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 *

291 related articles for article (PubMed ID: 31489473)

  • 1. CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.
    Daye D; Staziaki PV; Furtado VF; Tabari A; Fintelmann FJ; Frenk NE; Shyn P; Tuncali K; Silverman S; Arellano R; Gee MS; Uppot RN
    Cardiovasc Intervent Radiol; 2019 Dec; 42(12):1771-1776. PubMed ID: 31489473
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.
    Taguchi N; Oda S; Yokota Y; Yamamura S; Imuta M; Tsuchigame T; Nagayama Y; Kidoh M; Nakaura T; Shiraishi S; Funama Y; Shinriki S; Miyamoto Y; Baba H; Yamashita Y
    Eur J Radiol; 2019 Sep; 118():38-43. PubMed ID: 31439256
    [TBL] [Abstract][Full Text] [Related]  

  • 4. CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases.
    Taghavi M; Staal F; Gomez Munoz F; Imani F; Meek DB; Simões R; Klompenhouwer LG; van der Heide UA; Beets-Tan RGH; Maas M
    Cardiovasc Intervent Radiol; 2021 Jun; 44(6):913-920. PubMed ID: 33506278
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.
    Purkayastha S; Xiao Y; Jiao Z; Thepumnoeysuk R; Halsey K; Wu J; Tran TML; Hsieh B; Choi JW; Wang D; Vallières M; Wang R; Collins S; Feng X; Feldman M; Zhang PJ; Atalay M; Sebro R; Yang L; Fan Y; Liao WH; Bai HX
    Korean J Radiol; 2021 Jul; 22(7):1213-1224. PubMed ID: 33739635
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer.
    Daye D; Tabari A; Kim H; Chang K; Kamran SC; Hong TS; Kalpathy-Cramer J; Gee MS
    Eur Radiol; 2021 Aug; 31(8):5759-5767. PubMed ID: 33454799
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
    Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
    Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours.
    Hu J; Zhao Y; Li M; Liu Y; Wang F; Weng Q; You R; Cao D
    Eur J Radiol; 2020 May; 126():108929. PubMed ID: 32169748
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An investigation of machine learning methods in delta-radiomics feature analysis.
    Chang Y; Lafata K; Sun W; Wang C; Chang Z; Kirkpatrick JP; Yin FF
    PLoS One; 2019; 14(12):e0226348. PubMed ID: 31834910
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies.
    Masuda T; Nakaura T; Funama Y; Sugino K; Sato T; Yoshiura T; Baba Y; Awai K
    Radiography (Lond); 2021 Aug; 27(3):920-926. PubMed ID: 33762147
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.
    Cozzi L; Dinapoli N; Fogliata A; Hsu WC; Reggiori G; Lobefalo F; Kirienko M; Sollini M; Franceschini D; Comito T; Franzese C; Scorsetti M; Wang PM
    BMC Cancer; 2017 Dec; 17(1):829. PubMed ID: 29207975
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans.
    Moawad AW; Ahmed A; Fuentes DT; Hazle JD; Habra MA; Elsayes KM
    Abdom Radiol (NY); 2021 Oct; 46(10):4853-4863. PubMed ID: 34085089
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.
    Detrait MY; Warnon S; Lagasse R; Dumont L; De Prophétis S; Hansenne A; Raedemaeker J; Robin V; Verstraete G; Gillain A; Depasse N; Jacmin P; Pranger D
    PLoS One; 2024; 19(10):e0311261. PubMed ID: 39352921
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT.
    Depeursinge A; Yanagawa M; Leung AN; Rubin DL
    Med Phys; 2015 Apr; 42(4):2054-63. PubMed ID: 25832095
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.
    Zhou Y; Zhan Y; Zhao J; Zhong L; Tan Y; Zeng W; Zeng Q; Gong M; Li A; Gong L; Liu L
    Acad Radiol; 2024 Jul; 31(7):2859-2871. PubMed ID: 38302388
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.
    Wang T; Gong J; Li Q; Chu C; Shen W; Peng W; Gu Y; Li W
    Eur Radiol; 2021 Aug; 31(8):6125-6135. PubMed ID: 33486606
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 15.