174 related articles for article (PubMed ID: 35669501)
21. Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears.
Cheng Q; Lin H; Zhao J; Lu X; Wang Q
J Orthop Surg Res; 2024 Jan; 19(1):99. PubMed ID: 38297322
[TBL] [Abstract][Full Text] [Related]
22. Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors.
Shao S; Mao N; Liu W; Cui J; Xue X; Cheng J; Zheng N; Wang B
J Xray Sci Technol; 2020; 28(4):799-808. PubMed ID: 32538891
[TBL] [Abstract][Full Text] [Related]
23. 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]
24. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.
Feng Z; Rong P; Cao P; Zhou Q; Zhu W; Yan Z; Liu Q; Wang W
Eur Radiol; 2018 Apr; 28(4):1625-1633. PubMed ID: 29134348
[TBL] [Abstract][Full Text] [Related]
25. CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma.
Chen M; Yin F; Yu Y; Zhang H; Wen G
Cancer Imaging; 2021 Jun; 21(1):42. PubMed ID: 34162442
[TBL] [Abstract][Full Text] [Related]
26. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.
Nie P; Yang G; Wang Z; Yan L; Miao W; Hao D; Wu J; Zhao Y; Gong A; Cui J; Jia Y; Niu H
Eur Radiol; 2020 Feb; 30(2):1274-1284. PubMed ID: 31506816
[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. Ultrasound Image-Based Deep Features and Radiomics for the Discrimination of Small Fat-Poor Angiomyolipoma and Small Renal Cell Carcinoma.
Zhang L; Sun K; Shi L; Qiu J; Wang X; Wang S
Ultrasound Med Biol; 2023 Feb; 49(2):560-568. PubMed ID: 36376157
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors.
Li J; Zhang T; Ma J; Zhang N; Zhang Z; Ye Z
Front Oncol; 2022; 12():934735. PubMed ID: 36016613
[TBL] [Abstract][Full Text] [Related]
31. Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study.
Deng XY; Cao PW; Nan SM; Pan YP; Yu C; Pan T; Dai G
Clin Breast Cancer; 2023 Oct; 23(7):729-736. PubMed ID: 37481337
[TBL] [Abstract][Full Text] [Related]
32. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.
Dai H; Lu M; Huang B; Tang M; Pang T; Liao B; Cai H; Huang M; Zhou Y; Chen X; Ding H; Feng ST
Quant Imaging Med Surg; 2021 May; 11(5):1836-1853. PubMed ID: 33936969
[TBL] [Abstract][Full Text] [Related]
33. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
Erdim C; Yardimci AH; Bektas CT; Kocak B; Koca SB; Demir H; Kilickesmez O
Acad Radiol; 2020 Oct; 27(10):1422-1429. PubMed ID: 32014404
[TBL] [Abstract][Full Text] [Related]
34. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.
Shen Y; Xu F; Zhu W; Hu H; Chen T; Li Q
Ann Transl Med; 2020 Mar; 8(5):171. PubMed ID: 32309318
[TBL] [Abstract][Full Text] [Related]
35. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
Lee H; Hong H; Kim J; Jung DC
Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
[TBL] [Abstract][Full Text] [Related]
36. MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study.
Jian L; Liu Y; Xie Y; Jiang S; Ye M; Lin H
Front Oncol; 2022; 12():876664. PubMed ID: 35719934
[TBL] [Abstract][Full Text] [Related]
37. Radiomics Analysis of Computed Tomography for Prediction of Thyroid Capsule Invasion in Papillary Thyroid Carcinoma: A Multi-Classifier and Two-Center Study.
Wu X; Yu P; Jia C; Mao N; Che K; Li G; Zhang H; Mou Y; Song X
Front Endocrinol (Lausanne); 2022; 13():849065. PubMed ID: 35692398
[TBL] [Abstract][Full Text] [Related]
38. CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors.
Lu Y; Liu H; Liu Q; Wang S; Zhu Z; Qiu J; Xing W
Front Oncol; 2023; 13():1118351. PubMed ID: 36969052
[TBL] [Abstract][Full Text] [Related]
39. Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models.
Li T; Liu Y; Guo J; Wang Y
J Xray Sci Technol; 2022; 30(6):1155-1168. PubMed ID: 35988261
[TBL] [Abstract][Full Text] [Related]
40. Dynamic contract-enhanced CT-based radiomics for differentiation of pancreatobiliary-type and intestinal-type periampullary carcinomas.
Bi L; Yang L; Ma J; Cai S; Li L; Huang C; Xu J; Wang X; Huang M
Clin Radiol; 2022 Jan; 77(1):e75-e83. PubMed ID: 34753589
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
