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For: Ekert K, Hinterleitner C, Horger M. Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis. Eur J Radiol. 2019;116:98-105. [PMID: 31153581 DOI: 10.1016/j.ejrad.2019.04.018] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
Number Citing Articles
1 Tian Y, Komolafe TE, Chen T, Zhou B, Yang X. Prediction of TACE Treatment Response in a Preoperative MRI via Analysis of Integrating Deep Learning and Radiomics Features. J Med Biol Eng . [DOI: 10.1007/s40846-022-00692-w] [Reference Citation Analysis]
2 Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021;13:2522. [PMID: 34063937 DOI: 10.3390/cancers13112522] [Reference Citation Analysis]
3 George-jones NA, Wang K, Wang J, Hunter JB. Prediction of Vestibular Schwannoma Enlargement After Radiosurgery Using Tumor Shape and MRI Texture Features. Otology & Neurotology 2021;42:e348-54. [DOI: 10.1097/mao.0000000000002938] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
4 Cannella R, La Grutta L, Midiri M, Bartolotta TV. New advances in radiomics of gastrointestinal stromal tumors. World J Gastroenterol 2020; 26(32): 4729-4738 [PMID: 32921953 DOI: 10.3748/wjg.v26.i32.4729] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
5 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]
6 Yang CW, Liu XJ, Liu SY, Wan S, Ye Z, Song B. Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging. Contrast Media Mol Imaging 2020;2020:6058159. [PMID: 33304203 DOI: 10.1155/2020/6058159] [Reference Citation Analysis]
7 Liu S, Zhang C, Liu R, Li S, Xu F, Liu X, Li Z, Hu Y, Ge Y, Chen J, Zhang Z. CT Texture Analysis for Preoperative Identification of Lymphoma from Other Types of Primary Small Bowel Malignancies. Biomed Res Int 2021;2021:5519144. [PMID: 33884262 DOI: 10.1155/2021/5519144] [Reference Citation Analysis]
8 Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JTM, Boellaard R, Punt CJA, Huiskens J, Kazemier G. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2021;48:1785-94. [PMID: 33326049 DOI: 10.1007/s00259-020-05142-w] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
9 Tang X, Pang T, Yan WF, Qian WL, Gong YL, Yang ZG. The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy. Front Oncol 2021;11:591502. [PMID: 33747910 DOI: 10.3389/fonc.2021.591502] [Reference Citation Analysis]
10 Yang M, Cao Q, Xu Z, Ge Y, Li S, Yan F, Yang W. Development and Validation of a Machine Learning-Based Radiomics Model on Cardiac Computed Tomography of Epicardial Adipose Tissue in Predicting Characteristics and Recurrence of Atrial Fibrillation. Front Cardiovasc Med 2022;9:813085. [DOI: 10.3389/fcvm.2022.813085] [Reference Citation Analysis]
11 Su X, Sun H, Chen N, Roberts N, Yang X, Wang W, Li J, Huang X, Gong Q, Yue Q. A radiomics-clinical nomogram for preoperative prediction of IDH1 mutation in primary glioblastoma multiforme. Clin Radiol 2020;75:963.e7-963.e15. [PMID: 32921406 DOI: 10.1016/j.crad.2020.07.036] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]