187 related articles for article (PubMed ID: 37644397)
1. Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis.
Lu XF; Zhu TY
BMC Med Imaging; 2023 Aug; 23(1):115. PubMed ID: 37644397
[TBL] [Abstract][Full Text] [Related]
2. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.
Chen X; Feng B; Xu K; Chen Y; Duan X; Jin Z; Li K; Li R; Long W; Liu X
Eur Radiol; 2023 Oct; 33(10):6804-6816. PubMed ID: 37148352
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis.
Zhang H; Lei H; Pang J
Front Oncol; 2022; 12():975183. PubMed ID: 36119492
[TBL] [Abstract][Full Text] [Related]
5. A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography.
Chen X; Feng B; Li C; Duan X; Chen Y; Li Z; Liu Z; Zhang C; Long W
Oncol Rep; 2020 Apr; 43(4):1256-1266. PubMed ID: 32323834
[TBL] [Abstract][Full Text] [Related]
6. Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study.
Zhou H; Bai HX; Jiao Z; Cui B; Wu J; Zheng H; Yang H; Liao W
Eur J Radiol; 2023 Nov; 168():111136. PubMed ID: 37832194
[TBL] [Abstract][Full Text] [Related]
7. MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis.
Tabnak P; HajiEsmailPoor Z; Baradaran B; Pashazadeh F; Aghebati Maleki L
Acad Radiol; 2024 Mar; 31(3):763-787. PubMed ID: 37925343
[TBL] [Abstract][Full Text] [Related]
8. Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours.
Hu J; Zhao Y; Li M; Liu Y; Wang F; Weng Q; You R; Cao D
Eur J Radiol; 2020 May; 126():108929. PubMed ID: 32169748
[TBL] [Abstract][Full Text] [Related]
9. MRI Radiomics Analysis for Predicting the Pathologic Classification and TNM Staging of Thymic Epithelial Tumors: A Pilot Study.
Xiao G; Rong WC; Hu YC; Shi ZQ; Yang Y; Ren JL; Cui GB
AJR Am J Roentgenol; 2020 Feb; 214(2):328-340. PubMed ID: 31799873
[No Abstract] [Full Text] [Related]
10. Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes.
Zhao W; Ozawa Y; Hara M; Okuda K; Hiwatashi A
Jpn J Radiol; 2024 Apr; 42(4):367-373. PubMed ID: 38010596
[TBL] [Abstract][Full Text] [Related]
11. Quantitative CT parameters combined with preoperative systemic inflammatory markers for differentiating risk subgroups of thymic epithelial tumors.
Gao R; Zhou J; Zhang J; Zhu J; Wang T; Yan C
BMC Cancer; 2023 Nov; 23(1):1158. PubMed ID: 38012604
[TBL] [Abstract][Full Text] [Related]
12. Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors.
Yu C; Li T; Yang X; Zhang R; Xin L; Zhao Z; Cui J
BMC Med Imaging; 2022 Mar; 22(1):37. PubMed ID: 35249531
[TBL] [Abstract][Full Text] [Related]
13. Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors.
Liufu Y; Wen Y; Wu W; Su R; Liu S; Li J; Pan X; Chen K; Guan Y
J Comput Assist Tomogr; 2023 Mar-Apr 01; 47(2):220-228. PubMed ID: 36877755
[TBL] [Abstract][Full Text] [Related]
14. CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors.
Liu J; Yin P; Wang S; Liu T; Sun C; Hong N
Front Oncol; 2021; 11():628534. PubMed ID: 33718203
[TBL] [Abstract][Full Text] [Related]
15. Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis.
Ponsiglione A; Gambardella M; Stanzione A; Green R; Cantoni V; Nappi C; Crocetto F; Cuocolo R; Cuocolo A; Imbriaco M
Eur Radiol; 2024 Jun; 34(6):3981-3991. PubMed ID: 37955670
[TBL] [Abstract][Full Text] [Related]
16. The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis.
Liang G; Yu W; Liu S; Zhang M; Xie M; Liu M; Liu W
Front Oncol; 2022; 12():960944. PubMed ID: 36798691
[TBL] [Abstract][Full Text] [Related]
17. A meta-analysis of based radiomics for predicting lymph node metastasis in patients with biliary tract cancers.
Ma Y; Lin Y; Lu J; He Y; Shi Q; Liu H; Li J; Zhang B; Zhang J; Zhang Y; Yue P; Meng W; Li X
Front Surg; 2022; 9():1045295. PubMed ID: 36684162
[TBL] [Abstract][Full Text] [Related]
18. Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis.
Deng L; Shuai P; Liu Y; Yong T; Liu Y; Li H; Zheng X
Osteoporos Int; 2024 May; ():. PubMed ID: 38802557
[TBL] [Abstract][Full Text] [Related]
19. CT Radiomic Features for Predicting Resectability and TNM Staging in Thymic Epithelial Tumors.
Araujo-Filho JAB; Mayoral M; Zheng J; Tan KS; Gibbs P; Shepherd AF; Rimner A; Simone CB; Riely G; Huang J; Ginsberg MS
Ann Thorac Surg; 2022 Mar; 113(3):957-965. PubMed ID: 33844992
[TBL] [Abstract][Full Text] [Related]
20. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.
Jia LL; Zheng QY; Tian JH; He DL; Zhao JX; Zhao LP; Huang G
Front Oncol; 2022; 12():1026216. PubMed ID: 36313696
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]