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For: Zhu W, Xie L, Han J, Guo X. The Application of Deep Learning in Cancer Prognosis Prediction. Cancers (Basel). 2020;12:603. [PMID: 32150991 DOI: 10.3390/cancers12030603] [Cited by in Crossref: 44] [Cited by in F6Publishing: 26] [Article Influence: 22.0] [Reference Citation Analysis]
Number Citing Articles
1 Xie CY, Pang CL, Chan B, Wong EY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021;13:2469. [PMID: 34069367 DOI: 10.3390/cancers13102469] [Reference Citation Analysis]
2 Koumakis L. Deep learning models in genomics; are we there yet? Comput Struct Biotechnol J 2020;18:1466-73. [PMID: 32637044 DOI: 10.1016/j.csbj.2020.06.017] [Cited by in Crossref: 19] [Cited by in F6Publishing: 9] [Article Influence: 9.5] [Reference Citation Analysis]
3 Vale-Silva LA, Rohr K. Long-term cancer survival prediction using multimodal deep learning. Sci Rep 2021;11:13505. [PMID: 34188098 DOI: 10.1038/s41598-021-92799-4] [Reference Citation Analysis]
4 Li H, Qiu L, Wang M. Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers. Sensors (Basel) 2021;21:6484. [PMID: 34640802 DOI: 10.3390/s21196484] [Reference Citation Analysis]
5 Adeoye J, Tan JY, Choi SW, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform 2021;154:104557. [PMID: 34455119 DOI: 10.1016/j.ijmedinf.2021.104557] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Alabi RO, Almangush A, Elmusrati M, Mäkitie AA. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine. Front Oral Health 2022;2:794248. [DOI: 10.3389/froh.2021.794248] [Reference Citation Analysis]
7 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
8 Chen S, Zhang E, Jiang L, Wang T, Guo T, Gao F, Zhang N, Wang X, Zheng J. Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm. Front Immunol 2022;13:798471. [DOI: 10.3389/fimmu.2022.798471] [Reference Citation Analysis]
9 Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020;18:2312-25. [PMID: 32994890 DOI: 10.1016/j.csbj.2020.08.003] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
10 Gómez-veiga F, Alcaraz-asensio A, Burgos-revilla J, Cózar-olmo F. Avances en Uro-Oncología «OncoUrology Forum Special Edition»: lo mejor del 2020. Actas Urológicas Españolas 2021. [DOI: 10.1016/j.acuro.2021.09.001] [Reference Citation Analysis]
11 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]
12 Gómez-Veiga F, Alcaraz-Asensio A, Burgos-Revilla JM, Cózar-Olmo FJ. Advances in urologic oncology "OncoUrology Forum Special Edition": The best of 2020. Actas Urol Esp (Engl Ed) 2021:S2173-5786(21)00143-8. [PMID: 34844900 DOI: 10.1016/j.acuroe.2021.09.001] [Reference Citation Analysis]
13 Hayashi H, Uemura N, Matsumura K, Zhao L, Sato H, Shiraishi Y, Yamashita YI, Baba H. Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma. World J Gastroenterol 2021; 27(43): 7480-7496 [PMID: 34887644 DOI: 10.3748/wjg.v27.i43.7480] [Reference Citation Analysis]
14 Kang M, Ko E, Mersha TB. A roadmap for multi-omics data integration using deep learning. Brief Bioinform 2021:bbab454. [PMID: 34791014 DOI: 10.1093/bib/bbab454] [Reference Citation Analysis]
15 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]
16 Chen Z, Li S, Wang Y, Fu Z, Liu N, Wang H, Liu X. Overall Survival Benefit in Rectal Cancer After Neoadjuvant Radiotherapy and Adjuvant Chemotherapy: A Propensity-Matched Population-Based Study. Front Oncol 2020;10:584835. [PMID: 33363014 DOI: 10.3389/fonc.2020.584835] [Reference Citation Analysis]
17 Coulouarn C. Artificial intelligence and omics in cancer. Artif Intell Cancer 2020; 1(1): 1-7 [DOI: 10.35713/aic.v1.i1.1] [Reference Citation Analysis]
18 Gupta S, Gupta MK. A Comparative Analysis of Deep Learning Approaches for Predicting Breast Cancer Survivability. Arch Computat Methods Eng. [DOI: 10.1007/s11831-021-09679-3] [Reference Citation Analysis]
