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For: Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69:127-157. [PMID: 30720861 DOI: 10.3322/caac.21552] [Cited by in Crossref: 132] [Cited by in F6Publishing: 156] [Article Influence: 66.0] [Reference Citation Analysis]
Number Citing Articles
1 Lu L, Sun SH, Yang H, E L, Guo P, Schwartz LH, Zhao B. Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data. Tomography 2020;6:223-30. [PMID: 32548300 DOI: 10.18383/j.tom.2020.00017] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
2 Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers (Basel) 2020;12:E2453. [PMID: 32872466 DOI: 10.3390/cancers12092453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Tanabe S. Cancer recognition of artificial intelligence. AIC 2021;2:1-6. [DOI: 10.35713/aic.v2.i1.1] [Reference Citation Analysis]
4 Ji GW, Zhu FP, Xu Q, Wang K, Wu MY, Tang WW, Li XC, Wang XH. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study. EBioMedicine 2019;50:156-65. [PMID: 31735556 DOI: 10.1016/j.ebiom.2019.10.057] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
5 Wang X, Zou C, Zhang Y, Li X, Wang C, Ke F, Chen J, Wang W, Wang D, Xu X, Xie L, Zhang Y. Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images. Front Genet 2021;12:661109. [PMID: 34354733 DOI: 10.3389/fgene.2021.661109] [Reference Citation Analysis]
6 Wu X, Li J, Mou Y, Yao Y, Cui J, Mao N, Song X. Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions. Front Oncol 2021;11:580886. [PMID: 34164333 DOI: 10.3389/fonc.2021.580886] [Reference Citation Analysis]
7 Dong HC, Dong HK, Yu MH, Lin YH, Chang CC. Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study. Int J Environ Res Public Health 2020;17:E5993. [PMID: 32824765 DOI: 10.3390/ijerph17165993] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
8 Chang X, Guo X, Li X, Han X, Li X, Liu X, Ren J. Potential Value of Radiomics in the Identification of Stage T3 and T4a Esophagogastric Junction Adenocarcinoma Based on Contrast-Enhanced CT Images. Front Oncol 2021;11:627947. [PMID: 33747947 DOI: 10.3389/fonc.2021.627947] [Reference Citation Analysis]
9 Hoeschen C. [Use of artificial intelligence for image reconstruction]. Radiologe 2020;60:15-23. [PMID: 31897503 DOI: 10.1007/s00117-019-00630-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
10 Wang S, Ni Y. Editorial for "Tumor Stiffness Measurements on Magnetic Resonance Elastography for Single Nodular Hepatocellular Carcinomas Can Predict Tumor Recurrence after Hepatic Resection". J Magn Reson Imaging 2021;53:597-8. [PMID: 32964630 DOI: 10.1002/jmri.27371] [Reference Citation Analysis]
11 Wang Y, Wei W, Liu Z, Liang Y, Liu X, Li Y, Tang Z, Jiang T, Tian J. Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study. Front Oncol 2020;10:235. [PMID: 32231995 DOI: 10.3389/fonc.2020.00235] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
12 Ke C, Chen H, Lv X, Li H, Zhang Y, Chen M, Hu D, Ruan G, Zhang Y, Zhang Y, Liu L, Feng Y. Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI. J Magn Reson Imaging 2020;51:1810-20. [PMID: 31710413 DOI: 10.1002/jmri.26976] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
13 Ullah M, Akbar A, Yannarelli G. Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine. AIC 2020;1:39-44. [DOI: 10.35713/aic.v1.i2.39] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
