221 related articles for article (PubMed ID: 31750250)
1. Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma.
Fan Y; Chen C; Zhao F; Tian Z; Wang J; Ma X; Xu J
Front Oncol; 2019; 9():1164. PubMed ID: 31750250
[No Abstract] [Full Text] [Related]
2. Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach.
Zhang Y; Chen C; Cheng Y; Teng Y; Guo W; Xu H; Ou X; Wang J; Li H; Ma X; Xu J
Front Oncol; 2019; 9():1371. PubMed ID: 31921635
[No Abstract] [Full Text] [Related]
3. Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.
Chen C; Zheng A; Ou X; Wang J; Ma X
Front Oncol; 2020; 10():1151. PubMed ID: 33042784
[No Abstract] [Full Text] [Related]
4. The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.
Teng Y; Chen C; Zhang Y; Xu J
Transl Cancer Res; 2022 Nov; 11(11):4079-4088. PubMed ID: 36523299
[TBL] [Abstract][Full Text] [Related]
5. Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques.
Chen B; Chen C; Wang J; Teng Y; Ma X; Xu J
Front Oncol; 2021; 11():521313. PubMed ID: 34141605
[TBL] [Abstract][Full Text] [Related]
6. The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study.
Chen C; Guo X; Wang J; Guo W; Ma X; Xu J
Front Oncol; 2019; 9():1338. PubMed ID: 31867272
[No Abstract] [Full Text] [Related]
7. Glioblastoma and Anaplastic Astrocytoma: Differentiation Using MRI Texture Analysis.
Tian Z; Chen C; Fan Y; Ou X; Wang J; Ma X; Xu J
Front Oncol; 2019; 9():876. PubMed ID: 31552189
[No Abstract] [Full Text] [Related]
8. Use of radiomics based on
Zhou Y; Ma XL; Zhang T; Wang J; Zhang T; Tian R
Eur J Nucl Med Mol Imaging; 2021 Aug; 48(9):2904-2913. PubMed ID: 33547553
[TBL] [Abstract][Full Text] [Related]
9. Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors.
Chen C; Ou X; Wang J; Guo W; Ma X
Front Oncol; 2019; 9():806. PubMed ID: 31508366
[No Abstract] [Full Text] [Related]
10. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.
Dai H; Lu M; Huang B; Tang M; Pang T; Liao B; Cai H; Huang M; Zhou Y; Chen X; Ding H; Feng ST
Quant Imaging Med Surg; 2021 May; 11(5):1836-1853. PubMed ID: 33936969
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification.
Kong X; Mao Y; Luo Y; Xi F; Li Y; Ma J
Acta Radiol; 2023 Nov; 64(11):2938-2947. PubMed ID: 37735892
[TBL] [Abstract][Full Text] [Related]
13. Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.
Qian Z; Zhang L; Hu J; Chen S; Chen H; Shen H; Zheng F; Zang Y; Chen X
Front Oncol; 2021; 11():699789. PubMed ID: 34490097
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.
Yu Q; Wang A; Gu J; Li Q; Ning Y; Peng J; Lv F; Zhang X
Front Oncol; 2022; 12():913898. PubMed ID: 35847942
[TBL] [Abstract][Full Text] [Related]
16. Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion.
Wan Q; Zhou J; Xia X; Hu J; Wang P; Peng Y; Zhang T; Sun J; Song Y; Yang G; Li X
Front Oncol; 2021; 11():683587. PubMed ID: 34868905
[TBL] [Abstract][Full Text] [Related]
17. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.
Qian Z; Li Y; Wang Y; Li L; Li R; Wang K; Li S; Tang K; Zhang C; Fan X; Chen B; Li W
Cancer Lett; 2019 Jun; 451():128-135. PubMed ID: 30878526
[TBL] [Abstract][Full Text] [Related]
18. Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases.
Shen S; Li C; Fan Y; Lu S; Yan Z; Liu H; Zhou H; Zhang Z
Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2024 Jan; 49(1):58-67. PubMed ID: 38615167
[TBL] [Abstract][Full Text] [Related]
19. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in
Zhang Y; Cheng C; Liu Z; Wang L; Pan G; Sun G; Chang Y; Zuo C; Yang X
Med Phys; 2019 Oct; 46(10):4520-4530. PubMed ID: 31348535
[TBL] [Abstract][Full Text] [Related]
20. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]