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For: Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 2020;395:350-60. [PMID: 32007170 DOI: 10.1016/S0140-6736(19)32998-8] [Cited by in Crossref: 70] [Cited by in F6Publishing: 34] [Article Influence: 35.0] [Reference Citation Analysis]
Number Citing Articles
1 Banegas-Luna AJ, Peña-García J, Iftene A, Guadagni F, Ferroni P, Scarpato N, Zanzotto FM, Bueno-Crespo A, Pérez-Sánchez H. Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey. Int J Mol Sci 2021;22:4394. [PMID: 33922356 DOI: 10.3390/ijms22094394] [Reference Citation Analysis]
2 Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102474] [Reference Citation Analysis]
3 Koyuncu CF, Lu C, Bera K, Zhang Z, Xu J, Toro P, Corredor G, Chute D, Fu P, Thorstad WL, Faraji F, Bishop JA, Mehrad M, Castro PD, Sikora AG, Thompson LD, Chernock RD, Lang Kuhs KA, Luo J, Sandulache V, Adelstein DJ, Koyfman S, Lewis JS Jr, Madabhushi A. Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma. J Clin Invest 2021;131:145488. [PMID: 33651718 DOI: 10.1172/JCI145488] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Liu H, Zhao Y, Yang F, Lou X, Wu F, Li H, Xing X, Peng T, Menze B, Huang J, Zhang S, Han A, Yao J, Fan X. Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning. BME Frontiers 2022;2022:1-12. [DOI: 10.34133/2022/9860179] [Reference Citation Analysis]
5 Bianconi F, Kather JN, Reyes-Aldasoro CC. Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin. Cancers (Basel) 2020;12:E3337. [PMID: 33187299 DOI: 10.3390/cancers12113337] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
6 Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, Mao Q, Yu H, Cai X. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14. [PMID: 32550270 DOI: 10.1038/s41698-020-0120-3] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 10.0] [Reference Citation Analysis]
7 Li X, Xu Z, Shen X, Zhou Y, Xiao B, Li TQ. Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN. Curr Oncol 2021;28:3585-601. [PMID: 34590614 DOI: 10.3390/curroncol28050307] [Reference Citation Analysis]
8 Tan W, Guan P, Wu L, Chen H, Li J, Ling Y, Fan T, Wang Y, Li J, Yan B. The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography. Ann Transl Med 2021;9:969. [PMID: 34277769 DOI: 10.21037/atm-21-1171] [Reference Citation Analysis]
9 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
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. [PMID: 33294256 DOI: 10.7150/thno.49168] [Cited by in Crossref: 10] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
11 Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021;11:1523. [PMID: 34573865 DOI: 10.3390/diagnostics11091523] [Reference Citation Analysis]
12 Ala M, Wu J, Koundal D. Ultrasonic Omics Based on Intelligent Classification Algorithm in Hormone Receptor Expression and Efficacy Evaluation of Breast Cancer. Computational and Mathematical Methods in Medicine 2022;2022:1-8. [DOI: 10.1155/2022/6557494] [Reference Citation Analysis]
13 Lv R, Raab M, Wang Y, Tian J, Lin J, Prasad PN. Nanochemistry advancing photon conversion in rare-earth nanostructures for theranostics. Coordination Chemistry Reviews 2022;460:214486. [DOI: 10.1016/j.ccr.2022.214486] [Reference Citation Analysis]
14 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, Zhou X, Li Q, Li J, Wang J, Ma K, Qi J, Hu Z, Tang P, Deng J, Qiu X, Li BY, Shen WD, Quan RP, Yang JT, Huang LY, Xiao Y, Yang ZC, Li Z, Wang SC, Ren H, Liang C, Guo W, Li Y, Xiao H, Gu Y, Yun JP, Huang D, Song Z, Fan X, Chen L, Yan X, Huang ZC, Huang J, Luttrell J, Zhang CY, Zhou W, Zhang K, Yi C, Wu C, Shen H, Wang YP, Xiao HM, Deng HW. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med. 2021;19:76. [PMID: 33752648 DOI: 10.1186/s12916-021-01942-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
