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Cited by in F6Publishing
For: van Griethuysen JJM, Lambregts DMJ, Trebeschi S, Lahaye MJ, Bakers FCH, Vliegen RFA, Beets GL, Aerts HJWL, Beets-Tan RGH. Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY). 2020;45:632-643. [PMID: 31734709 DOI: 10.1007/s00261-019-02321-8] [Cited by in Crossref: 14] [Cited by in F6Publishing: 13] [Article Influence: 14.0] [Reference Citation Analysis]
Number Citing Articles
1 Crimì F, Capelli G, Spolverato G, Bao QR, Florio A, Milite Rossi S, Cecchin D, Albertoni L, Campi C, Pucciarelli S, Stramare R. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Radiol med 2020;125:1216-24. [DOI: 10.1007/s11547-020-01215-w] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
2 Song M, Li S, Wang H, Hu K, Wang F, Teng H, Wang Z, Liu J, Jia AY, Cai Y, Li Y, Zhu X, Geng J, Zhang Y, Wan X, Wang W. MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer. Br J Cancer. [DOI: 10.1038/s41416-022-01786-7] [Reference Citation Analysis]
3 Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F, Khababi NE, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Beets-Tan RGH, Lambregts DMJ. Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 2021. [PMID: 34655313 DOI: 10.1007/s00330-021-08251-8] [Reference Citation Analysis]
4 Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, Valdesi C, Croce P, Mastrodicasa D, Villani M, Trebeschi S, Serafini FL, Rosa C, Cocco G, Luberti R, Conte S, Mazzamurro L, Mereu M, Patea RL, Panara V, Marinari S, Vecchiet J, Caulo M. Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease. Sci Rep 2021;11:17237. [PMID: 34446812 DOI: 10.1038/s41598-021-96755-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Gao Y, Pham J, Yoon S, Cao M, Hu P, Yang Y. Recent Advances in Functional MRI to Predict Treatment Response for Locally Advanced Rectal Cancer. Curr Colorectal Cancer Rep 2021;17:77-87. [DOI: 10.1007/s11888-021-00470-x] [Reference Citation Analysis]
6 Petresc B, Lebovici A, Caraiani C, Feier DS, Graur F, Buruian MM. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study. Cancers (Basel) 2020;12:E1894. [PMID: 32674345 DOI: 10.3390/cancers12071894] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
7 Nougaret S, Mccague C, Tibermacine H, Vargas HA, Rizzo S, Sala E. Radiomics and radiogenomics in ovarian cancer: a literature review. Abdom Radiol 2021;46:2308-22. [DOI: 10.1007/s00261-020-02820-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
8 Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS. A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer. Front Oncol. 2020;10:574337. [PMID: 33194680 DOI: 10.3389/fonc.2020.574337] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
9 Lei M, Varghese B, Hwang D, Cen S, Lei X, Desai B, Azadikhah A, Oberai A, Duddalwar V. Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis. J Digit Imaging 2021;34:1156-70. [PMID: 34545475 DOI: 10.1007/s10278-021-00506-6] [Reference Citation Analysis]
10 Haak HE, Maas M, Trebeschi S, Beets-Tan RGH. Modern MR Imaging Technology in Rectal Cancer; There Is More Than Meets the Eye. Front Oncol 2020;10:537532. [PMID: 33117678 DOI: 10.3389/fonc.2020.537532] [Reference Citation Analysis]
11 Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol 2021; 27(25): 3802-3814 [PMID: 34321845 DOI: 10.3748/wjg.v27.i25.3802] [Reference Citation Analysis]
12 Capelli G, Campi C, Bao QR, Morra F, Lacognata C, Zucchetta P, Cecchin D, Pucciarelli S, Spolverato G, Crimì F. 18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy. Nucl Med Commun 2022. [PMID: 35471653 DOI: 10.1097/MNM.0000000000001570] [Reference Citation Analysis]
13 Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G, Dinapoli N, Peng Q, Casà C, Gambacorta MA, Valentini V, Lang J. Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol 2022;17. [DOI: 10.1186/s13014-022-02048-9] [Reference Citation Analysis]
14 Chuanji Z, Zheng W, Shaolv L, Linghou M, Yixin L, Xinhui L, Ling L, Yunjing T, Shilai Z, Shaozhou M, Boyang Z. Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery. Translational Oncology 2022;18:101352. [DOI: 10.1016/j.tranon.2022.101352] [Reference Citation Analysis]
15 Miranda J, Tan GXV, Fernandes MC, Yildirim O, Sims JA, Araujo-Filho JAB, de M Machado FA, Assuncao-Jr AN, Nomura CH, Horvat N. Rectal MRI radiomics for predicting pathological complete response: Where we are. Clin Imaging 2021;82:141-9. [PMID: 34826772 DOI: 10.1016/j.clinimag.2021.10.005] [Reference Citation Analysis]
16 Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, d'Annibale M, Croce P, Rosa C, Mastrodicasa D, Trebeschi S, Lambregts DMJ, Caposiena D, Serafini FL, Basilico R, Cocco G, Di Sebastiano P, Cinalli S, Ferretti A, Wise RG, Genovesi D, Beets-Tan RGH, Caulo M. MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Sci Rep 2021;11:5379. [PMID: 33686147 DOI: 10.1038/s41598-021-84816-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321 [PMID: 34539134 DOI: 10.3748/wjg.v27.i32.5306] [Reference Citation Analysis]
18 Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY, Ding Y. MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends. Cancer Manag Res 2021;13:4317-28. [PMID: 34103987 DOI: 10.2147/CMAR.S309252] [Reference Citation Analysis]
19 Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review [Internet]. Clin Colorectal Cancer. 2021;20:52-71. [PMID: 33349519 DOI: 10.1016/j.clcc.2020.11.001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
20 Jiang W, Li M, Tan J, Feng M, Zheng J, Chen D, Liu Z, Yan B, Wang G, Xu S, Xiao W, Gao Y, Zhuo S, Yan J. A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients. Ann Surg Oncol 2021. [PMID: 34148136 DOI: 10.1245/s10434-021-10218-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]