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For: Jiang Y, Liang X, Han Z, Wang W, Xi S, Li T, Chen C, Yuan Q, Li N, Yu J, Xie Y, Xu Y, Zhou Z, Poultsides GA, Li G, Li R. Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study. Lancet Digit Health 2021;3:e371-82. [PMID: 34045003 DOI: 10.1016/S2589-7500(21)00065-0] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Giandola T, Maino C, Marrapodi G, Ratti M, Ragusi M, Bigiogera V, Talei Franzesi C, Corso R, Ippolito D. Imaging in Gastric Cancer: Current Practice and Future Perspectives. Diagnostics 2023;13:1276. [DOI: 10.3390/diagnostics13071276] [Reference Citation Analysis]
2 Feng N, Chen H, Wang X, Lu Y, Zhou J, Zhou Q, Wang X, Yu J, Xu J, Yu R. A CT-based nomogram established for differentiating heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model.. [DOI: 10.21203/rs.3.rs-2717399/v1] [Reference Citation Analysis]
3 Jiang Y, Li R, Li G. Artificial intelligence for clinical oncology: current status and future outlook. Sci Bull (Beijing) 2023;68:448-51. [PMID: 36822911 DOI: 10.1016/j.scib.2023.02.015] [Reference Citation Analysis]
4 Liang X, Dai J, Zhou X, Liu L, Zhang C, Jiang Y, Li N, Niu T, Xie Y, Dai Z, Wang X. An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation. J Digit Imaging 2023. [PMID: 36717520 DOI: 10.1007/s10278-023-00779-z] [Reference Citation Analysis]
5 Li N, Zhou X, Chen S, Dai J, Wang T, Zhang C, He W, Xie Y, Liang X. Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT. Front Oncol 2023;13:1127866. [PMID: 36910636 DOI: 10.3389/fonc.2023.1127866] [Reference Citation Analysis]
6 Zeng Q, Zhu Y, Li L, Feng Z, Shu X, Wu A, Luo L, Cao Y, Tu Y, Xiong J, Zhou F, Li Z. CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer. Front Oncol 2022;12:883109. [DOI: 10.3389/fonc.2022.883109] [Reference Citation Analysis]
7 Vanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, Horvat N, Pagano A, Araujo-filho JDAB, Geneslaw L, Rizvi H, Sosa R, Boehm KM, Yang S, Bodd FM, Ventura K, Hollmann TJ, Ginsberg MS, Gao J, Vanguri R, Hellmann MD, Sauter JL, Shah SP, MSK MIND Consortium. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer 2022. [DOI: 10.1038/s43018-022-00416-8] [Reference Citation Analysis]
8 Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022;10:e004848. [PMID: 35793875 DOI: 10.1136/jitc-2022-004848] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Jiang Y, Zhang Z, Yuan Q, Wang W, Wang H, Li T, Huang W, Xie J, Chen C, Sun Z, Yu J, Xu Y, Poultsides GA, Xing L, Zhou Z, Li G, Li R. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. The Lancet Digital Health 2022;4:e340-50. [DOI: 10.1016/s2589-7500(22)00040-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
10 Xiao C, Zhou M, Yang X, Wang H, Tang Z, Zhou Z, Tian Z, Liu Q, Li X, Jiang W, Luo J. Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images. Front Oncol 2022;12:844067. [DOI: 10.3389/fonc.2022.844067] [Reference Citation Analysis]
11 Khurana MP, Raaschou-Pedersen DE, Kurtzhals J, Bardram JE, Ostrowski SR, Bundgaard JS. Digital health competencies in medical school education: a scoping review and Delphi method study. BMC Med Educ 2022;22:129. [PMID: 35216611 DOI: 10.1186/s12909-022-03163-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Liang X, Bassenne M, Hristov DH, Islam MT, Zhao W, Jia M, Zhang Z, Gensheimer M, Beadle B, Le Q, Xing L. Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Comput Biol Med 2022;141:105139. [PMID: 34942395 DOI: 10.1016/j.compbiomed.2021.105139] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Wu J, Li C, Gensheimer M, Padda S, Kato F, Shirato H, Wei Y, Schönlieb CB, Price SJ, Jaffray D, Heymach J, Neal JW, Loo BW Jr, Wakelee H, Diehn M, Li R. Radiological tumor classification across imaging modality and histology. Nat Mach Intell 2021;3:787-98. [PMID: 34841195 DOI: 10.1038/s42256-021-00377-0] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
14 Klein S, Duda DG. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers (Basel) 2021;13:4919. [PMID: 34638408 DOI: 10.3390/cancers13194919] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]