These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
176 related articles for article (PubMed ID: 34538748)
1. A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma. Nassiri N; Maas M; Cacciamani G; Varghese B; Hwang D; Lei X; Aron M; Desai M; Oberai AA; Cen SY; Gill IS; Duddalwar VA Eur Urol Focus; 2022 Jul; 8(4):988-994. PubMed ID: 34538748 [TBL] [Abstract][Full Text] [Related]
2. Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach. Uhlig J; Biggemann L; Nietert MM; Beißbarth T; Lotz J; Kim HS; Trojan L; Uhlig A Medicine (Baltimore); 2020 Apr; 99(16):e19725. PubMed ID: 32311963 [TBL] [Abstract][Full Text] [Related]
3. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation. Wentland AL; Yamashita R; Kino A; Pandit P; Shen L; Brooke Jeffrey R; Rubin D; Kamaya A Abdom Radiol (NY); 2023 Feb; 48(2):642-648. PubMed ID: 36370180 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning. Massa'a RN; Stoeckl EM; Lubner MG; Smith D; Mao L; Shapiro DD; Abel EJ; Wentland AL Abdom Radiol (NY); 2022 Aug; 47(8):2896-2904. PubMed ID: 35723716 [TBL] [Abstract][Full Text] [Related]
6. 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]
7. Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting. Uhlig A; Uhlig J; Leha A; Biggemann L; Bachanek S; Stöckle M; Reichert M; Lotz J; Zeuschner P; Maßmann A Eur Radiol; 2024 Oct; 34(10):6254-6263. PubMed ID: 38634876 [TBL] [Abstract][Full Text] [Related]
8. An In-vivo Prospective Study of the Diagnostic Yield and Accuracy of Optical Biopsy Compared with Conventional Renal Mass Biopsy for the Diagnosis of Renal Cell Carcinoma: The Interim Analysis. Buijs M; Wagstaff PGK; de Bruin DM; Zondervan PJ; Savci-Heijink CD; van Delden OM; van Leeuwen TG; van Moorselaar RJA; de la Rosette JJMCH; Laguna Pes MP Eur Urol Focus; 2018 Dec; 4(6):978-985. PubMed ID: 29079496 [TBL] [Abstract][Full Text] [Related]
9. Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid ultrasound morphology. Moro F; Vagni M; Tran HE; Bernardini F; Mascilini F; Ciccarone F; Nero C; Giannarelli D; Boldrini L; Fagotti A; Scambia G; Valentin L; Testa AC Ultrasound Obstet Gynecol; 2024 May; ():. PubMed ID: 38748935 [TBL] [Abstract][Full Text] [Related]
10. Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses. Mayoral M; Pagano AM; Araujo-Filho JAB; Zheng J; Perez-Johnston R; Tan KS; Gibbs P; Fernandes Shepherd A; Rimner A; Simone II CB; Riely G; Huang J; Ginsberg MS Lung Cancer; 2023 Apr; 178():206-212. PubMed ID: 36871345 [TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. A preoperative prognostic nomogram for solid enhancing renal tumors 7 cm or less amenable to partial nephrectomy. Lane BR; Babineau D; Kattan MW; Novick AC; Gill IS; Zhou M; Weight CJ; Campbell SC J Urol; 2007 Aug; 178(2):429-34. PubMed ID: 17561141 [TBL] [Abstract][Full Text] [Related]
14. Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics. Varghese BA; Lee S; Cen S; Talebi A; Mohd P; Stahl D; Perkins M; Desai B; Duddalwar VA; Larsen LH J Ultrasound; 2022 Sep; 25(3):699-708. PubMed ID: 35040103 [TBL] [Abstract][Full Text] [Related]
15. Using Aorta-Lesion-Attenuation Difference on Preoperative Contrast-enhanced Computed Tomography Scan to Differentiate Between Malignant and Benign Renal Tumors. Grajo JR; Terry RS; Ruoss J; Noennig BJ; Pavlinec JG; Bozorgmehri S; Crispen PL; Su LM Urology; 2019 Mar; 125():123-130. PubMed ID: 30552939 [TBL] [Abstract][Full Text] [Related]
16. Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists. Sun XY; Feng QX; Xu X; Zhang J; Zhu FP; Yang YH; Zhang YD AJR Am J Roentgenol; 2020 Jan; 214(1):W44-W54. PubMed ID: 31553660 [No Abstract] [Full Text] [Related]
17. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion. Zabihollahy F; Schieda N; Krishna S; Ukwatta E Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661 [TBL] [Abstract][Full Text] [Related]
18. 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]
19. Differentiating Oncocytic Renal Tumors from Chromophobe Renal Cell Carcinoma: Comparison of Peak Early-phase Enhancement Ratio to Clinical Risk Factors and Rater Predictions. Patel HD; Huai K; Elliott N; Thorson DL; Rac G; Picken MM; Quek ML; Bova D; Gupta GN Eur Urol Open Sci; 2022 Dec; 46():8-14. PubMed ID: 36506255 [TBL] [Abstract][Full Text] [Related]
20. Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors. von Schacky CE; Wilhelm NJ; Schäfer VS; Leonhardt Y; Jung M; Jungmann PM; Russe MF; Foreman SC; Gassert FG; Gassert FT; Schwaiger BJ; Mogler C; Knebel C; von Eisenhart-Rothe R; Makowski MR; Woertler K; Burgkart R; Gersing AS Eur Radiol; 2022 Sep; 32(9):6247-6257. PubMed ID: 35396665 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]