19 Vélez JI, Samper LA, Arcos-Holzinger M, Espinosa LG, Isaza-Ruget MA, Lopera F, Arcos-Burgos M. A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease. Diagnostics (Basel) 2021;11:887. [PMID: 34067584 DOI: 10.3390/diagnostics11050887] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Liu L, Huang J, Wei B, Mo J, Wei Q, Chen C, Yan W, Huang X, He F, Qin L, Huang H, Li X, Pan X. Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation. Biomed Res Int 2021;2021:6644827. [PMID: 33834070 DOI: 10.1155/2021/6644827] [Reference Citation Analysis]
21 Cornish TC. Artificial intelligence for automating the measurement of histologic image biomarkers. J Clin Invest 2021;131:147966. [PMID: 33855974 DOI: 10.1172/JCI147966] [Reference Citation Analysis]
22 Waljee AK, Weinheimer-Haus EM, Abubakar A, Ngugi AK, Siwo GH, Kwakye G, Singal AG, Rao A, Saini SD, Read AJ, Baker JA, Balis U, Opio CK, Zhu J, Saleh MN. Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa. Gut 2022:gutjnl-2022-327211. [PMID: 35418482 DOI: 10.1136/gutjnl-2022-327211] [Reference Citation Analysis]
23 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]
24 Zadeh Shirazi A, Fornaciari E, McDonnell MD, Yaghoobi M, Cevallos Y, Tello-Oquendo L, Inca D, Gomez GA. The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey. J Pers Med 2020;10:E224. [PMID: 33198332 DOI: 10.3390/jpm10040224] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
25 Shi Z, Zhu C, Zhang Y, Wang Y, Hou W, Li X, Lu J, Guo X, Xu F, Jiang X, Wang Y, Liu J, Jin M. Deep learning for automatic diagnosis of gastric dysplasia using whole-slide histopathology images in endoscopic specimens. Gastric Cancer 2022. [PMID: 35394573 DOI: 10.1007/s10120-022-01294-w] [Reference Citation Analysis]
26 Domblides C, Rochefort J, Riffard C, Panouillot M, Lescaille G, Teillaud JL, Mateo V, Dieu-Nosjean MC. Tumor-Associated Tertiary Lymphoid Structures: From Basic and Clinical Knowledge to Therapeutic Manipulation. Front Immunol 2021;12:698604. [PMID: 34276690 DOI: 10.3389/fimmu.2021.698604] [Reference Citation Analysis]
27 Benzekry S. Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology. Clin Pharmacol Ther 2020;108:471-86. [PMID: 32557598 DOI: 10.1002/cpt.1951] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 5.5] [Reference Citation Analysis]
28 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]
29 Rafique R, Islam SMR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021;19:4003-17. [PMID: 34377366 DOI: 10.1016/j.csbj.2021.07.003] [Reference Citation Analysis]
30 Guo W, Liang W, Deng Q, Zou X. A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients. Front Genet 2021;12:709027. [PMID: 34490038 DOI: 10.3389/fgene.2021.709027] [Reference Citation Analysis]
31 Wu Y, Cheng M, Huang S, Pei Z, Zuo Y, Liu J, Yang K, Zhu Q, Zhang J, Hong H, Zhang D, Huang K, Cheng L, Shao W. Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications. Cancers 2022;14:1199. [DOI: 10.3390/cancers14051199] [Reference Citation Analysis]
32 Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021;19:3735-46. [PMID: 34285775 DOI: 10.1016/j.csbj.2021.06.030] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
33 Ghosh A, Chaudhuri D, Adhikari S, Chatterjee K, Roychowdhury A, Das AK, Barui A. Deep reinforced neural network model for cyto-spectroscopic analysis of epigenetic markers for automated oral cancer risk prediction. Chemometrics and Intelligent Laboratory Systems 2022. [DOI: 10.1016/j.chemolab.2022.104548] [Reference Citation Analysis]
