Published online Apr 28, 2021. doi: 10.35713/aic.v2.i2.7
Peer-review started: March 24, 2021
First decision: March 26, 2021
Revised: April 1, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: April 28, 2021
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Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.
Core Tip: In this editorial, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.
- Citation: Alloro R, Sinagra E. Artificial intelligence and colorectal cancer: How far can you go? Artif Intell Cancer 2021; 2(2): 7-11
- URL: https://www.wjgnet.com/2644-3228/full/v2/i2/7.htm
- DOI: https://dx.doi.org/10.35713/aic.v2.i2.7
Colorectal cancer (CRC) is a major healthcare concern worldwide. It is the third most common cancer in males, the second most common cancer in females and the fourth leading cause of cancer death worldwide[1-3]. Furthermore, up to 60%-70% of recog
Artificial intelligence (AI) is a form of machine technology in which intelligent agents perform functions associated with the human mind, such as learning and problem solving[7-9]; AI algorithms are primarily used for disease diagnosis, treatment and prognosis[10,11].
In the setting of endoscopic diagnosis, AI has been primarily evaluated in 3 clinical scenarios: Polyp detection, polyp characterization (adenomatous vs nonadenomatous), and the prediction of invasive cancer within a polypoid lesion[12].
With regard to polyp detection, the adenoma detection rate (ADR), defined as the proportion of patients with at least one colorectal adenoma detected at the first scree
The outcomes reported by different mono- and multicenter randomized clinical trials are highly promising; the overall ADR of these studies was significantly higher when computer-aided diagnosis (CAD) systems were incorporated (up to 80%)[16-20].
With regard to polyp characterization, CAD systems can achieve thresholds of preservation and incorporate valuable endoscopic innovations for diminutive, nonneoplastic rectosigmoid polyps according to various studies[6,21-25].
With regard to differentiation between invasive cancer and nonmalignant adenoma
AI has also been evaluated in the classification and diagnosis of biopsy samples. In a recent systematic review performed by Thakur and coworkers, the authors concluded that artificial intelligence showed promising results in terms of accuracy in diagnosing CRC with regard to tumor classification, tumor microenvironment analysis, and prognosis prediction. However, the scale and quality of the training and validation datasets of most of these studies are insufficiently adequate, limiting the applicability of this technique in clinical practice[28].
With regard to surgical approaches, robot-assisted colorectal surgery has shown better performance than human-alone surgery, in terms of short- and long-term outcomes[10,29].
Additionally, with regard to the pharmacological approach, some studies evaluated targeted drug delivery[30], drug pharmacokinetics[31] and prediction of the rate of drug toxicity[32].
Furthermore, the personalization and precision of cancer treatments have become major themes in oncology research. For example, “Watson for Oncology” is an AI system that can assist in the precision medicine-based treatment of tumors[10,33]. It can automatically extract medical language from doctors’ records and translate them into a practical language for learning[10]. This model can be used to identify new cancer sub
Finally, the emergence of AI has allowed clinicians to predict the prognoses of CRC patients more easily and precisely by using several approaches. For example, in one study, genetic markers of CRC were used to train a model based on different algori
In the near future, AI technology will help doctors diagnose and treat their patients and provide CRC patients with personalized and accurate prognosis evaluations.
In conclusion. AI could play a pivotal role in gastrointestinal oncology, especially in the setting of CRC, for tailoring patient treatments and predicting their clinical outcomes[9].
Future randomized studies could directly increase the overall value (quality and costs) of AI by examining its effects not only in diagnosis (by evaluating colonoscopy findings, endoscopy durations, polyps and ADRs) but also in prognosis and therapy.
Since AI science continues to grow and evolve, the current limitations must be considered as a future challenge; these limitations are also inherited by the medicine applications of AI, including the difficult predictability of situations characterized by some degree of uncertainty[6]. Table 1 shows the applications of AI in CRC.
