Choi WJ, An JK, Woo JJ, Kwak HY. Comparison of Diagnostic Performance in Mammography Assessment: Radiologist with Reference to Clinical Information Versus Standalone Artificial Intelligence Detection. Diagnostics (Basel) 2022; 13:diagnostics13010117. [PMID: 36611409 PMCID: PMC9818877 DOI: 10.3390/diagnostics13010117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
We compared diagnostic performances between radiologists with reference to clinical information and standalone artificial intelligence (AI) detection of breast cancer on digital mammography. This study included 392 women (average age: 57.3 ± 12.1 years, range: 30−94 years) diagnosed with malignancy between January 2010 and June 2021 who underwent digital mammography prior to biopsy. Two radiologists assessed mammographic findings based on clinical symptoms and prior mammography. All mammographies were analyzed via AI. Breast cancer detection performance was compared between radiologists and AI based on how the lesion location was concordant between each analysis method (radiologists or AI) and pathological results. Kappa coefficient was used to measure the concordance between radiologists or AI analysis and pathology results. Binominal logistic regression analysis was performed to identify factors influencing the concordance between radiologists’ analysis and pathology results. Overall, the concordance was higher in radiologists’ diagnosis than on AI analysis (kappa coefficient: 0.819 vs. 0.698). Impact of prior mammography (odds ratio (OR): 8.55, p < 0.001), clinical symptom (OR: 5.49, p < 0.001), and fatty breast density (OR: 5.18, p = 0.008) were important factors contributing to the concordance of lesion location between radiologists’ diagnosis and pathology results.
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Affiliation(s)
- Won Jae Choi
- Department of Radiology, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Jin Kyung An
- Department of Radiology, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
- Correspondence: ; Tel.: +82-2-970-8290; Fax: +82-2-970-8346
| | - Jeong Joo Woo
- Department of Radiology, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Hee Yong Kwak
- Department of Surgery, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
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21
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Raafat M, Mansour S, Kamal R, Ali HW, Shibel PE, Marey A, Taha SN, Alkalaawy B. Does artificial intelligence aid in the detection of different types of breast cancer? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00868-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Abstract
Background
On mammography many cancers may be missed even in retrospect either due to the breast density, the small size of the tumor or the subtle signs of cancer that are imperceptible. We aimed to compare the sensitivity of artificial intelligence (AI) to that of digital mammography in the detection of different types of breast cancer. Also, the sensitivity of AI in picking up the different breast cancer morphologies namely mass, pathological calcifications, asymmetry, and distortion was assessed. Tissue biopsy and pathology were used as the standard reference. The study included 123 female patients with 134 proved carcinoma. All patients underwent digital mammogram (DM) examination scanned with artificial intelligence algorithm.
Results
AI achieved higher sensitivity than mammography in detecting malignant breast lesions. The sensitivity of AI was 96.6%, and false negative rate was 3.4%, while mammography sensitivity was 87.3% and false negative rate 12.7%. Our study showed AI performed better than mammography in detecting ductal carcinoma in situ and invasive lobular carcinoma with sensitivity (100% and 96.6%) vs (88.9% and 82.2%) respectively. AI was more sensitive to detect cancers presented with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% and asymmetry and distortion 100% vs 84.6%, than mammography.
Conclusions
AI showed potential values to overcome mammographic limitations in the detection of breast cancer even those with challenging morphology as invasive lobular carcinoma, ductal carcinoma in situ, tubular carcinoma and micropapillary carcinoma.