14 Elmore LW, Greer SF, Daniels EC, Saxe CC, Melner MH, Krawiec GM, Cance WG, Phelps WC. Blueprint for cancer research: Critical gaps and opportunities. CA Cancer J Clin 2021;71:107-39. [PMID: 33326126 DOI: 10.3322/caac.21652] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 O'Shea RJ, Sharkey AR, Cook GJR, Goh V. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol 2021. [PMID: 33860829 DOI: 10.1007/s00330-021-07881-2] [Reference Citation Analysis]
16 Feng C, Xiang T, Yi Z, Meng X, Chu X, Huang G, Zhao X, Chen F, Xiong B, Feng J. A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma. Front Oncol 2021;11:653863. [PMID: 34336652 DOI: 10.3389/fonc.2021.653863] [Reference Citation Analysis]
17 Wang M, Fu F, Zheng B, Bai Y, Wu Q, Wu J, Sun L, Liu Q, Liu M, Yang Y, Shen H, Kong D, Ma X, You P, Li X, Tian F. Development of an AI system for accurately diagnose hepatocellular carcinoma from computed tomography imaging data. Br J Cancer 2021. [PMID: 34365472 DOI: 10.1038/s41416-021-01511-w] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Yoon JH, Kim EK. Deep Learning-Based Artificial Intelligence for Mammography. Korean J Radiol 2021;22:1225-39. [PMID: 33987993 DOI: 10.3348/kjr.2020.1210] [Reference Citation Analysis]
19 Slevin F, Beasley M, Cross W, Scarsbrook A, Murray L, Henry A. Patterns of Lymph Node Failure in Patients With Recurrent Prostate Cancer Postradical Prostatectomy and Implications for Salvage Therapies. Adv Radiat Oncol 2020;5:1126-40. [PMID: 33305073 DOI: 10.1016/j.adro.2020.07.009] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
20 Guzzi EA, Tibbitt MW. Additive Manufacturing of Precision Biomaterials. Adv Mater 2020;32:e1901994. [PMID: 31423679 DOI: 10.1002/adma.201901994] [Cited by in Crossref: 41] [Cited by in F6Publishing: 23] [Article Influence: 20.5] [Reference Citation Analysis]
21 Chen B, Zhong L, Dong D, Zheng J, Fang M, Yu C, Dai Q, Zhang L, Tian J, Lu W, Jin Y. Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma. Front Oncol 2019;9:829. [PMID: 31555589 DOI: 10.3389/fonc.2019.00829] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
22 Wang X, Wan Q, Chen H, Li Y, Li X. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods. Eur Radiol 2020;30:4595-605. [PMID: 32222795 DOI: 10.1007/s00330-020-06768-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
23 Mahmoudi S, Martin SS, Ackermann J, Zhdanovich Y, Koch I, Vogl TJ, Albrecht MH, Lenga L, Bernatz S. Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans. BMC Med Imaging 2021;21:123. [PMID: 34384385 DOI: 10.1186/s12880-021-00654-9] [Reference Citation Analysis]
24 Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021;11:742. [PMID: 33919342 DOI: 10.3390/diagnostics11050742] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Yan J, Zhang S, Li KK, Wang W, Li K, Duan W, Yuan B, Wang L, Liu L, Zhan Y, Pei D, Zhao H, Sun T, Sun C, Wang W, Liu Z, Hong X, Wang X, Guo Y, Li W, Cheng J, Liu X, Ng HK, Li Z, Zhang Z. Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma. EBioMedicine 2020;61:103093. [PMID: 33096488 DOI: 10.1016/j.ebiom.2020.103093] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
26 Xu W, Ding Z, Shan Y, Chen W, Feng Z, Pang P, Shen Q. A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion. Front Neurosci 2020;14:491. [PMID: 32581674 DOI: 10.3389/fnins.2020.00491] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
27 Meng Y, Sun J, Qu N, Zhang G, Yu T, Piao H. Application of Radiomics for Personalized Treatment of Cancer Patients. Cancer Manag Res 2019;11:10851-8. [PMID: 31920394 DOI: 10.2147/CMAR.S232473] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