Setting | Application | Ref. |
Diagnosis | Polyp identification | [16-20] |
Polyp characterization | [21-25] | |
Prediction of invasive cancer within a polypoid lesion | [26,27] | |
Search for new diagnostic biomarkers | [10] | |
Pathologic biopsy | [28] | |
Treatment | Preoperative evaluation | [10] |
Robot-assisted surgery | [29] | |
Drug delivering in a targeted manner | [30] | |
Evaluation of drugs pharmacokinetic | [31] | |
Prediction of the rate of toxicity | [32] | |
Watson for Oncology project | [33] | |
Prognosis | Search for new prognostic biomarkers | [38] |
Evaluation of tumour-stroma ratio | [35] | |
Prediction of lymph-node metastasis | [36,37] |
Future applications of AI could be implemented in all the settings of CRC management, such as the determination of the potential role of noncoding RNAs in tumor diagnosis and treatment[10].
Finally, the integration of AI in human-based medicine has to considered. AI has never been nor will ever be considered a substitute for the physician; on the contrary, it seems to be an extremely helpful tool to be used by the physician who, given his or her ability and skills, is the only one able to process and interpret all the information extracted by the AI to make decisions on patient management.
Manuscript source: Invited manuscript
Specialty type: Gastroenterology and hepatology
Country/Territory of origin: Italy
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P-Reviewer: Yau TO S-Editor: Wang JL L-Editor: A P-Editor: Yuan YY
1. | Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre MF, Allen C, Hansen G, Woodbrook R, Wolfe C, Hamadeh RR, Moore A, Werdecker A, Gessner BD, Te Ao B, McMahon B, Karimkhani C, Yu C, Cooke GS, Schwebel DC, Carpenter DO, Pereira DM, Nash D, Kazi DS, De Leo D, Plass D, Ukwaja KN, Thurston GD, Yun Jin K, Simard EP, Mills E, Park EK, Catalá-López F, deVeber G, Gotay C, Khan G, Hosgood HD 3rd, Santos IS, Leasher JL, Singh J, Leigh J, Jonas JB, Sanabria J, Beardsley J, Jacobsen KH, Takahashi K, Franklin RC, Ronfani L, Montico M, Naldi L, Tonelli M, Geleijnse J, Petzold M, Shrime MG, Younis M, Yonemoto N, Breitborde N, Yip P, Pourmalek F, Lotufo PA, Esteghamati A, Hankey GJ, Ali R, Lunevicius R, Malekzadeh R, Dellavalle R, Weintraub R, Lucas R, Hay R, Rojas-Rueda D, Westerman R, Sepanlou SG, Nolte S, Patten S, Weichenthal S, Abera SF, Fereshtehnejad SM, Shiue I, Driscoll T, Vasankari T, Alsharif U, Rahimi-Movaghar V, Vlassov VV, Marcenes WS, Mekonnen W, Melaku YA, Yano Y, Artaman A, Campos I, MacLachlan J, Mueller U, Kim D, Trillini M, Eshrati B, Williams HC, Shibuya K, Dandona R, Murthy K, Cowie B, Amare AT, Antonio CA, Castañeda-Orjuela C, van Gool CH, Violante F, Oh IH, Deribe K, Soreide K, Knibbs L, Kereselidze M, Green M, Cardenas R, Roy N, Tillmann T, Li Y, Krueger H, Monasta L, Dey S, Sheikhbahaei S, Hafezi-Nejad N, Kumar GA, Sreeramareddy CT, Dandona L, Wang H, Vollset SE, Mokdad A, Salomon JA, Lozano R, Vos T, Forouzanfar M, Lopez A, Murray C, Naghavi M. The Global Burden of Cancer 2013. JAMA Oncol. 2015;1:505-527. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1945] [Cited by in F6Publishing: 1998] [Article Influence: 222.0] [Reference Citation Analysis (0)] |
2. | Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87-108. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 18694] [Cited by in F6Publishing: 21065] [Article Influence: 2340.6] [Reference Citation Analysis (2)] |
3. | Maida M, Morreale G, Sinagra E, Ianiro G, Margherita V, Cirrone Cipolla A, Camilleri S. Quality measures improving endoscopic screening of colorectal cancer: a review of the literature. Expert Rev Anticancer Ther. 2019;19:223-235. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 10] [Cited by in F6Publishing: 16] [Article Influence: 3.2] [Reference Citation Analysis (1)] |