16 Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 2021;124:686-96. [PMID: 33204028 DOI: 10.1038/s41416-020-01122-x] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
17 Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020;9:E3697. [PMID: 33217963 DOI: 10.3390/jcm9113697] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
18 Valieris R, Amaro L, Osório CABT, Bueno AP, Rosales Mitrowsky RA, Carraro DM, Nunes DN, Dias-Neto E, Silva ITD. Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer. Cancers (Basel) 2020;12:E3687. [PMID: 33316873 DOI: 10.3390/cancers12123687] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
19 Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Current Oncology 2022;29:1773-95. [DOI: 10.3390/curroncol29030146] [Reference Citation Analysis]
20 Sarode SC, Sharma NK, Sarode G. A critical appraisal on cancer prognosis and artificial intelligence. Future Oncol 2022. [PMID: 35137629 DOI: 10.2217/fon-2021-1528] [Reference Citation Analysis]
21 Cyll K, Kleppe A, Kalsnes J, Vlatkovic L, Pradhan M, Kildal W, Tobin KAR, Reine TM, Wæhre H, Brennhovd B, Askautrud HA, Skaaheim Haug E, Hveem TS, Danielsen HE. PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer. Cancers (Basel) 2021;13:4291. [PMID: 34503100 DOI: 10.3390/cancers13174291] [Reference Citation Analysis]
22 Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, Liu S, He F, Zheng J, Meng X, Xie P, Yang G, Ding Y, Wei M, Yun J, Hung MC, Zhou W, Wahl DR, Lan P, Tian J, Wan X. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 2022;4:e8-e17. [PMID: 34952679 DOI: 10.1016/S2589-7500(21)00215-6] [Reference Citation Analysis]
23 Wulczyn E, Steiner DF, Moran M, Plass M, Reihs R, Tan F, Flament-Auvigne I, Brown T, Regitnig P, Chen PC, Hegde N, Sadhwani A, MacDonald R, Ayalew B, Corrado GS, Peng LH, Tse D, Müller H, Xu Z, Liu Y, Stumpe MC, Zatloukal K, Mermel CH. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit Med 2021;4:71. [PMID: 33875798 DOI: 10.1038/s41746-021-00427-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
24 Zhu X, Li X, Ong K, Zhang W, Li W, Li L, Young D, Su Y, Shang B, Peng L, Xiong W, Liu Y, Liao W, Xu J, Wang F, Liao Q, Li S, Liao M, Li Y, Rao L, Lin J, Shi J, You Z, Zhong W, Liang X, Han H, Zhang Y, Tang N, Hu A, Gao H, Cheng Z, Liang L, Yu W, Ding Y. Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears. Nat Commun 2021;12:3541. [PMID: 34112790 DOI: 10.1038/s41467-021-23913-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Hildebrand LA, Pierce CJ, Dennis M, Paracha M, Maoz A. Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer. Cancers (Basel). 2021;13. [PMID: 33494280 DOI: 10.3390/cancers13030391] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
26 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27(21): 2681-2709 [PMID: 34135549 DOI: 10.3748/wjg.v27.i21.2681] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Liu Z, Liu Y, Zhang W, Hong Y, Meng J, Wang J, Zheng S, Xu X. Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. Hepatol Int. [DOI: 10.1007/s12072-022-10321-y] [Reference Citation Analysis]
29 Yamashita R, Long J, Saleem A, Rubin DL, Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep 2021;11:2047. [PMID: 33479370 DOI: 10.1038/s41598-021-81506-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
30 Nazari E, Aghemiri M, Avan A, Mehrabian A, Tabesh H. Machine learning approaches for classification of colorectal cancer with and without feature selection method on microarray data. Gene Reports 2021;25:101419. [DOI: 10.1016/j.genrep.2021.101419] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Chen J, Xiang Y, Li L, Xu A, Hu W, Lin Z, Xu F, Lin D, Chen W, Lin H. Application of Surgical Decision Model for Patients With Childhood Cataract: A Study Based on Real World Data. Front Bioeng Biotechnol 2021;9:657866. [PMID: 34513804 DOI: 10.3389/fbioe.2021.657866] [Reference Citation Analysis]
