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 *

217 related articles for article (PubMed ID: 34460921)

  • 21. Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas.
    Zhang J; Sun J; Han T; Zhao Z; Cao Y; Zhang G; Zhou J
    Eur J Radiol; 2020 Nov; 132():109287. PubMed ID: 32980725
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.
    Wang X; Li J; Sun J; Liu W; Cai L; Zhao P; Yang Z; Lv H; Wang Z
    Biomed Res Int; 2022; 2022():8955227. PubMed ID: 36132071
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. Progesterone Receptor Is Responsible for Benign Biology of Skull Base Meningioma.
    Kuroi Y; Matsumoto K; Shibuya M; Kasuya H
    World Neurosurg; 2018 Oct; 118():e918-e924. PubMed ID: 30031954
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Changes in radiomic and radiologic features in meningiomas after radiation therapy.
    Jo SW; Kim ES; Yoon DY; Kwon MJ
    BMC Med Imaging; 2023 Oct; 23(1):164. PubMed ID: 37858048
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma.
    Li Q; Liu YJ; Dong D; Bai X; Huang QB; Guo AT; Ye HY; Tian J; Wang HY
    J Magn Reson Imaging; 2020 Nov; 52(5):1557-1566. PubMed ID: 32462799
    [TBL] [Abstract][Full Text] [Related]  

  • 28. 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]  

  • 29. Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer.
    Song SE; Cho KR; Cho Y; Kim K; Jung SP; Seo BK; Woo OH
    Eur Radiol; 2022 Feb; 32(2):853-863. PubMed ID: 34383145
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma.
    Li L; Wang K; Ma X; Liu Z; Wang S; Du J; Tian K; Zhou X; Wei W; Sun K; Lin Y; Wu Z; Tian J
    Eur J Radiol; 2019 Sep; 118():81-87. PubMed ID: 31439263
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.
    Laukamp KR; Shakirin G; Baeßler B; Thiele F; Zopfs D; Große Hokamp N; Timmer M; Kabbasch C; Perkuhn M; Borggrefe J
    World Neurosurg; 2019 Dec; 132():e366-e390. PubMed ID: 31476455
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade.
    Duan CF; Li N; Li Y; Liu F; Wang JC; Liu XJ; Xu WJ
    Clin Radiol; 2022 Apr; 77(4):e302-e307. PubMed ID: 35168757
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.
    Bernatz S; Ackermann J; Mandel P; Kaltenbach B; Zhdanovich Y; Harter PN; Döring C; Hammerstingl R; Bodelle B; Smith K; Bucher A; Albrecht M; Rosbach N; Basten L; Yel I; Wenzel M; Bankov K; Koch I; Chun FK; Köllermann J; Wild PJ; Vogl TJ
    Eur Radiol; 2020 Dec; 30(12):6757-6769. PubMed ID: 32676784
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery.
    Chidambaram S; Pannullo SC; Roytman M; Pisapia DJ; Liechty B; Magge RS; Ramakrishna R; Stieg PE; Schwartz TH; Ivanidze J
    Neurosurg Focus; 2019 Jun; 46(6):E10. PubMed ID: 31153141
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.
    Park JH; Quang LT; Yoon W; Baek BH; Park I; Kim SK
    Biomedicines; 2023 Dec; 11(12):. PubMed ID: 38137489
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Ultrarapid Evaluation of Meningioma Malignancy by Intraoperative Flow Cytometry.
    Matsuoka G; Eguchi S; Anami H; Ishikawa T; Yamaguchi K; Nitta M; Muragaki Y; Kawamata T
    World Neurosurg; 2018 Dec; 120():320-327. PubMed ID: 30144616
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study.
    Wang H; Zhang J; Bao S; Liu J; Hou F; Huang Y; Chen H; Duan S; Hao D; Liu J
    J Magn Reson Imaging; 2020 Sep; 52(3):873-882. PubMed ID: 32112598
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.
    Wang H; Song B; Ye N; Ren J; Sun X; Dai Z; Zhang Y; Chen BT
    Eur J Radiol; 2020 Jan; 122():108755. PubMed ID: 31783344
    [TBL] [Abstract][Full Text] [Related]  

  • 39. A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.
    Yang L; Xu P; Zhang Y; Cui N; Wang M; Peng M; Gao C; Wang T
    Neuroradiology; 2022 Jul; 64(7):1373-1382. PubMed ID: 35037985
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?
    Yu H; Wen X; Wu P; Chen Y; Zou T; Wang X; Jiang S; Zhou J; Wen Z
    Eur Radiol; 2019 Oct; 29(10):5298-5306. PubMed ID: 30887206
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 11.