235 related articles for article (PubMed ID: 32086583)
21. Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.
Zuo Z; Zeng W; Peng K; Mao Y; Wu Y; Zhou Y; Qi W
Clin Radiol; 2023 Oct; 78(10):e698-e706. PubMed ID: 37487842
[TBL] [Abstract][Full Text] [Related]
22. Solid component ratio influences prognosis of GGO-featured IA stage invasive lung adenocarcinoma.
Sun F; Huang Y; Yang X; Zhan C; Xi J; Lin Z; Shi Y; Jiang W; Wang Q
Cancer Imaging; 2020 Dec; 20(1):87. PubMed ID: 33308323
[TBL] [Abstract][Full Text] [Related]
23. Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.
Yang X; Dong X; Wang J; Li W; Gu Z; Gao D; Zhong N; Guan Y
Oncologist; 2019 Nov; 24(11):e1156-e1164. PubMed ID: 30936378
[TBL] [Abstract][Full Text] [Related]
24. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
Sun Y; Li C; Jin L; Gao P; Zhao W; Ma W; Tan M; Wu W; Duan S; Shan Y; Li M
Eur Radiol; 2020 Jul; 30(7):3650-3659. PubMed ID: 32162003
[TBL] [Abstract][Full Text] [Related]
25. Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT.
Cui X; Zheng S; Zhang W; Fan S; Wang J; Song F; Liu X; Zhu W; Ye Z
Eur Radiol; 2023 Jul; 33(7):4734-4745. PubMed ID: 36723725
[TBL] [Abstract][Full Text] [Related]
26. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
Wu G; Woodruff HC; Sanduleanu S; Refaee T; Jochems A; Leijenaar R; Gietema H; Shen J; Wang R; Xiong J; Bian J; Wu J; Lambin P
Eur Radiol; 2020 May; 30(5):2680-2691. PubMed ID: 32006165
[TBL] [Abstract][Full Text] [Related]
27. Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.
Pan Z; Hu G; Zhu Z; Tan W; Han W; Zhou Z; Song W; Yu Y; Song L; Jin Z
Radiology; 2024 Apr; 311(1):e232057. PubMed ID: 38591974
[TBL] [Abstract][Full Text] [Related]
28. Computed Tomography-based Prognostication in Lung Adenocarcinomas through Histopathological Feature Learning: A Retrospective Multicenter Study.
Lee KH; Lee JH; Park S; Jeon YK; Chung DH; Kim YT; Goo JM; Kim H
Ann Am Thorac Soc; 2023 Jul; 20(7):1020-1028. PubMed ID: 37075305
[No Abstract] [Full Text] [Related]
29. Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.
Sadhwani A; Chang HW; Behrooz A; Brown T; Auvigne-Flament I; Patel H; Findlater R; Velez V; Tan F; Tekiela K; Wulczyn E; Yi ES; Mermel CH; Hanks D; Chen PC; Kulig K; Batenchuk C; Steiner DF; Cimermancic P
Sci Rep; 2021 Aug; 11(1):16605. PubMed ID: 34400666
[TBL] [Abstract][Full Text] [Related]
30. Quantitative CT density histogram values and standardized uptake values of FDG-PET/CT with respiratory gating can distinguish solid adenocarcinomas from squamous cell carcinomas of the lung.
Tsubakimoto M; Yamashiro T; Tamashiro Y; Murayama S
Eur J Radiol; 2018 Mar; 100():108-115. PubMed ID: 29496067
[TBL] [Abstract][Full Text] [Related]
31. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules.
She Y; Zhang L; Zhu H; Dai C; Xie D; Xie H; Zhang W; Zhao L; Zou L; Fei K; Sun X; Chen C
Eur Radiol; 2018 Dec; 28(12):5121-5128. PubMed ID: 29869172
[TBL] [Abstract][Full Text] [Related]
32. Clinical implication of the 2020 International Association for the Study of Lung Cancer histologic grading in surgically resected pathologic stage 1 lung adenocarcinomas: Prognostic value and association with computed tomography characteristics.
Cho IS; Shim HS; Lee HJ; Suh YJ
Lung Cancer; 2023 Oct; 184():107345. PubMed ID: 37611496
[TBL] [Abstract][Full Text] [Related]
33. CT-defined Visceral Pleural Invasion in T1 Lung Adenocarcinoma: Lack of Relationship to Disease-Free Survival.
Kim H; Goo JM; Kim YT; Park CM
Radiology; 2019 Sep; 292(3):741-749. PubMed ID: 31361207
[TBL] [Abstract][Full Text] [Related]
34. Quantifying invasiveness of clinical stage IA lung adenocarcinoma with computed tomography texture features.
Qiu ZB; Zhang C; Chu XP; Cai FY; Yang XN; Wu YL; Zhong WZ
J Thorac Cardiovasc Surg; 2022 Mar; 163(3):805-815.e3. PubMed ID: 33541730
[TBL] [Abstract][Full Text] [Related]
35. Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule.
Fan L; Fang M; Li Z; Tu W; Wang S; Chen W; Tian J; Dong D; Liu S
Eur Radiol; 2019 Feb; 29(2):889-897. PubMed ID: 29967956
[TBL] [Abstract][Full Text] [Related]
36. Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.
Shaish H; Ahmed FS; Lederer D; D'Souza B; Armenta P; Salvatore M; Saqi A; Huang S; Jambawalikar S; Mutasa S
Ann Am Thorac Soc; 2021 Jan; 18(1):51-59. PubMed ID: 32857594
[No Abstract] [Full Text] [Related]
37. Role of CT and PET Imaging in Predicting Tumor Recurrence and Survival in Patients with Lung Adenocarcinoma: A Comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma.
Lee HY; Lee SW; Lee KS; Jeong JY; Choi JY; Kwon OJ; Song SH; Kim EY; Kim J; Shim YM
J Thorac Oncol; 2015 Dec; 10(12):1785-94. PubMed ID: 26473646
[TBL] [Abstract][Full Text] [Related]
38. Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography.
Wang C; Shao J; Lv J; Cao Y; Zhu C; Li J; Shen W; Shi L; Liu D; Li W
Transl Oncol; 2021 Aug; 14(8):101141. PubMed ID: 34087705
[TBL] [Abstract][Full Text] [Related]
39. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction.
Vaidya P; Bera K; Gupta A; Wang X; Corredor G; Fu P; Beig N; Prasanna P; Patil PD; Velu PD; Rajiah P; Gilkeson R; Feldman MD; Choi H; Velcheti V; Madabhushi A
Lancet Digit Health; 2020 Mar; 2(3):e116-e128. PubMed ID: 33334576
[TBL] [Abstract][Full Text] [Related]
40. Prognostic impact of nomogram based on whole tumour size, tumour disappearance ratio on CT and SUVmax on PET in lung adenocarcinoma.
Song SH; Ahn JH; Lee HY; Lee G; Choi JY; Kang J; Kim EY; Han J; Kwon OJ; Lee KS; Kim HK; Choi YS; Kim J; Shim YM
Eur Radiol; 2016 Jun; 26(6):1538-46. PubMed ID: 26455720
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
