183 related articles for article (PubMed ID: 38762472)
1. Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.
Wu T; Gao C; Lou X; Wu J; Xu M; Wu L
BMC Pulm Med; 2024 May; 24(1):246. PubMed ID: 38762472
[TBL] [Abstract][Full Text] [Related]
2. Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis.
Castellana R; Fanni SC; Roncella C; Romei C; Natrella M; Neri E
Eur J Radiol; 2024 Jul; 176():111510. PubMed ID: 38781919
[TBL] [Abstract][Full Text] [Related]
3. A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.
Xie H; Song C; Jian L; Guo Y; Li M; Luo J; Li Q; Tan T
BMC Med Imaging; 2024 May; 24(1):121. PubMed ID: 38789936
[TBL] [Abstract][Full Text] [Related]
4. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer.
Li L; Zhang J; Zhe X; Tang M; Zhang X; Lei X; Zhang L
Eur J Radiol; 2022 Jun; 151():110243. PubMed ID: 35366583
[TBL] [Abstract][Full Text] [Related]
5. Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis.
HajiEsmailPoor Z; Kargar Z; Tabnak P
Eur J Radiol; 2023 Nov; 168():111129. PubMed ID: 37820522
[TBL] [Abstract][Full Text] [Related]
6. Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study.
Deng Z; Liu X; Wu R; Yan H; Gou L; Hu W; Wan J; Song C; Chen J; Ma D; Zhou H; Tian D
BMC Cancer; 2024 Apr; 24(1):536. PubMed ID: 38678211
[TBL] [Abstract][Full Text] [Related]
7. Integrative nomogram of intratumoral, peritumoral, and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas.
Das SK; Fang KW; Xu L; Li B; Zhang X; Yang HF
Sci Rep; 2021 May; 11(1):10829. PubMed ID: 34031529
[TBL] [Abstract][Full Text] [Related]
8. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?
Wang X; Zhao X; Li Q; Xia W; Peng Z; Zhang R; Li Q; Jian J; Wang W; Tang Y; Liu S; Gao X
Eur Radiol; 2019 Nov; 29(11):6049-6058. PubMed ID: 30887209
[TBL] [Abstract][Full Text] [Related]
9. Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.
Ma D; Zhou T; Chen J; Chen J
BMC Med Imaging; 2024 Jun; 24(1):144. PubMed ID: 38867143
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer.
Zheng X; Shao J; Zhou L; Wang L; Ge Y; Wang G; Feng F
Ther Innov Regul Sci; 2022 Jan; 56(1):155-167. PubMed ID: 34699046
[TBL] [Abstract][Full Text] [Related]
12. A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.
Xing X; Li L; Sun M; Zhu X; Feng Y
Ther Adv Respir Dis; 2024; 18():17534666241249168. PubMed ID: 38757628
[TBL] [Abstract][Full Text] [Related]
13. Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas.
Liu Y; Kim J; Balagurunathan Y; Hawkins S; Stringfield O; Schabath MB; Li Q; Qu F; Liu S; Garcia AL; Ye Z; Gillies RJ
Med Phys; 2018 Jun; 45(6):2518-2526. PubMed ID: 29624702
[TBL] [Abstract][Full Text] [Related]
14. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.
Ma X; Xia L; Chen J; Wan W; Zhou W
Eur Radiol; 2023 Mar; 33(3):1949-1962. PubMed ID: 36169691
[TBL] [Abstract][Full Text] [Related]
15. Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.
Zhang W; Wang S; Dong Q; Chen W; Wang P; Zhu G; Chen X; Cai Y
PeerJ; 2024; 12():e17111. PubMed ID: 38525272
[TBL] [Abstract][Full Text] [Related]
16. Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer.
Cong M; Feng H; Ren JL; Xu Q; Cong L; Hou Z; Wang YY; Shi G
Lung Cancer; 2020 Jan; 139():73-79. PubMed ID: 31743889
[TBL] [Abstract][Full Text] [Related]
17. Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma.
Zhong Y; Yuan M; Zhang T; Zhang YD; Li H; Yu TF
AJR Am J Roentgenol; 2018 Jul; 211(1):109-113. PubMed ID: 29667885
[TBL] [Abstract][Full Text] [Related]
18. Using ultrasound features and radiomics analysis to predict lymph node metastasis in patients with thyroid cancer.
Li F; Pan D; He Y; Wu Y; Peng J; Li J; Wang Y; Yang H; Chen J
BMC Surg; 2020 Dec; 20(1):315. PubMed ID: 33276765
[TBL] [Abstract][Full Text] [Related]
19. Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer.
Wang X; Li C; Fang M; Zhang L; Zhong L; Dong D; Tian J; Shan X
BMC Med Imaging; 2021 Mar; 21(1):58. PubMed ID: 33757460
[TBL] [Abstract][Full Text] [Related]
20. Diagnostic performance of CT scan-based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis.
HajiEsmailPoor Z; Tabnak P; Baradaran B; Pashazadeh F; Aghebati-Maleki L
Front Oncol; 2023; 13():1185663. PubMed ID: 37936604
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]