28 Alami H, Lehoux P, Auclair Y, de Guise M, Gagnon MP, Shaw J, Roy D, Fleet R, Ag Ahmed MA, Fortin JP. Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity. J Med Internet Res 2020;22:e17707. [PMID: 32406850 DOI: 10.2196/17707] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
29 Trebeschi S, Bodalal Z, Boellaard TN, Tareco Bucho TM, Drago SG, Kurilova I, Calin-Vainak AM, Delli Pizzi A, Muller M, Hummelink K, Hartemink KJ, Nguyen-Kim TDL, Smit EF, Aerts HJWL, Beets-Tan RGH. Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy. Front Oncol 2021;11:609054. [PMID: 33738253 DOI: 10.3389/fonc.2021.609054] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Fu H, Li F, Sun X, Cao X, Liao J, Orlando JI, Tao X, Li Y, Zhang S, Tan M, Yuan C, Bian C, Xie R, Li J, Li X, Wang J, Geng L, Li P, Hao H, Liu J, Kong Y, Ren Y, Bogunović H, Zhang X, Xu Y. AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography. Medical Image Analysis 2020;66:101798. [DOI: 10.1016/j.media.2020.101798] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
31 Ge L, Chen Y, Yan C, Zhao P, Zhang P, A R, Liu J. Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management. Front Oncol 2019;9:1296. [PMID: 31850202 DOI: 10.3389/fonc.2019.01296] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
32 Niu XK, He XF. Development of a computed tomography-based radiomics nomogram for prediction of transarterial chemoembolization refractoriness in hepatocellular carcinoma. World J Gastroenterol 2021;27:189-207. [PMID: 33510559 DOI: 10.3748/wjg.v27.i2.189] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Qin Y, Deng Y, Jiang H, Hu N, Song B. Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction. Front Oncol 2021;11:631686. [PMID: 34367946 DOI: 10.3389/fonc.2021.631686] [Reference Citation Analysis]
34 Wentzensen N, Lahrmann B, Clarke MA, Kinney W, Tokugawa D, Poitras N, Locke A, Bartels L, Krauthoff A, Walker J, Zuna R, Grewal KK, Goldhoff PE, Kingery JD, Castle PE, Schiffman M, Lorey TS, Grabe N. Accuracy and Efficiency of Deep-Learning-Based Automation of Dual Stain Cytology in Cervical Cancer Screening. J Natl Cancer Inst 2021;113:72-9. [PMID: 32584382 DOI: 10.1093/jnci/djaa066] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 12.0] [Reference Citation Analysis]
35 Stollmayer R, Budai BK, Tóth A, Kalina I, Hartmann E, Szoldán P, Bérczi V, Maurovich-horvat P, Kaposi PN. Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging. WJG 2021;27:5978-88. [DOI: 10.3748/wjg.v27.i35.5978] [Reference Citation Analysis]
36 Meng XP, Wang YC, Zhou JY, Yu Q, Lu CQ, Xia C, Tang TY, Xu J, Sun K, Xiao W, Ju S. Comparison of MRI and CT for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma Based on a Non-Radiomics and Radiomics Method: Which Imaging Modality Is Better? J Magn Reson Imaging 2021;54:526-36. [PMID: 33622022 DOI: 10.1002/jmri.27575] [Reference Citation Analysis]
37 Dominietto M, Pica A, Safai S, Lomax AJ, Weber DC, Capobianco E. Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy. Front Med (Lausanne) 2019;6:333. [PMID: 32010703 DOI: 10.3389/fmed.2019.00333] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
38 Liu X, Yang Q, Zhang C, Sun J, He K, Xie Y, Zhang Y, Fu Y, Zhang H. Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer. Front Oncol 2020;10:585767. [PMID: 33680919 DOI: 10.3389/fonc.2020.585767] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
39 Lacroix M, Frouin F, Dirand AS, Nioche C, Orlhac F, Bernaudin JF, Brillet PY, Buvat I. Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer. Front Oncol 2020;10:43. [PMID: 32083003 DOI: 10.3389/fonc.2020.00043] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