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22
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Malliori A, Pallikarakis N. Breast cancer detection using machine learning in digital mammography and breast tomosynthesis: A systematic review. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00693-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, Lynge E, Vejborg I, Nielsen M, Karssemeijer N, Lillholm M. An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload. Radiology 2022; 304:41-49. [PMID: 35438561 DOI: 10.1148/radiol.210948] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk, and suspicious mammograms in a screening sample to safely reduce radiologist workload and evaluate across Breast Imaging Reporting and Data System (BI-RADS) densities. Materials and Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January 2014 to December 2015 in the Danish Capital Region breast cancer screening program. All mammograms were scored from 0 to 10, representing the risk of malignancy, using an AI tool. During simulation, normal mammograms (score < 5) would be excluded from radiologist reading and suspicious mammograms (score > recall threshold [RT]) would be recalled. Two radiologists read the remaining mammograms. The RT was fitted using another independent cohort (same institution) by matching to the radiologist sensitivity. This protocol was further applied to each BI-RADS density. Screening outcomes were measured using the sensitivity, specificity, workload, and false-positive rate. The AI-based screening was tested for noninferiority sensitivity compared with radiologist screening using the Farrington-Manning test. Specificities were compared using the McNemar test. Results The study sample comprised 114 421 screenings for breast cancer in 114 421 women, resulting in 791 screen-detected, 327 interval, and 1473 long-term cancers and 2107 false-positive screenings. The mean age of the women was 59 years ± 6 (SD). The AI-based screening sensitivity was 69.7% (779 of 1118; 95% CI: 66.9, 72.4) and was noninferior (P = .02) to the radiologist screening sensitivity of 70.8% (791 of 1118; 95% CI: 68.0, 73.5). The AI-based screening specificity was 98.6% (111 725 of 113 303; 95% CI: 98.5, 98.7), which was higher (P < .001) than the radiologist specificity of 98.1% (111 196 of 113 303; 95% CI: 98.1, 98.2). The radiologist workload was reduced by 62.6% (71 585 of 114 421), and 25.1% (529 of 2107) of false-positive screenings were avoided. Screening results were consistent across BI-RADS densities, although not significantly so for sensitivity. Conclusion Artificial intelligence (AI)-based screening could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload. AI-based screening performed consistently across breast densities. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Andreas D Lauritzen
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - Alejandro Rodríguez-Ruiz
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - My Catarina von Euler-Chelpin
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - Elsebeth Lynge
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - Ilse Vejborg
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - Mads Nielsen
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - Nico Karssemeijer
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
| | - Martin Lillholm
- From the Department of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark; ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R., N.K.); Centre for Epidemiological Research, Nykøbing Falster Hospital, Nykøbing, Denmark (E.L.); Department of Radiology, Copenhagen University Hospital Herlev/Gentofte, Copenhagen, Denmark (I.V.); and Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, the Netherlands (N.K.)
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Kim HJ, Kim HH, Kim KH, Choi WJ, Chae EY, Shin HJ, Cha JH, Shim WH. Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics. Insights Imaging 2022; 13:57. [PMID: 35347508 PMCID: PMC8960489 DOI: 10.1186/s13244-022-01183-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software.
Methods By retrospectively reviewing our institutional database (January 2017–September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0–100; > 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers. Results Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as “AI-detected cancers.” As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients (p < 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p < 0.001), and accompany axillary lymph node metastasis (p = 0.003). Conclusions AI conferred an added value in the detection of mammographically occult breast cancers.
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Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis. NPJ Digit Med 2022; 5:19. [PMID: 35169217 PMCID: PMC8847584 DOI: 10.1038/s41746-022-00559-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022] Open
Abstract
Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.
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Tsai KJ, Chou MC, Li HM, Liu ST, Hsu JH, Yeh WC, Hung CM, Yeh CY, Hwang SH. A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography. SENSORS 2022; 22:s22031160. [PMID: 35161903 PMCID: PMC8838754 DOI: 10.3390/s22031160] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0-2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.
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Affiliation(s)
- Kuen-Jang Tsai
- Department of General Surgey, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan; (K.-J.T.); (C.-M.H.)