4. | Mandel JS, Bond JH, Church TR, Snover DC, Bradley GM, Schuman LM, Ederer F. Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study. N Engl J Med. 1993;328:1365-1371. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 2183] [Cited by in F6Publishing: 2128] [Article Influence: 68.6] [Reference Citation Analysis (1)] |
5. | Maida M, Macaluso FS, Ianiro G, Mangiola F, Sinagra E, Hold G, Maida C, Cammarota G, Gasbarrini A, Scarpulla G. Screening of colorectal cancer: present and future. Expert Rev Anticancer Ther. 2017;17:1131-1146. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 80] [Cited by in F6Publishing: 98] [Article Influence: 14.0] [Reference Citation Analysis (0)] |
6. | Sinagra E, Badalamenti M, Maida M, Spadaccini M, Maselli R, Rossi F, Conoscenti G, Raimondo D, Pallio S, Repici A, Anderloni A. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped. World J Gastroenterol. 2020;26:5911-5918. [PubMed] [DOI] [Cited in This Article: ] [Cited by in CrossRef: 23] [Cited by in F6Publishing: 19] [Article Influence: 4.8] [Reference Citation Analysis (0)] |
7. | Russell S, Norvig P. Artificial Intelligence: A Modern Approach, Global Edition. 3rd editon. London: Pearson, 2016. [Cited in This Article: ] |
8. | Colom R, Karama S, Jung RE, Haier RJ. Human intelligence and brain networks. Dialogues Clin Neurosci. 2010;12:489-501. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 154] [Cited by in F6Publishing: 145] [Article Influence: 11.2] [Reference Citation Analysis (0)] |
9. | Morreale GC, Sinagra E, Vitello A, Shahini E, Maida M. Emerging artificial intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc. 2020;1:6-18. [DOI] [Cited in This Article: ] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis (1)] |
10. | Wang Y, He X, Nie H, Zhou J, Cao P, Ou C. Application of artificial intelligence to the diagnosis and therapy of colorectal cancer. Am J Cancer Res. 2020;10:3575-3598. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 28] [Cited by in F6Publishing: 50] [Article Influence: 12.5] [Reference Citation Analysis (1)] |
11. | Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 728] [Cited by in F6Publishing: 709] [Article Influence: 101.3] [Reference Citation Analysis (0)] |
12. | Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VideoGIE. 2020;5:598-613. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 50] [Cited by in F6Publishing: 38] [Article Influence: 9.5] [Reference Citation Analysis (0)] |
13. | Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, Guo L, Meng Q, Yang F, Qian W, Xu Z, Wang Y, Wang Z, Gu L, Wang R, Jia F, Yao J, Li Z, Bai Y. Magnitude, Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy: A Systematic Review and Meta-analysis. Gastroenterology 2019; 156: 1661-1674. e11. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 205] [Cited by in F6Publishing: 301] [Article Influence: 60.2] [Reference Citation Analysis (0)] |
14. | Rex DK, Cutler CS, Lemmel GT, Rahmani EY, Clark DW, Helper DJ, Lehman GA, Mark DG. Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies. Gastroenterology. 1997;112:24-28. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1089] [Cited by in F6Publishing: 1020] [Article Influence: 37.8] [Reference Citation Analysis (0)] |
15. | Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370:1298-1306. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1251] [Cited by in F6Publishing: 1437] [Article Influence: 143.7] [Reference Citation Analysis (0)] |
16. | Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, Ferrara E, Spadaccini M, Alkandari A, Fugazza A, Anderloni A, Galtieri PA, Pellegatta G, Carrara S, Di Leo M, Craviotto V, Lamonaca L, Lorenzetti R, Andrealli A, Antonelli G, Wallace M, Sharma P, Rosch T, Hassan C. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology 2020; 159: 512-520. e7. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 237] [Cited by in F6Publishing: 334] [Article Influence: 83.5] [Reference Citation Analysis (0)] |
17. | Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68:1813-1819. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 398] [Cited by in F6Publishing: 490] [Article Influence: 98.0] [Reference Citation Analysis (0)] |
18. | Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5:343-351. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 164] [Cited by in F6Publishing: 264] [Article Influence: 66.0] [Reference Citation Analysis (0)] |
19. | Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020;5:352-361. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 139] [Cited by in F6Publishing: 226] [Article Influence: 56.5] [Reference Citation Analysis (0)] |
20. | Liu WN, Zhang YY, Bian XQ, Wang LJ, Yang Q, Zhang XD, Huang J. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol. 2020;26:13-19. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 82] [Cited by in F6Publishing: 109] [Article Influence: 21.8] [Reference Citation Analysis (0)] |
21. | Aihara H, Saito S, Inomata H, Ide D, Tamai N, Ohya TR, Kato T, Amitani S, Tajiri H. Computer-aided diagnosis of neoplastic colorectal lesions using 'real-time' numerical color analysis during autofluorescence endoscopy. Eur J Gastroenterol Hepatol. 2013;25:488-494. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 42] [Cited by in F6Publishing: 46] [Article Influence: 4.2] [Reference Citation Analysis (0)] |
22. | Kuiper T, Alderlieste YA, Tytgat KM, Vlug MS, Nabuurs JA, Bastiaansen BA, Löwenberg M, Fockens P, Dekker E. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy. 2015;47:56-62. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 13] [Cited by in F6Publishing: 21] [Article Influence: 2.3] [Reference Citation Analysis (0)] |
23. | Rath T, Tontini GE, Vieth M, Nägel A, Neurath MF, Neumann H. In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy. Endoscopy. 2016;48:557-562. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 46] [Cited by in F6Publishing: 49] [Article Influence: 6.1] [Reference Citation Analysis (0)] |
24. | Kominami Y, Yoshida S, Tanaka S, Sanomura Y, Hirakawa T, Raytchev B, Tamaki T, Koide T, Kaneda K, Chayama K. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc. 2016;83:643-649. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 157] [Cited by in F6Publishing: 154] [Article Influence: 19.3] [Reference Citation Analysis (0)] |
25. | Mori Y, Kudo SE, Misawa M, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Urushibara F, Kataoka S, Ogawa Y, Maeda Y, Takeda K, Nakamura H, Ichimasa K, Kudo T, Hayashi T, Wakamura K, Ishida F, Inoue H, Itoh H, Oda M, Mori K. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Ann Intern Med. 2018;169:357-366. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 299] [Cited by in F6Publishing: 308] [Article Influence: 51.3] [Reference Citation Analysis (1)] |
26. | Takeda K, Kudo SE, Mori Y, Misawa M, Kudo T, Wakamura K, Katagiri A, Baba T, Hidaka E, Ishida F, Inoue H, Oda M, Mori K. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy. 2017;49:798-802. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 80] [Cited by in F6Publishing: 92] [Article Influence: 13.1] [Reference Citation Analysis (0)] |
27. | Ito N, Kawahira H, Nakashima H, Uesato M, Miyauchi H, Matsubara H. Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning. Oncology. 2019;96:44-50. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 40] [Cited by in F6Publishing: 47] [Article Influence: 7.8] [Reference Citation Analysis (0)] |
