146 related articles for article (PubMed ID: 37735892)
1. 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]
2. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.
Abbaspour S; Barahman M; Abdollahi H; Arabalibeik H; Hajainfar G; Babaei M; Iraji H; Barzegartahamtan M; Ay MR; Mahdavi SR
Biomed Phys Eng Express; 2023 Dec; 10(1):. PubMed ID: 37995359
[No Abstract] [Full Text] [Related]
3. Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears.
Cheng Q; Lin H; Zhao J; Lu X; Wang Q
J Orthop Surg Res; 2024 Jan; 19(1):99. PubMed ID: 38297322
[TBL] [Abstract][Full Text] [Related]
4. Establishment of a Prediction Model Based on Preoperative MRI Radiomics for Diffuse Astrocytic Glioma, IDH-Wildtype, with Molecular Features of Glioblastoma.
Du P; Wu X; Liu X; Chen J; Cao A; Geng D
Cancers (Basel); 2023 Oct; 15(20):. PubMed ID: 37894461
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Development of a nomogram based on radiomics and semantic features for predicting chromosome 7 gain/chromosome 10 loss in IDH wild-type histologically low-grade gliomas.
Kong X; Mao Y; Xi F; Li Y; Luo Y; Ma J
Front Oncol; 2023; 13():1196614. PubMed ID: 37781185
[TBL] [Abstract][Full Text] [Related]
7. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
Hashido T; Saito S; Ishida T
J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):606-613. PubMed ID: 34270479
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.
Granata V; Fusco R; Brunese MC; Di Mauro A; Avallone A; Ottaiano A; Izzo F; Normanno N; Petrillo A
Radiol Med; 2024 Mar; 129(3):420-428. PubMed ID: 38308061
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. [Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model].
He H; Guo E; Meng W; Wang Y; Wang W; He W; Wu Y; Yang W
Nan Fang Yi Ke Da Xue Xue Bao; 2024 Jan; 44(1):194-200. PubMed ID: 38293992
[TBL] [Abstract][Full Text] [Related]
14. Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.
Ni J; Zhang H; Yang Q; Fan X; Xu J; Sun J; Zhang J; Hu Y; Xiao Z; Zhao Y; Zhu H; Shi X; Feng W; Wang J; Wan C; Zhang X; Liu Y; You Y; Yu Y
Acad Radiol; 2024 Mar; ():. PubMed ID: 38458887
[TBL] [Abstract][Full Text] [Related]
15. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.
Adamidi ES; Mitsis K; Nikita KS
Comput Struct Biotechnol J; 2021; 19():2833-2850. PubMed ID: 34025952
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. [Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics].
Lu M; Qu Y; Ma A; Zhu J; Zou X; Lin G; Li Y; Liu X; Wen Z
Nan Fang Yi Ke Da Xue Xue Bao; 2023 Jun; 43(6):1023-1028. PubMed ID: 37439176
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.
Wang J; Chen J; Zhou R; Gao Y; Li J
BMC Cancer; 2022 Apr; 22(1):420. PubMed ID: 35439946
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]