- College of Medicine, I-Shou University, Yanchao Dist., Kaohsiung 82445, Taiwan
| | - Mei-Chun Chou
- Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan; (M.-C.C.); (H.-M.L.); (S.-T.L.); (J.-H.H.)
| | - Hao-Ming Li
- Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan; (M.-C.C.); (H.-M.L.); (S.-T.L.); (J.-H.H.)
| | - Shin-Tso Liu
- Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan; (M.-C.C.); (H.-M.L.); (S.-T.L.); (J.-H.H.)
| | - Jung-Hsiu Hsu
- Department of Radiology, E-Da Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan; (M.-C.C.); (H.-M.L.); (S.-T.L.); (J.-H.H.)
| | - Wei-Cheng Yeh
- Department of Radiology, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan;
| | - Chao-Ming Hung
- Department of General Surgey, E-Da Cancer Hospital, Yanchao Dist., Kaohsiung 82445, Taiwan; (K.-J.T.); (C.-M.H.)
| | - Cheng-Yu Yeh
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
- Correspondence:
| | - Shaw-Hwa Hwang
- Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
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Anderson AW, Marinovich ML, Houssami N, Lowry KP, Elmore JG, Buist DS, Hofvind S, Lee CI. Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review. J Am Coll Radiol 2022; 19:259-273. [PMID: 35065909 PMCID: PMC8857031 DOI: 10.1016/j.jacr.2021.11.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography. METHODS A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. RESULTS After data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage. CONCLUSIONS To date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns.
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Affiliation(s)
- Anna W. Anderson
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - M. Luke Marinovich
- Curtin School of Population Health, Curtin University, Bentley, Western Australia, Australia
| | - Nehmat Houssami
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - Kathryn P. Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Joann G. Elmore
- David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA
| | - Diana S.M. Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | | | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
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Dahlblom V, Andersson I, Lång K, Tingberg A, Zackrisson S, Dustler M. Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis. Radiol Artif Intell 2021; 3:e200299. [PMID: 34870215 DOI: 10.1148/ryai.2021200299] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 07/12/2021] [Accepted: 08/09/2021] [Indexed: 11/11/2022]
Abstract
Purpose To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. Materials and Methods In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. Results The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). Conclusion Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.
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Affiliation(s)
- Victor Dahlblom
- Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden
| | - Ingvar Andersson
- Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden
| | - Kristina Lång
- Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden
| | - Anders Tingberg
- Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden
| | - Sophia Zackrisson
- Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden
| | - Magnus Dustler
- Diagnostic Radiology (V.D., I.A., K.L., S.Z., M.D.) and Medical Radiation Physics (A.T., M.D.), Department of Translational Medicine, Lund University, Malmö, Sweden; and Department of Medical Imaging and Physiology (V.D., S.Z.), Unilabs Breast Centre (I.A., K.L.), and Department of Radiation Physics (A.T.), Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02 Malmö, Sweden
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Heenaye-Mamode Khan M, Boodoo-Jahangeer N, Dullull W, Nathire S, Gao X, Sinha GR, Nagwanshi KK. Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN). PLoS One 2021; 16:e0256500. [PMID: 34437623 PMCID: PMC8389446 DOI: 10.1371/journal.pone.0256500] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/06/2021] [Indexed: 01/07/2023] Open
Abstract
The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.