28. | Thakur N, Yoon H, Chong Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers (Basel). 2020;12. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 52] [Cited by in F6Publishing: 43] [Article Influence: 10.8] [Reference Citation Analysis (0)] |
29. | Yoon SN, Kim KY, Kim JW, Lee SC, Kwon YJ, Cho JW, Jung SY, Kim BC. Comparison of short- and long-term outcomes of an early experience with robotic and laparoscopic-assisted resection for rectal cancer. Hepatogastroenterology. 2015;62:34-39. [PubMed] [Cited in This Article: ] |
30. | Felfoul O, Mohammadi M, Taherkhani S, de Lanauze D, Zhong Xu Y, Loghin D, Essa S, Jancik S, Houle D, Lafleur M, Gaboury L, Tabrizian M, Kaou N, Atkin M, Vuong T, Batist G, Beauchemin N, Radzioch D, Martel S. Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions. Nat Nanotechnol. 2016;11:941-947. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 688] [Cited by in F6Publishing: 571] [Article Influence: 71.4] [Reference Citation Analysis (0)] |
31. | Cruz S, Gomes SE, Borralho PM, Rodrigues CMP, Gaudêncio SP, Pereira F. In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs. Biomolecules. 2018;8. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 18] [Cited by in F6Publishing: 26] [Article Influence: 4.3] [Reference Citation Analysis (0)] |
32. | Oyaga-Iriarte E, Insausti A, Sayar O, Aldaz A. Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters. J Pharmacol Sci. 2019;140:20-25. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 19] [Cited by in F6Publishing: 31] [Article Influence: 6.2] [Reference Citation Analysis (0)] |
33. | Schmidt C. M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in Oncology. J Natl Cancer Inst. 2017;109. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 29] [Cited by in F6Publishing: 33] [Article Influence: 4.7] [Reference Citation Analysis (0)] |
34. | Gründner J, Prokosch HU, Stürzl M, Croner R, Christoph J, Toddenroth D. Predicting Clinical Outcomes in Colorectal Cancer Using Machine Learning. Stud Health Technol Inform. 2018;247:101-105. [PubMed] [Cited in This Article: ] |
35. | Mezheyeuski A, Hrynchyk I, Karlberg M, Portyanko A, Egevad L, Ragnhammar P, Edler D, Glimelius B, Östman A. Image analysis-derived metrics of histomorphological complexity predicts prognosis and treatment response in stage II-III colon cancer. Sci Rep. 2016;6:36149. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 2.4] [Reference Citation Analysis (0)] |
36. | Zhou YP, Li S, Zhang XX, Zhang ZD, Gao YX, Ding L, Lu Y. [High definition MRI rectal lymph node aided diagnostic system based on deep neural network]. Zhonghua Wai Ke Za Zhi. 2019;57:108-113. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 6] [Reference Citation Analysis (0)] |
37. | Lu Y, Yu Q, Gao Y, Zhou Y, Liu G, Dong Q, Ma J, Ding L, Yao H, Zhang Z, Xiao G, An Q, Wang G, Xi J, Yuan W, Lian Y, Zhang D, Zhao C, Yao Q, Liu W, Zhou X, Liu S, Wu Q, Xu W, Zhang J, Wang D, Sun Z, Zhang X, Hu J, Zhang M, Zheng X, Wang L, Zhao J, Yang S. Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks. Cancer Res. 2018;78:5135-5143. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 24] [Cited by in F6Publishing: 50] [Article Influence: 8.3] [Reference Citation Analysis (0)] |
38. | Eyraud D, Granger B, Bardier A, Loncar Y, Gottrand G, Le Naour G, Siksik JM, Vaillant JC, Klatzmann D, Puybasset L, Charlotte F, Augustin J. Immunological environment in colorectal cancer: a computer-aided morphometric study of whole slide digital images derived from tissue microarray. Pathology. 2018;50:607-612. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis (0)] |