172 related articles for article (PubMed ID: 37014470)
1. Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton.
Özgül HA; Akin IB; Mutlu U; Balci A
Skeletal Radiol; 2023 Sep; 52(9):1703-1711. PubMed ID: 37014470
[TBL] [Abstract][Full Text] [Related]
2. Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.
Kocak B; Durmaz ES; Kaya OK; Kilickesmez O
Acta Radiol; 2020 Jun; 61(6):856-864. PubMed ID: 31635476
[TBL] [Abstract][Full Text] [Related]
3. Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.
Kocak B; Ates E; Durmaz ES; Ulusan MB; Kilickesmez O
Eur Radiol; 2019 Sep; 29(9):4765-4775. PubMed ID: 30747300
[TBL] [Abstract][Full Text] [Related]
4. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.
Bektas CT; Kocak B; Yardimci AH; Turkcanoglu MH; Yucetas U; Koca SB; Erdim C; Kilickesmez O
Eur Radiol; 2019 Mar; 29(3):1153-1163. PubMed ID: 30167812
[TBL] [Abstract][Full Text] [Related]
5. Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.
Ren J; Yuan Y; Qi M; Tao X
Eur Radiol; 2020 Dec; 30(12):6858-6866. PubMed ID: 32591885
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
Kocak B; Yardimci AH; Bektas CT; Turkcanoglu MH; Erdim C; Yucetas U; Koca SB; Kilickesmez O
Eur J Radiol; 2018 Oct; 107():149-157. PubMed ID: 30292260
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases.
Mannam P; Murali A; Gokulakrishnan P; Venkatachalapathy E; Venkata Sai PM
Indian J Nucl Med; 2022; 37(3):217-226. PubMed ID: 36686312
[TBL] [Abstract][Full Text] [Related]
10. Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.
Zhou T; Guan J; Feng B; Xue H; Cui J; Kuang Q; Chen Y; Xu K; Lin F; Cui E; Long W
Eur Radiol; 2023 Jun; 33(6):4323-4332. PubMed ID: 36645455
[TBL] [Abstract][Full Text] [Related]
11. Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms.
Miskin N; Qin L; Silverman SG; Shinagare AB
J Comput Assist Tomogr; 2023 May-Jun 01; 47(3):376-381. PubMed ID: 37184999
[TBL] [Abstract][Full Text] [Related]
12. Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.
Kocak B; Durmaz ES; Ates E; Ulusan MB
AJR Am J Roentgenol; 2019 Mar; 212(3):W55-W63. PubMed ID: 30601030
[TBL] [Abstract][Full Text] [Related]
13. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Nazari M; Shiri I; Hajianfar G; Oveisi N; Abdollahi H; Deevband MR; Oveisi M; Zaidi H
Radiol Med; 2020 Aug; 125(8):754-762. PubMed ID: 32193870
[TBL] [Abstract][Full Text] [Related]
14. Texture analysis as a radiomic marker for differentiating renal tumors.
Yu H; Scalera J; Khalid M; Touret AS; Bloch N; Li B; Qureshi MM; Soto JA; Anderson SW
Abdom Radiol (NY); 2017 Oct; 42(10):2470-2478. PubMed ID: 28421244
[TBL] [Abstract][Full Text] [Related]
15. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
16. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma.
Lin F; Cui EM; Lei Y; Luo LP
Abdom Radiol (NY); 2019 Jul; 44(7):2528-2534. PubMed ID: 30919041
[TBL] [Abstract][Full Text] [Related]
17. Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.
Acar E; Leblebici A; Ellidokuz BE; Başbınar Y; Kaya GÇ
Br J Radiol; 2019 Sep; 92(1101):20190286. PubMed ID: 31219712
[TBL] [Abstract][Full Text] [Related]
18. XGBoost-based multiparameters from dual-energy computed tomography for the differentiation of multiple myeloma of the spine from vertebral osteolytic metastases.
Shi J; Huang H; Xu S; Du L; Zeng X; Cao Y; Liu D; Wang X; Zhang J
Eur Radiol; 2023 Jul; 33(7):4801-4811. PubMed ID: 36719494
[TBL] [Abstract][Full Text] [Related]
19. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.
Haji-Momenian S; Lin Z; Patel B; Law N; Michalak A; Nayak A; Earls J; Loew M
Abdom Radiol (NY); 2020 Mar; 45(3):789-798. PubMed ID: 31822969
[TBL] [Abstract][Full Text] [Related]
20. Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.
Yang R; Wu J; Sun L; Lai S; Xu Y; Liu X; Ma Y; Zhen X
Eur Radiol; 2020 Feb; 30(2):1254-1263. PubMed ID: 31468159
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