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Affiliation(s)
| | | | - Wasiimah Dullull
- Department of Software and Information Systems, University of Mauritius, Reduit, Mauritius
| | - Shaista Nathire
- Department of Software and Information Systems, University of Mauritius, Reduit, Mauritius
| | - Xiaohong Gao
- Department of Computer Science, Middlesex University, London, England, United Kingdom
| | - G. R. Sinha
- Department of Electronics and Communication Engineering, Myanmar Institute of Information Technology (MIIT) Mandalay, Myanmar
| | - Kapil Kumar Nagwanshi
- Department of Computer Science and Engineering, Amity University Rajasthan, Jaipur, India
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Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology 2021; 300:57-65. [PMID: 33944627 DOI: 10.1148/radiol.2021203555] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-negative assessments. Purpose To evaluate if using an artificial intelligence (AI) system could reduce workload without reducing cancer detection in breast cancer screening with digital mammography (DM) or digital breast tomosynthesis (DBT). Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired from January 2015 to December 2016 were retrospectively collected from the Córdoba Tomosynthesis Screening Trial. The original reading settings were single or double reading of DM or DBT images. An AI system computed a cancer risk score for DM and DBT examinations independently. Each original setting was compared with a simulated autonomous AI triaging strategy (the least suspicious examinations for AI are not human-read; the rest are read in the same setting as the original, and examinations not recalled by radiologists but graded as very suspicious by AI are recalled) in terms of workload, sensitivity, and recall rate. The McNemar test with Bonferroni correction was used for statistical analysis. Results A total of 15 987 DM and DBT examinations (which included 98 screening-detected and 15 interval cancers) from 15 986 women (mean age ± standard deviation, 58 years ± 6) were evaluated. In comparison with double reading of DBT images (568 hours needed, 92 of 113 cancers detected, 706 recalls in 15 987 examinations), AI with DBT would result in 72.5% less workload (P < .001, 156 hours needed), noninferior sensitivity (95 of 113 cancers detected, P = .38), and 16.7% lower recall rate (P < .001, 588 recalls in 15 987 examinations). Similar results were obtained for AI with DM. In comparison with the original double reading of DM images (222 hours needed, 76 of 113 cancers detected, 807 recalls in 15 987 examinations), AI with DBT would result in 29.7% less workload (P < .001), 25.0% higher sensitivity (P < .001), and 27.1% lower recall rate (P < .001). Conclusion Digital mammography and digital breast tomosynthesis screening strategies based on artificial intelligence systems could reduce workload up to 70%. Published under a CC BY 4.0 license.
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Affiliation(s)
- José Luis Raya-Povedano
- From the Breast Cancer Unit, Department of Radiology, Hospital Universitario Reina Sofía, Av Menéndez Pidal s/n, Córdoba 14004, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); Maimonides Institute for Biomedical Research of Córdoba, Córdoba, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); and Department of Clinical Science, ScreenPoint Medical, Nijmegen, the Netherlands (A.G.M., A.R.R.)
| | - Sara Romero-Martín
- From the Breast Cancer Unit, Department of Radiology, Hospital Universitario Reina Sofía, Av Menéndez Pidal s/n, Córdoba 14004, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); Maimonides Institute for Biomedical Research of Córdoba, Córdoba, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); and Department of Clinical Science, ScreenPoint Medical, Nijmegen, the Netherlands (A.G.M., A.R.R.)
| | - Esperanza Elías-Cabot
- From the Breast Cancer Unit, Department of Radiology, Hospital Universitario Reina Sofía, Av Menéndez Pidal s/n, Córdoba 14004, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); Maimonides Institute for Biomedical Research of Córdoba, Córdoba, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); and Department of Clinical Science, ScreenPoint Medical, Nijmegen, the Netherlands (A.G.M., A.R.R.)
| | - Albert Gubern-Mérida
- From the Breast Cancer Unit, Department of Radiology, Hospital Universitario Reina Sofía, Av Menéndez Pidal s/n, Córdoba 14004, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); Maimonides Institute for Biomedical Research of Córdoba, Córdoba, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); and Department of Clinical Science, ScreenPoint Medical, Nijmegen, the Netherlands (A.G.M., A.R.R.)
| | - Alejandro Rodríguez-Ruiz
- From the Breast Cancer Unit, Department of Radiology, Hospital Universitario Reina Sofía, Av Menéndez Pidal s/n, Córdoba 14004, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); Maimonides Institute for Biomedical Research of Córdoba, Córdoba, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); and Department of Clinical Science, ScreenPoint Medical, Nijmegen, the Netherlands (A.G.M., A.R.R.)
| | - Marina Álvarez-Benito
- From the Breast Cancer Unit, Department of Radiology, Hospital Universitario Reina Sofía, Av Menéndez Pidal s/n, Córdoba 14004, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); Maimonides Institute for Biomedical Research of Córdoba, Córdoba, Spain (J.L.R.P., S.R.M., E.E.C., M.Á.B.); and Department of Clinical Science, ScreenPoint Medical, Nijmegen, the Netherlands (A.G.M., A.R.R.)
