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Cited by in F6Publishing
For: Wang T, Gong J, Li Q, Chu C, Shen W, Peng W, Gu Y, Li W. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI. Eur Radiol 2021;31:6125-35. [PMID: 33486606 DOI: 10.1007/s00330-020-07678-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Lin YC, Lin Y, Huang YL, Ho CY, Chiang HJ, Lu HY, Wang CC, Wang JJ, Ng SH, Lai CH, Lin G. Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights Imaging 2023;14:14. [PMID: 36690870 DOI: 10.1186/s13244-022-01356-8] [Reference Citation Analysis]
2 Zhao LM, Hu R, Xie FF, Clay Kargilis D, Imami M, Yang S, Guo JQ, Jiao X, Chen RT, Wei-Hua L, Li L. Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System. J Magn Reson Imaging 2023;57:227-35. [PMID: 35652509 DOI: 10.1002/jmri.28276] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Toyohara Y, Sone K, Noda K, Yoshida K, Kurokawa R, Tanishima T, Kato S, Inui S, Nakai Y, Ishida M, Gonoi W, Tanimoto S, Takahashi Y, Inoue F, Kukita A, Kawata Y, Taguchi A, Furusawa A, Miyamoto Y, Tsukazaki T, Tanikawa M, Iriyama T, Mori-Uchino M, Tsuruga T, Oda K, Yasugi T, Takechi K, Abe O, Osuga Y. Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases. Sci Rep 2022;12:19612. [PMID: 36385486 DOI: 10.1038/s41598-022-23064-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Zhou Y, Zhang J, Chen J, Yang C, Gong C, Li C, Li F. Prediction using T2-weighted magnetic resonance imaging-based radiomics of residual uterine myoma regrowth after high-intensity focused ultrasound ablation. Ultrasound Obstet Gynecol 2022;60:681-92. [PMID: 36054291 DOI: 10.1002/uog.26053] [Reference Citation Analysis]
5 Dai M, Liu Y, Hu Y, Li G, Zhang J, Xiao Z, Lv F. Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas. Eur Radiol 2022;32:7988-97. [PMID: 35583712 DOI: 10.1007/s00330-022-08783-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
6 Crombé A, Roulleau-Dugage M, Italiano A. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. Cancer Commun (Lond) 2022;42:1288-313. [PMID: 36260064 DOI: 10.1002/cac2.12373] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Fiste O, Liontos M, Zagouri F, Stamatakos G, Dimopoulos MA. Machine learning applications in gynecological cancer: A critical review. Crit Rev Oncol Hematol 2022;179:103808. [PMID: 36087852 DOI: 10.1016/j.critrevonc.2022.103808] [Reference Citation Analysis]
8 Liu J, Wang Z. Advances in the Preoperative Identification of Uterine Sarcoma. Cancers 2022;14:3517. [DOI: 10.3390/cancers14143517] [Reference Citation Analysis]
9 Zheng Y, Wang H, Li Q, Sun H, Guo L. Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI. Acad Radiol 2022:S1076-6332(22)00331-2. [PMID: 35810066 DOI: 10.1016/j.acra.2022.06.007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Shrestha P, Poudyal B, Yadollahi S, E. Wright D, V. Gregory A, D. Warner J, Korfiatis P, C. Green I, L. Rassier S, Mariani A, Kim B, K. Laughlin-tommaso S, L. Kline T. A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions. Gynecologic Oncology 2022. [DOI: 10.1016/j.ygyno.2022.07.024] [Reference Citation Analysis]
11 Yu Q, Wang A, Gu J, Li Q, Ning Y, Peng J, Lv F, Zhang X. Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors. Front Oncol 2022;12:913898. [DOI: 10.3389/fonc.2022.913898] [Reference Citation Analysis]
12 Lin Y, Wu RC, Huang YL, Chen K, Tseng SC, Wang CJ, Chao A, Lai CH, Lin G. Uterine fibroid-like tumors: spectrum of MR imaging findings and their differential diagnosis. Abdom Radiol (NY) 2022;47:2197-208. [PMID: 35347386 DOI: 10.1007/s00261-022-03431-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Żak K, Zaremba B, Rajtak A, Kotarski J, Amant F, Bobiński M. Preoperative Differentiation of Uterine Leiomyomas and Leiomyosarcomas: Current Possibilities and Future Directions. Cancers (Basel) 2022;14:1966. [PMID: 35454875 DOI: 10.3390/cancers14081966] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021;13:3590. [PMID: 34298803 DOI: 10.3390/cancers13143590] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]