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 *

140 related articles for article (PubMed ID: 37153238)

  • 21. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors.
    Zheng Y; Zhou D; Liu H; Wen M
    Eur Radiol; 2022 Oct; 32(10):6953-6964. PubMed ID: 35484339
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.
    Feng XL; Wang SZ; Chen HH; Huang YX; Xin YK; Zhang T; Cheng DL; Mao L; Li XL; Liu CX; Hu YC; Wang W; Cui GB; Nan HY
    Lung Cancer; 2022 Apr; 166():150-160. PubMed ID: 35287067
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers.
    Haider SP; Qureshi AI; Jain A; Tharmaseelan H; Berson ER; Zeevi T; Werring DJ; Gross M; Mak A; Malhotra A; Sansing LH; Falcone GJ; Sheth KN; Payabvash S
    Front Neurosci; 2023; 17():1225342. PubMed ID: 37655013
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.
    Gangil T; Sharan K; Rao BD; Palanisamy K; Chakrabarti B; Kadavigere R
    PLoS One; 2022; 17(12):e0277168. PubMed ID: 36520945
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion.
    Yang Q; Sun J; Guo Y; Zeng P; Jin K; Huang C; Xu J; Hou L; Li C; Feng J
    Front Neurol; 2022; 13():839784. PubMed ID: 35775053
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A reliable grading system for prediction of hematoma expansion in intracerebral hemorrhage in the basal ganglia.
    Huang Y; Zhang Q; Yang M
    Biosci Trends; 2018; 12(2):193-200. PubMed ID: 29760358
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Machine Learning-Based Perihematomal Tissue Features to Predict Clinical Outcome after Spontaneous Intracerebral Hemorrhage.
    Qi X; Hu G; Sun H; Chen Z; Yang C
    J Stroke Cerebrovasc Dis; 2022 Jun; 31(6):106475. PubMed ID: 35417846
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion.
    Xu W; Ding Z; Shan Y; Chen W; Feng Z; Pang P; Shen Q
    Front Neurosci; 2020; 14():491. PubMed ID: 32581674
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Establishment and evaluation of a nomogram model for predicting hematoma expansion in hypertensive intracerebral hemorrhage based on clinical factors and plain CT scan signs.
    Yu F; Yang Y; He Y; Liu J; Liu H; Liu H
    Ann Palliat Med; 2021 Dec; 10(12):12789-12800. PubMed ID: 35016444
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Feasibility of a combined swirl and blending sign on non-contrast computed tomography for predicting early hematoma expansion after spontaneous intracerebral hemorrhage.
    Kim JH; Choi JI
    J Neurosurg Sci; 2022 Dec; 66(6):582-588. PubMed ID: 33870668
    [TBL] [Abstract][Full Text] [Related]  

  • 31. A radiomics model based on aortic computed tomography angiography: the impact on predicting the prognosis of patients with aortic intramural hematoma (IMH).
    Ding Y; Zhang C; Wu W; Pu J; Zhao X; Zhang H; Zhao L; Schoenhagen P; Liu S; Ma X
    Quant Imaging Med Surg; 2023 Feb; 13(2):598-609. PubMed ID: 36819258
    [TBL] [Abstract][Full Text] [Related]  

  • 32. CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma.
    Chen X; Wang H; Huang K; Liu H; Ding H; Zhang L; Zhang T; Yu W; He L
    Front Oncol; 2021; 11():687884. PubMed ID: 34109133
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Are computed-tomography-based hematoma radiomics features reproducible and predictive of intracerebral hemorrhage expansion? an
    Chen K; Deng L; Li Q; Luo L
    Br J Radiol; 2021 May; 94(1121):20200724. PubMed ID: 33835831
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.
    Dong W; Xiong S; Lei P; Wang X; Liu H; Liu Y; Zou H; Fan B; Qiu Y
    Front Oncol; 2022; 12():944005. PubMed ID: 36081562
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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]  

  • 36. Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With
    Tang X; Li Y; Yan WF; Qian WL; Pang T; Gong YL; Yang ZG
    Front Oncol; 2021; 11():719919. PubMed ID: 34660285
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis.
    Jiang YW; Xu XJ; Wang R; Chen CM
    Front Oncol; 2022; 12():973104. PubMed ID: 36703784
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging.
    Yin Y; Yakar D; Dierckx RAJO; Mouridsen KB; Kwee TC; de Haas RJ
    Diagnostics (Basel); 2022 Feb; 12(2):. PubMed ID: 35204639
    [No Abstract]   [Full Text] [Related]  

  • 39. Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network.
    Wu F; Wang P; Yang H; Wu J; Liu Y; Yang Y; Zuo Z; Wu T; Li J
    Postgrad Med J; 2024 Mar; ():. PubMed ID: 38507237
    [TBL] [Abstract][Full Text] [Related]  

  • 40. The Role of Machine Learning and Radiomics for Treatment Response Prediction in Idiopathic Normal Pressure Hydrocephalus.
    Sotoudeh H; Sadaatpour Z; Rezaei A; Shafaat O; Sotoudeh E; Tabatabaie M; Singhal A; Tanwar M
    Cureus; 2021 Oct; 13(10):e18497. PubMed ID: 34754658
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.