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Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2:12-24. [DOI: 10.35713/aic.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/02/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Affiliation(s)
- Ying Shao
- Department of Laboratory Medicine, People Hospital of Jiangying, Jiangying 214400, Jiangsu Province, China
| | - Yu-Xuan Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Huan-Huan Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shi-Chang Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jie-Xin Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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Al-Sowayan BS, Al-Shareeda AT. Nanogenomics and Artificial Intelligence: A Dynamic Duo for the Fight Against Breast Cancer. Front Mol Biosci 2021; 8:651588. [PMID: 33937332 PMCID: PMC8082244 DOI: 10.3389/fmolb.2021.651588] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 03/16/2021] [Indexed: 12/24/2022] Open
Abstract
Application software is utilized to aid in the diagnosis of breast cancer. Yet, recent advances in artificial intelligence (AI) are addressing challenges related to the detection, classification, and monitoring of different types of tumors. AI can apply deep learning algorithms to perform automated analysis on mammographic or histologic examinations. Large volume of data generated by digitalized mammogram or whole-slide images can be interoperated through advanced machine learning. This enables fast evaluation of every tissue patch on an image, resulting in a quicker more sensitivity, and more reproducible diagnoses compared to human performance. On the other hand, cancer cell-exosomes which are extracellular vesicles released by cancer cells into the blood circulation, are being explored as cancer biomarker. Recent studies on cancer-exosome-content revealed that the encapsulated miRNA and other biomolecules are indicative of tumor sub-type, possible metastasis and prognosis. Thus, theoretically, through nanogenomicas, a profile of each breast tumor sub-type, estrogen receptor status, and potential metastasis site can be constructed. Then, a laboratory instrument, fitted with an AI program, can be used to diagnose suspected patients by matching their sera miRNA and biomolecules composition with the available template profiles. In this paper, we discuss the advantages of establishing a nanogenomics-AI-based breast cancer diagnostic approach, compared to the gold standard radiology or histology based approaches that are currently being adapted to AI. Also, we discuss the advantages of building the diagnostic and prognostic biomolecular profiles for breast cancers based on the exosome encapsulated content, rather than the free circulating miRNA and other biomolecules.
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Affiliation(s)
| | - Alaa T. Al-Shareeda
- Stem Cells and Regenerative Medicine Unit, Cell Therapy & Cancer Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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Janan F, Brady M. RICE: A method for quantitative mammographic image enhancement. Med Image Anal 2021; 71:102043. [PMID: 33813287 DOI: 10.1016/j.media.2021.102043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 03/09/2021] [Accepted: 03/15/2021] [Indexed: 10/21/2022]
Abstract
We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting regions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tissue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the 'neighbourhood' for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise constant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system.
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Affiliation(s)
- Faraz Janan
- School of Computer Science, University of Lincoln, Issac Newton Building, Bradyford Pool LN6 7TS, United Kingdom.
| | - Michael Brady
- Department of Oncological Imaging, University of Oxford, Old Road Campus Research Building, Headington, Oxford OX3 7DQ, United Kingdom.
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35
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de Vries CF, Morrissey BE, Duggan D, Staff RT, Lip G. Screening participants' attitudes to the introduction of artificial intelligence in breast screening. J Med Screen 2021; 28:221-222. [PMID: 33715512 DOI: 10.1177/09691413211001405] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Clarisse F de Vries
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK.,Aberdeen Centre for Health Data Science, University of Aberdeen, Aberdeen, UK
| | - Brian E Morrissey
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK.,Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK
| | - Donna Duggan
- Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK.,North East of Scotland Breast Screening Centre, Aberdeen, UK
| | - Roger T Staff
- Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK
| | - Gerald Lip
- Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK.,North East of Scotland Breast Screening Centre, Aberdeen, UK
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