40 Mallio CA, Quattrocchi CC, Beomonte Zobel B, Parizel PM. Artificial intelligence, chest radiographs, and radiology trainees: a powerful combination to enhance the future of radiologists? Quant Imaging Med Surg 2021;11:2204-7. [PMID: 33937001 DOI: 10.21037/qims-20-1306] [Reference Citation Analysis]
41 Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. J Breast Imaging 2020;2:304-14. [PMID: 32803154 DOI: 10.1093/jbi/wbaa033] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
42 Tang D, Zhou J, Wang L, Ni M, Chen M, Hassan S, Luo R, Chen X, He X, Zhang L, Ding X, Yu H, Xu G, Zou X. A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video). Front Oncol 2021;11:622827. [PMID: 33959495 DOI: 10.3389/fonc.2021.622827] [Reference Citation Analysis]
43 Wang Z, Li N, Zheng F, Sui X, Han W, Xue F, Xu X, Yang C, Hu Y, Wang L, Song W, Jiang J. Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study. J Transl Med 2021;19:191. [PMID: 33947428 DOI: 10.1186/s12967-021-02849-8] [Reference Citation Analysis]
44 Merisaari H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Boström PJ, Madabhushi A, Aronen HJ, Jambor I. Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magn Reson Med 2020;83:2293-309. [PMID: 31703155 DOI: 10.1002/mrm.28058] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
45 Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front Pharmacol 2020;11:1177. [PMID: 32903628 DOI: 10.3389/fphar.2020.01177] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
46 Guo X, Liu Z, Sun C, Zhang L, Wang Y, Li Z, Shi J, Wu T, Cui H, Zhang J, Tian J, Tian J. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer. EBioMedicine 2020;60:103018. [PMID: 32980697 DOI: 10.1016/j.ebiom.2020.103018] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
47 Prior F, Almeida J, Kathiravelu P, Kurc T, Smith K, Fitzgerald TJ, Saltz J. Open access image repositories: high-quality data to enable machine learning research. Clin Radiol 2020;75:7-12. [PMID: 31040006 DOI: 10.1016/j.crad.2019.04.002] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 7.5] [Reference Citation Analysis]
48 Jiang H, Liu X, Chen J, Wei Y, Lee JM, Cao L, Wu Y, Duan T, Li X, Ma L, Song B. Man or machine?Cancer Imaging. 2019;19:84. [PMID: 31806050 DOI: 10.1186/s40644-019-0266-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
49 Su R, Liu J, Zhang D, Cheng C, Ye M. Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block. Front Neurosci 2020;14:586197. [PMID: 33192272 DOI: 10.3389/fnins.2020.586197] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Cho BJ, Choi YJ, Lee MJ, Kim JH, Son GH, Park SH, Kim HB, Joo YJ, Cho HY, Kyung MS, Park YH, Kang BS, Hur SY, Lee S, Park ST. Classification of cervical neoplasms on colposcopic photography using deep learning. Sci Rep 2020;10:13652. [PMID: 32788635 DOI: 10.1038/s41598-020-70490-4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
51 Rubin DL. Artificial Intelligence in Imaging: The Radiologist's Role. J Am Coll Radiol 2019;16:1309-17. [PMID: 31492409 DOI: 10.1016/j.jacr.2019.05.036] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 20.0] [Reference Citation Analysis]
52 Dong J, Geng Y, Lu D, Li B, Tian L, Lin D, Zhang Y. Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov. Front Oncol 2020;10:1629. [PMID: 33042806 DOI: 10.3389/fonc.2020.01629] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
53 Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. AIG 2020;1:71-85. [DOI: 10.35712/aig.v1.i4.71] [Reference Citation Analysis]
