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 *

198 related articles for article (PubMed ID: 34754658)

  • 1. 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]  

  • 2. Utility of MRI-based disproportionately enlarged subarachnoid space hydrocephalus scoring for predicting prognosis after surgery for idiopathic normal pressure hydrocephalus: clinical research.
    Shinoda N; Hirai O; Hori S; Mikami K; Bando T; Shimo D; Kuroyama T; Kuramoto Y; Matsumoto M; Ueno Y
    J Neurosurg; 2017 Dec; 127(6):1436-1442. PubMed ID: 28156249
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. One-year outcome in patients with idiopathic normal-pressure hydrocephalus: comparison of lumboperitoneal shunt to ventriculoperitoneal shunt.
    Miyajima M; Kazui H; Mori E; Ishikawa M;
    J Neurosurg; 2016 Dec; 125(6):1483-1492. PubMed ID: 26871203
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Disability risk or unimproved symptoms following shunt surgery in patients with idiopathic normal-pressure hydrocephalus: post hoc analysis of SINPHONI-2.
    Yamada S; Kimura T; Jingami N; Atsuchi M; Hirai O; Tokuda T; Miyajima M; Kazui H; Mori E; Ishikawa M;
    J Neurosurg; 2017 Jun; 126(6):2002-2009. PubMed ID: 27419822
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
    Wang X; Wan Q; Chen H; Li Y; Li X
    Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning.
    Xu X; Zhang J; Yang K; Wang Q; Chen X; Xu B
    Brain Behav; 2021 May; 11(5):e02085. PubMed ID: 33624945
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.
    Liu J; Zeng P; Guo W; Wang C; Geng Y; Lang N; Yuan H
    J Magn Reson Imaging; 2021 Oct; 54(4):1303-1311. PubMed ID: 33979466
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine Learning Assisted MRI Characterization for Diagnosis of Neonatal Acute Bilirubin Encephalopathy.
    Liu Z; Ji B; Zhang Y; Cui G; Liu L; Man S; Ding L; Yang X; Mao H; Wang L
    Front Neurol; 2019; 10():1018. PubMed ID: 31632332
    [No Abstract]   [Full Text] [Related]  

  • 10. Utilization of radiomics to predict long-term outcome of magnetic resonance-guided focused ultrasound ablation therapy in adenomyosis.
    Li Z; Zhang J; Song Y; Yin X; Chen A; Tang N; Prince MR; Yang G; Wang H
    Eur Radiol; 2021 Jan; 31(1):392-402. PubMed ID: 32725335
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
    Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
    EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.
    Hectors SJ; Chen C; Chen J; Wang J; Gordon S; Yu M; Al Hussein Al Awamlh B; Sabuncu MR; Margolis DJA; Hu JC
    J Magn Reson Imaging; 2021 Nov; 54(5):1466-1473. PubMed ID: 33970516
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Artificial Intelligence for Prediction of Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review.
    Fernandes RT; Fernandes FW; Kundu M; Ramsay DSC; Salih A; Namireddy SN; Jankovic D; Kalasauskas D; Ottenhausen M; Kramer A; Ringel F; Thavarajasingam SG
    World Neurosurg; 2024 Dec; 192():e281-e291. PubMed ID: 39313190
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Cortical atrophy distinguishes idiopathic normal-pressure hydrocephalus from progressive supranuclear palsy: A machine learning approach.
    Bianco MG; Quattrone A; Sarica A; Vescio B; Buonocore J; Vaccaro MG; Aracri F; Calomino C; Gramigna V; Quattrone A
    Parkinsonism Relat Disord; 2022 Oct; 103():7-14. PubMed ID: 35988437
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Non-contrast CT radiomics and machine learning for outcomes prediction of patients with acute ischemic stroke receiving conventional treatment.
    Zhang L; Wu J; Yu R; Xu R; Yang J; Fan Q; Wang D; Zhang W
    Eur J Radiol; 2023 Aug; 165():110959. PubMed ID: 37437435
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs.
    Gao Y; Kalbasi A; Hsu W; Ruan D; Fu J; Shao J; Cao M; Wang C; Eilber FC; Bernthal N; Bukata S; Dry SM; Nelson SD; Kamrava M; Lewis J; Low DA; Steinberg M; Hu P; Yang Y
    Phys Med Biol; 2020 Aug; 65(17):175006. PubMed ID: 32554891
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI.
    Zhang Y; Zhu Y; Shi X; Tao J; Cui J; Dai Y; Zheng M; Wang S
    Acad Radiol; 2019 Sep; 26(9):1262-1268. PubMed ID: 30377057
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.
    Hamerla G; Meyer HJ; Schob S; Ginat DT; Altman A; Lim T; Gihr GA; Horvath-Rizea D; Hoffmann KT; Surov A
    Magn Reson Imaging; 2019 Nov; 63():244-249. PubMed ID: 31425811
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
    Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
    Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Default mode network connectivity in patients with idiopathic normal pressure hydrocephalus.
    Khoo HM; Kishima H; Tani N; Oshino S; Maruo T; Hosomi K; Yanagisawa T; Kazui H; Watanabe Y; Shimokawa T; Aso T; Kawaguchi A; Yamashita F; Saitoh Y; Yoshimine T
    J Neurosurg; 2016 Feb; 124(2):350-8. PubMed ID: 26295919
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.