32 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
33 Formica V, Morelli C, Riondino S, Renzi N, Nitti D, Roselli M. Artificial intelligence for the study of colorectal cancer tissue slides. Artif Intell Gastroenterol 2020; 1(3): 51-59 [DOI: 10.35712/aig.v1.i3.51] [Reference Citation Analysis]
34 Yu G, Sun K, Xu C, Shi XH, Wu C, Xie T, Meng RQ, Meng XH, Wang KS, Xiao HM, Deng HW. Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nat Commun 2021;12:6311. [PMID: 34728629 DOI: 10.1038/s41467-021-26643-8] [Reference Citation Analysis]
35 Wang M, Dong C, Gao Y, Li J, Han M, Wang L. A Deep Learning Model for the Automatic Recognition of Aplastic Anemia, Myelodysplastic Syndromes, and Acute Myeloid Leukemia Based on Bone Marrow Smear. Front Oncol 2022;12:844978. [DOI: 10.3389/fonc.2022.844978] [Reference Citation Analysis]
36 Hayashi K, Ono Y, Takamatsu M, Oba A, Ito H, Sato T, Inoue Y, Saiura A, Takahashi Y. Prediction of Recurrence Pattern of Pancreatic Cancer Post-Pancreatic Surgery Using Histology-Based Supervised Machine Learning Algorithms: A Single-Center Retrospective Study. Ann Surg Oncol 2022. [PMID: 35230581 DOI: 10.1245/s10434-022-11471-x] [Reference Citation Analysis]
37 Yeoh Y, Low TY, Abu N, Lee PY. Regulation of signal transduction pathways in colorectal cancer: implications for therapeutic resistance. PeerJ 2021;9:e12338. [PMID: 34733591 DOI: 10.7717/peerj.12338] [Reference Citation Analysis]
38 Bychkov D, Linder N, Tiulpin A, Kücükel H, Lundin M, Nordling S, Sihto H, Isola J, Lehtimäki T, Kellokumpu-Lehtinen PL, von Smitten K, Joensuu H, Lundin J. Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy. Sci Rep 2021;11:4037. [PMID: 33597560 DOI: 10.1038/s41598-021-83102-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
39 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Li F, Yang Y, Wei Y, He P, Chen J, Zheng Z, Bu H. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med 2021;19:348. [PMID: 34399795 DOI: 10.1186/s12967-021-03020-z] [Reference Citation Analysis]
41 Gabbutt C, Graham TA. Evolution's cartographer: Mapping the fitness landscape in cancer. Cancer Cell 2021;39:1311-3. [PMID: 34597592 DOI: 10.1016/j.ccell.2021.09.002] [Reference Citation Analysis]
42 Hacking SM, Wu D, Alexis C, Nasim M. A Novel Superpixel Approach to the Tumoral Microenvironment in Colorectal Cancer. J Pathol Inform 2022;13:100009. [PMID: 35223135 DOI: 10.1016/j.jpi.2022.100009] [Reference Citation Analysis]
43 Jung S, Hao J, Shivakumar M, Nam Y, Kim J, Kim MJ, Ryoo S, Choe EK, Jeong S, Park KJ, Park SC, Sohn DK, Oh JH, Won H, Kim D, Park JW. Development and validation of a novel strong prognostic index for colon cancer through a robust combination of laboratory features for systemic inflammation: a prognostic immune nutritional index. Br J Cancer. [DOI: 10.1038/s41416-022-01767-w] [Reference Citation Analysis]
44 Li S, Deng YQ, Hua HL, Li SL, Chen XX, Xie BJ, Zhu Z, Liu R, Huang J, Tao ZZ. Deep learning for locally advanced nasopharyngeal carcinoma prognostication based on pre- and post-treatment MRI. Comput Methods Programs Biomed 2022;219:106785. [PMID: 35397409 DOI: 10.1016/j.cmpb.2022.106785] [Reference Citation Analysis]
45 Tang H, Li G, Liu C, Huang D, Zhang X, Qiu Y, Liu Y. Diagnosis of lymph node metastasis in head and neck squamous cell carcinoma using deep learning. Laryngoscope Investig Oto. [DOI: 10.1002/lio2.742] [Reference Citation Analysis]
46 Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021;21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
47 Choi S, Cho SI, Ma M, Park S, Pereira S, Aum BJ, Shin S, Paeng K, Yoo D, Jung W, Ock C, Lee S, Choi Y, Chung J, Mok TS, Kim H, Kim S. Artificial intelligence–powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non–small cell lung cancer with better prediction of immunotherapy response. European Journal of Cancer 2022;170:17-26. [DOI: 10.1016/j.ejca.2022.04.011] [Reference Citation Analysis]