54 Wu X, Li J, Gassa A, Buchner D, Alakus H, Dong Q, Ren N, Liu M, Odenthal M, Stippel D, Bruns C, Zhao Y, Wahba R. Circulating tumor DNA as an emerging liquid biopsy biomarker for early diagnosis and therapeutic monitoring in hepatocellular carcinoma. Int J Biol Sci 2020;16:1551-62. [PMID: 32226301 DOI: 10.7150/ijbs.44024] [Cited by in Crossref: 20] [Cited by in F6Publishing: 18] [Article Influence: 20.0] [Reference Citation Analysis]
55 Hwang EJ, Goo JM, Kim HY, Yi J, Yoon SH, Kim Y. Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system. Eur Radiol 2021;31:475-85. [PMID: 32797309 DOI: 10.1007/s00330-020-07151-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
56 Goyal S. An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging. RMI 2021;Volume 14:15-25. [DOI: 10.2147/rmi.s295205] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
57 Vogrin M, Trojner T, Kelc R. Artificial intelligence in musculoskeletal oncological radiology. Radiol Oncol 2020;55:1-6. [PMID: 33885240 DOI: 10.2478/raon-2020-0068] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
58 Sartoris R, Gregory J, Dioguardi Burgio M, Ronot M, Vilgrain V. HCC advances in diagnosis and prognosis: Digital and Imaging. Liver Int 2021;41 Suppl 1:73-7. [PMID: 34155790 DOI: 10.1111/liv.14865] [Reference Citation Analysis]
59 Hang J, Xu K, Yin R, Shao Y, Liu M, Shi H, Wang X, Wu L. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases. J Cancer 2021;12:2351-8. [PMID: 33758611 DOI: 10.7150/jca.49569] [Reference Citation Analysis]
60 Han S, Shuen WH, Wang WW, Nazim E, Toh HC. Tailoring precision immunotherapy: coming to a clinic soon? ESMO Open 2020;5 Suppl 1:e000631. [PMID: 33558033 DOI: 10.1136/esmoopen-2019-000631] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
61 Zheng W, Yan L, Gou C, Zhang ZC, Jason Zhang J, Hu M, Wang FY. Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis. Inf Fusion 2021;75:168-85. [PMID: 34093095 DOI: 10.1016/j.inffus.2021.05.015] [Reference Citation Analysis]
62 Kelsey EA, Njeru JW, Chaudhry R, Fischer KM, Schroeder DR, Croghan IT. Understanding User Acceptance of Clinical Decision Support Systems to Promote Increased Cancer Screening Rates in a Primary Care Practice. J Prim Care Community Health 2020;11:2150132720958832. [PMID: 33016170 DOI: 10.1177/2150132720958832] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
63 Yee NS. Machine intelligence for precision oncology. WJTM 2021;9:1-10. [DOI: 10.5528/wjtm.v9.i1.1] [Reference Citation Analysis]
64 Formica V, Morelli C, Riondino S, Renzi N, Nitti D, Roselli M. Artificial intelligence for the study of colorectal cancer tissue slides. AIG 2020;1:51-9. [DOI: 10.35712/aig.v1.i3.51] [Reference Citation Analysis]
65 Pot M, Kieusseyan N, Prainsack B. Not all biases are bad: equitable and inequitable biases in machine learning and radiology. Insights Imaging 2021;12:13. [PMID: 33564955 DOI: 10.1186/s13244-020-00955-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
66 Kuang M, Hu HT, Li W, Chen SL, Lu XZ. Articles That Use Artificial Intelligence for Ultrasound: A Reader's Guide. Front Oncol 2021;11:631813. [PMID: 34178622 DOI: 10.3389/fonc.2021.631813] [Reference Citation Analysis]
67 Wang Y, Lu X, Zhang Y, Zhang X, Wang K, Liu J, Li X, Hu R, Meng X, Dou S, Hao H, Zhao X, Hu W, Li C, Gao Y, Wang Z, Lu G, Yan F, Zhang B. Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care. EBioMedicine 2020;54:102724. [PMID: 32251997 DOI: 10.1016/j.ebiom.2020.102724] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
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