157 related articles for article (PubMed ID: 38457546)
21. Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study.
Li Z; Liu Z; Guo Y; Wang S; Qu X; Li Y; Pan Y; Zhang L; Su D; Yang Q; Tao X; Yue Q; Xian J
Neuroradiology; 2022 Feb; 64(2):361-369. PubMed ID: 34860278
[TBL] [Abstract][Full Text] [Related]
22. CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma.
Zhou Z; Qian X; Hu J; Ma X; Zhou S; Dai Y; Zhu J
Abdom Radiol (NY); 2021 Jun; 46(6):2690-2698. PubMed ID: 33427908
[TBL] [Abstract][Full Text] [Related]
23. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.
Luo S; Wei R; Lu S; Lai S; Wu J; Wu Z; Pang X; Wei X; Jiang X; Zhen X; Yang R
Eur Radiol; 2022 Apr; 32(4):2340-2350. PubMed ID: 34636962
[TBL] [Abstract][Full Text] [Related]
24. Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC).
Wei J; Zhao J; Zhang X; Wang D; Zhang W; Wang Z; Zhou J
PLoS One; 2018; 13(5):e0195699. PubMed ID: 29715308
[TBL] [Abstract][Full Text] [Related]
25. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.
Lin F; Ma C; Xu J; Lei Y; Li Q; Lan Y; Sun M; Long W; Cui E
Eur J Radiol; 2020 Aug; 129():109079. PubMed ID: 32526669
[TBL] [Abstract][Full Text] [Related]
26. Machine Learning-Based Magnetic Resonance Radiomics Analysis for Predicting Low- and High-Grade Clear Cell Renal Cell Carcinoma.
Sim KC; Han NY; Cho Y; Sung DJ; Park BJ; Kim MJ; Han YE
J Comput Assist Tomogr; 2023 Nov-Dec 01; 47(6):873-881. PubMed ID: 37948361
[TBL] [Abstract][Full Text] [Related]
27. Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.
Sun X; Liu L; Xu K; Li W; Huo Z; Liu H; Shen T; Pan F; Jiang Y; Zhang M
Medicine (Baltimore); 2019 Apr; 98(14):e15022. PubMed ID: 30946334
[TBL] [Abstract][Full Text] [Related]
28. A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study.
Nie P; Liu S; Zhou R; Li X; Zhi K; Wang Y; Dai Z; Zhao L; Wang N; Zhao X; Li X; Cheng N; Wang Y; Chen C; Xu Y; Yang G
Eur J Radiol; 2023 Sep; 166():111018. PubMed ID: 37562222
[TBL] [Abstract][Full Text] [Related]
29. Advanced Virtual Monoenergetic Imaging: Improvement of Visualization and Differentiation of Intramuscular Lesions in Portal-Venous-phase Contrast-enhanced Dual-energy CT.
Kraus MS; Selo N; Kiefer LS; Esser M; Albtoush OM; Weiss J; Wichmann JL; Bamberg F; Othman AE
Acad Radiol; 2019 Nov; 26(11):1457-1465. PubMed ID: 30879946
[TBL] [Abstract][Full Text] [Related]
30. Prediction of Fuhrman nuclear grade for clear cell renal carcinoma by a multi-information fusion model that incorporates CT-based features of tumor and serum tumor associated material.
Zhang Y; Sun Z; Ma H; Wang C; Zhang W; Liu J; Li M; Zhang Y; Guo H; Ba X
J Cancer Res Clin Oncol; 2023 Nov; 149(17):15855-15865. PubMed ID: 37672076
[TBL] [Abstract][Full Text] [Related]
31. Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.
Zhou H; Mao H; Dong D; Fang M; Gu D; Liu X; Xu M; Yang S; Zou J; Yin R; Zheng H; Tian J; Pan C; Fang X
Ann Surg Oncol; 2020 Oct; 27(10):4057-4065. PubMed ID: 32424585
[TBL] [Abstract][Full Text] [Related]
32. A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.
Yang L; Gao L; Arefan D; Tan Y; Dan H; Zhang J
BMC Med Imaging; 2022 Jan; 22(1):15. PubMed ID: 35094674
[TBL] [Abstract][Full Text] [Related]
33. Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study.
Xv Y; Lv F; Guo H; Zhou X; Tan H; Xiao M; Zheng Y
Insights Imaging; 2021 Nov; 12(1):170. PubMed ID: 34800179
[TBL] [Abstract][Full Text] [Related]
34. MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma.
Bai X; Huang Q; Zuo P; Zhang X; Yuan J; Zhang X; Wang M; Xu W; Ye H; Zhao J; Sun H; Shao B; Wang H
Eur Radiol; 2021 Feb; 31(2):1029-1042. PubMed ID: 32856163
[TBL] [Abstract][Full Text] [Related]
35. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.
Cui E; Li Z; Ma C; Li Q; Lei Y; Lan Y; Yu J; Zhou Z; Li R; Long W; Lin F
Eur Radiol; 2020 May; 30(5):2912-2921. PubMed ID: 32002635
[TBL] [Abstract][Full Text] [Related]
36. The value of a dual-energy CT Iodine map radiomics model for the prediction of collagen fiber content in the ccRCC tumor microenvironment.
Li Z; Wang N; Bing X; Li Y; Yao J; Li R; Ouyang A
BMC Med Imaging; 2023 Nov; 23(1):186. PubMed ID: 37968599
[TBL] [Abstract][Full Text] [Related]
37.
Zhang L; Zhao H; Jiang H; Zhao H; Han W; Wang M; Fu P
Abdom Radiol (NY); 2021 Dec; 46(12):5618-5628. PubMed ID: 34455450
[TBL] [Abstract][Full Text] [Related]
38. A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma.
Meng X; Shu J; Xia Y; Yang R
Biomed Res Int; 2020; 2020():7103647. PubMed ID: 32775436
[TBL] [Abstract][Full Text] [Related]
39. Enhanced Computed Tomography-Based Radiomics Signature Combined With Clinical Features in Evaluating Nuclear Grading of Renal Clear Cell Carcinoma.
Yan L; Chai N; Bao Y; Ge Y; Cheng Q
J Comput Assist Tomogr; 2020; 44(5):730-736. PubMed ID: 32558771
[TBL] [Abstract][Full Text] [Related]
40. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