48 Peng J, Li W, Fan W, Zhang R, Li X, Xiao B, Dong Y, Wan D, Pan Z, Lin J, Wu X. Prognostic value of a novel biomarker combining DNA ploidy and tumor burden score for initially resectable liver metastases from patients with colorectal cancer. Cancer Cell Int 2021;21:554. [PMID: 34688293 DOI: 10.1186/s12935-021-02250-x] [Reference Citation Analysis]
49 Hayashi K, Ono Y, Ito H, Takamatsu M, Takahashi Y. ASO Author Reflections: Histology-Based Supervised Machine Learning Model Can Predict Recurrence Pattern of Pancreatic Cancer. Ann Surg Oncol. [DOI: 10.1245/s10434-022-11540-1] [Reference Citation Analysis]
50 Cai D, Duan X, Wang W, Huang ZP, Zhu Q, Zhong ME, Lv MY, Li CH, Kou WB, Wu XJ, Gao F. A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer. Front Mol Biosci 2020;7:613918. [PMID: 33490106 DOI: 10.3389/fmolb.2020.613918] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
51 Zhao J, Wang H, Zhang Y, Wang R, Liu Q, Li J, Li X, Huang H, Zhang J, Zeng Z, Zhang J, Yi Z, Zeng F. Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer. Radiother Oncol 2021:S0167-8140(21)09079-4. [PMID: 34968471 DOI: 10.1016/j.radonc.2021.12.031] [Reference Citation Analysis]
52 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
53 Ehrenreich J, Bette M, Schmidt A, Roeßler M, Bakowsky U, Geisthoff UW, Stuck BA, Mandic R. Evaluation of digital image analysis as a supportive tool for the stratification of head and neck vascular anomalies. Eur Arch Otorhinolaryngol 2020;277:2893-906. [PMID: 32488381 DOI: 10.1007/s00405-020-06097-2] [Reference Citation Analysis]
54 Tabarisaadi P, Khosravi A, Nahavandi S. Uncertainty-aware skin cancer detection: The element of doubt. Computers in Biology and Medicine 2022;144:105357. [DOI: 10.1016/j.compbiomed.2022.105357] [Reference Citation Analysis]
55 Su L, Huang S, Wang Z, Zhang Z, Wei H, Chen T. Whole slide cervical image classification based on convolutional neural network and random forest. Int J Imaging Syst Technol. [DOI: 10.1002/ima.22666] [Reference Citation Analysis]
56 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
57 Hashiguchi TCO, Oderkirk J, Slawomirski L. Fulfilling the Promise of Artificial Intelligence in the Health Sector: Let’s Get Real. Value in Health 2022. [DOI: 10.1016/j.jval.2021.11.1369] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
58 Qu W, Liu Q, Jiao X, Zhang T, Wang B, Li N, Dong T, Cui B. Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study. Front Oncol 2020;10:623818. [PMID: 33680946 DOI: 10.3389/fonc.2020.623818] [Reference Citation Analysis]
59 Diao JA, Chen RJ, Kvedar JC. Efficient cellular annotation of histopathology slides with real-time AI augmentation. NPJ Digit Med 2021;4:161. [PMID: 34811479 DOI: 10.1038/s41746-021-00534-0] [Reference Citation Analysis]
60 Yousef R, Gupta G, Yousef N, Khari M. A holistic overview of deep learning approach in medical imaging. Multimedia Systems. [DOI: 10.1007/s00530-021-00884-5] [Reference Citation Analysis]
61 Schiele S, Arndt TT, Martin B, Miller S, Bauer S, Banner BM, Brendel EM, Schenkirsch G, Anthuber M, Huss R, Märkl B, Müller G. Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images. Cancers (Basel) 2021;13:2074. [PMID: 33922988 DOI: 10.3390/cancers13092074] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
62 Nishida N, Kudo M. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor. Front Oncol 2020;10:594580. [PMID: 33409151 DOI: 10.3389/fonc.2020.594580] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
63 Wang R, Dai W, Gong J, Huang M, Hu T, Li H, Lin K, Tan C, Hu H, Tong T, Cai G. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol 2022;15. [DOI: 10.1186/s13045-022-01225-3] [Reference Citation Analysis]