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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

483 related articles for article (PubMed ID: 30747299)

  • 1. Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning.
    De Perrot T; Hofmeister J; Burgermeister S; Martin SP; Feutry G; Klein J; Montet X
    Eur Radiol; 2019 Sep; 29(9):4776-4782. PubMed ID: 30747299
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Distinguishing pelvic phleboliths from distal ureteral stones on routine unenhanced helical CT: is there a radiolucent center?
    Traubici J; Neitlich JD; Smith RC
    AJR Am J Roentgenol; 1999 Jan; 172(1):13-7. PubMed ID: 9888730
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.
    Große Hokamp N; Lennartz S; Salem J; Pinto Dos Santos D; Heidenreich A; Maintz D; Haneder S
    Eur Radiol; 2020 Mar; 30(3):1397-1404. PubMed ID: 31773296
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network.
    Jendeberg J; Thunberg P; Lidén M
    Urolithiasis; 2021 Feb; 49(1):41-49. PubMed ID: 32107579
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. Distinguishing pelvic phleboliths from distal ureteral calculi: thin-slice CT findings.
    Arac M; Celik H; Oner AY; Gultekin S; Gumus T; Kosar S
    Eur Radiol; 2005 Jan; 15(1):65-70. PubMed ID: 15448998
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Central lucency of pelvic phleboliths: comparison of radiographs and noncontrast helical CT.
    Kim JC
    Clin Imaging; 2001; 25(2):122-5. PubMed ID: 11483423
    [TBL] [Abstract][Full Text] [Related]  

  • 9. CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.
    Zhu B; Nie Y; Zheng S; Lin S; Li Z; Wu W
    Urolithiasis; 2024 Jun; 52(1):91. PubMed ID: 38878124
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Evaluation of Urinary Stone Composition and Differentiation between Urinary Stones and Phleboliths Using Single-source Dual-energy Computed Tomography.
    Ogawa N; Sato S; Ida K; Kato K; Ariyoshi Y; Wada K; Nasu Y; Kanazawa S
    Acta Med Okayama; 2017 Apr; 71(2):91-96. PubMed ID: 28420889
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
    Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
    Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones.
    Mannil M; von Spiczak J; Hermanns T; Poyet C; Alkadhi H; Fankhauser CD
    J Urol; 2018 Oct; 200(4):829-836. PubMed ID: 29673945
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.
    Zhang Y; Zhang B; Liang F; Liang S; Zhang Y; Yan P; Ma C; Liu A; Guo F; Jiang C
    Eur Radiol; 2019 Apr; 29(4):2157-2165. PubMed ID: 30306329
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.
    Granata V; Fusco R; Setola SV; Brunese MC; Di Mauro A; Avallone A; Ottaiano A; Normanno N; Petrillo A; Izzo F
    Radiol Med; 2024 Jul; 129(7):957-966. PubMed ID: 38761342
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively.
    Chen T; Ning Z; Xu L; Feng X; Han S; Roth HR; Xiong W; Zhao X; Hu Y; Liu H; Yu J; Zhang Y; Li Y; Xu Y; Mori K; Li G
    Eur Radiol; 2019 Mar; 29(3):1074-1082. PubMed ID: 30116959
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
    Shu J; Wen D; Xi Y; Xia Y; Cai Z; Xu W; Meng X; Liu B; Yin H
    Eur J Radiol; 2019 Dec; 121():108738. PubMed ID: 31756634
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.
    Huang L; Ye Y; Chen J; Feng W; Peng S; Du X; Li X; Song Z; Liu T
    Diagn Interv Radiol; 2024 Jul; 30(4):236-247. PubMed ID: 38164893
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
    Yin P; Mao N; Zhao C; Wu J; Sun C; Chen L; Hong N
    Eur Radiol; 2019 Apr; 29(4):1841-1847. PubMed ID: 30280245
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.
    Dana J; Lefebvre TL; Savadjiev P; Bodard S; Gauvin S; Bhatnagar SR; Forghani R; Hélénon O; Reinhold C
    Eur Radiol; 2022 Jun; 32(6):4116-4127. PubMed ID: 35066631
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.