34 Cheng ASK, Guan Q, Su Y, Zhou P, Zeng Y. Integration of Machine Learning and Blockchain Technology in the Healthcare Field: A Literature Review and Implications for Cancer Care. Asia Pac J Oncol Nurs 2021;8:720-4. [PMID: 34790856 DOI: 10.4103/apjon.apjon-2140] [Reference Citation Analysis]
35 Lai X, Zhou J, Wessely A, Heppt M, Maier A, Berking C, Vera J, Zhang L. A disease network-based deep learning approach for characterizing melanoma. Int J Cancer 2021. [PMID: 34716589 DOI: 10.1002/ijc.33860] [Reference Citation Analysis]
36 Li W, Hong T, Liu W, Dong S, Wang H, Tang Z, Li W, Wang B, Hu Z, Liu Q, Qin Y, Yin C. Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma. Front Med 2022;9:807382. [DOI: 10.3389/fmed.2022.807382] [Reference Citation Analysis]
37 Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021;49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
38 Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 26] [Cited by in F6Publishing: 4] [Article Influence: 26.0] [Reference Citation Analysis]
39 Alabi RO, Bello IO, Youssef O, Elmusrati M, Mäkitie AA, Almangush A. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. Front Oral Health 2021;2:686863. [PMID: 35048032 DOI: 10.3389/froh.2021.686863] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Fitzgerald J, Higgins D, Mazo Vargas C, Watson W, Mooney C, Rahman A, Aspell N, Connolly A, Aura Gonzalez C, Gallagher W. Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer. J Clin Pathol 2021;74:429-34. [PMID: 34117103 DOI: 10.1136/jclinpath-2020-207351] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Qu J, Zhao Q, Yang L, Ping Y, Zhang K, Lei Q, Liu F, Zhang Y. Identification and characterization of prognosis-related genes in the tumor microenvironment of esophageal squamous cell carcinoma. Int Immunopharmacol 2021;96:107616. [PMID: 34162127 DOI: 10.1016/j.intimp.2021.107616] [Reference Citation Analysis]
42 Fridrichova I, Kalinkova L, Karhanek M, Smolkova B, Machalekova K, Wachsmannova L, Nikolaieva N, Kajo K. miR-497-5p Decreased Expression Associated with High-Risk Endometrial Cancer. Int J Mol Sci 2020;22:E127. [PMID: 33374439 DOI: 10.3390/ijms22010127] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
43 Kimutai G, Ngenzi A, Said RN, Kiprop A, Förster A. An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks. Data 2020;5:44. [DOI: 10.3390/data5020044] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
44 Roy S, Kiral I, Mirmomeni M, Mummert T, Braz A, Tsay J, Tang J, Asif U, Schaffter T, Ahsen ME, Iwamori T, Yanagisawa H, Poonawala H, Madan P, Qin Y, Picone J, Obeid I, Marques BA, Maetschke S, Khalaf R, Rosen-Zvi M, Stolovitzky G, Harrer S; IBM Epilepsy Consortium. Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine 2021;66:103275. [PMID: 33745882 DOI: 10.1016/j.ebiom.2021.103275] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 Bohannan ZS, Coffman F, Mitrofanova A. Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.01.003] [Reference Citation Analysis]
46 Andreeva V, Aksamentova E, Muhachev A, Solovey A, Litvinov I, Gusarov A, Shevtsova NN, Kushkin D, Litvinova K. Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer. Diagnostics 2022;12:72. [DOI: 10.3390/diagnostics12010072] [Reference Citation Analysis]
47 Christie JR, Lang P, Zelko LM, Palma DA, Abdelrazek M, Mattonen SA. Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making. Can Assoc Radiol J. 2021;72:86-97. [PMID: 32735493 DOI: 10.1177/0846537120941434] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
48 Bai T, Zhu X, Zhou X, Grathwohl D, Yang P, Zha Y, Jin Y, Chong H, Yu Q, Isberner N, Wang D, Zhang L, Kortüm KM, Song J, Rasche L, Einsele H, Ning K, Hou X. Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany. Front Artif Intell 2021;4:672050. [PMID: 34541519 DOI: 10.3389/frai.2021.672050] [Reference Citation Analysis]