145 related articles for article (PubMed ID: 37320871)
21. CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study.
Jiang Z; Dong Y; Yang L; Lv Y; Dong S; Yuan S; Li D; Liu L
J Digit Imaging; 2021 Oct; 34(5):1073-1085. PubMed ID: 34327623
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.
Zhang X; Dong X; Saripan MIB; Du D; Wu Y; Wang Z; Cao Z; Wen D; Liu Y; Marhaban MH
Thorac Cancer; 2023 Jul; 14(19):1802-1811. PubMed ID: 37183577
[TBL] [Abstract][Full Text] [Related]
24. PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features.
Huang W; Wang J; Wang H; Zhang Y; Zhao F; Li K; Su L; Kang F; Cao X
Front Pharmacol; 2022; 13():898529. PubMed ID: 35571081
[No Abstract] [Full Text] [Related]
25. Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.
Sha X; Gong G; Qiu Q; Duan J; Li D; Yin Y
BMC Med Imaging; 2020 Feb; 20(1):12. PubMed ID: 32024469
[TBL] [Abstract][Full Text] [Related]
26. 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]
27. Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data.
Liu N; Liang BL; Lu L; Zhang BQ; Sun JJ; Yang JT; Xu J; Song ZB; Shi L
Neoplasma; 2023 Apr; 70(2):300-310. PubMed ID: 36812231
[TBL] [Abstract][Full Text] [Related]
28. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma.
Chang C; Sun X; Wang G; Yu H; Zhao W; Ge Y; Duan S; Qian X; Wang R; Lei B; Wang L; Liu L; Ruan M; Yan H; Liu C; Chen J; Xie W
Front Oncol; 2021; 11():603882. PubMed ID: 33738250
[TBL] [Abstract][Full Text] [Related]
29. Predicting EGFR mutation subtypes in lung adenocarcinoma using
Liu Q; Sun D; Li N; Kim J; Feng D; Huang G; Wang L; Song S
Transl Lung Cancer Res; 2020 Jun; 9(3):549-562. PubMed ID: 32676319
[TBL] [Abstract][Full Text] [Related]
30.
Li P; Wang X; Xu C; Liu C; Zheng C; Fulham MJ; Feng D; Wang L; Song S; Huang G
Eur J Nucl Med Mol Imaging; 2020 May; 47(5):1116-1126. PubMed ID: 31982990
[TBL] [Abstract][Full Text] [Related]
31. Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer.
Huang X; Sun Y; Tan M; Ma W; Gao P; Qi L; Lu J; Yang Y; Wang K; Chen W; Jin L; Kuang K; Duan S; Li M
Front Oncol; 2022; 12():772770. PubMed ID: 35186727
[TBL] [Abstract][Full Text] [Related]
32. A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.
Hu Z; Yang Z; Lafata KJ; Yin FF; Wang C
Med Phys; 2022 May; 49(5):3213-3222. PubMed ID: 35263458
[TBL] [Abstract][Full Text] [Related]
33. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.
Jiang M; Li CL; Luo XM; Chuan ZR; Lv WZ; Li X; Cui XW; Dietrich CF
Eur J Cancer; 2021 Apr; 147():95-105. PubMed ID: 33639324
[TBL] [Abstract][Full Text] [Related]
34. Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT.
Huang X; Mai J; Huang Y; He L; Chen X; Wu X; Li Y; Yang X; Dong M; Huang J; Zhang F; Liang C; Liu Z
Clin Breast Cancer; 2021 Aug; 21(4):e388-e401. PubMed ID: 33451965
[TBL] [Abstract][Full Text] [Related]
35. Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images.
Hao P; Deng BY; Huang CT; Xu J; Zhou F; Liu ZX; Zhou W; Xu YK
Front Oncol; 2022; 12():994285. PubMed ID: 36338735
[TBL] [Abstract][Full Text] [Related]
36. Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.
Coroller TP; Agrawal V; Huynh E; Narayan V; Lee SW; Mak RH; Aerts HJWL
J Thorac Oncol; 2017 Mar; 12(3):467-476. PubMed ID: 27903462
[TBL] [Abstract][Full Text] [Related]
37. Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer.
Tang X; Huang H; Du P; Wang L; Yin H; Xu X
J Cancer Res Clin Oncol; 2022 Sep; 148(9):2247-2260. PubMed ID: 35430688
[TBL] [Abstract][Full Text] [Related]
38. Development and Validation of a
Ouyang ML; Wang YR; Deng QS; Zhu YF; Zhao ZH; Wang L; Wang LX; Tang K
Front Oncol; 2021; 11():710909. PubMed ID: 34568038
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients.
Song L; Zhu Z; Mao L; Li X; Han W; Du H; Wu H; Song W; Jin Z
Front Oncol; 2020; 10():369. PubMed ID: 32266148
[No Abstract] [Full Text] [Related]
[Previous] [Next] [New Search]