231 related articles for article (PubMed ID: 32086583)
1. A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.
Lu L; Wang D; Wang L; E L; Guo P; Li Z; Xiang J; Yang H; Li H; Yin S; Schwartz LH; Xie C; Zhao B
Eur Radiol; 2020 Jul; 30(7):3614-3623. PubMed ID: 32086583
[TBL] [Abstract][Full Text] [Related]
2. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas.
Kim H; Goo JM; Lee KH; Kim YT; Park CM
Radiology; 2020 Jul; 296(1):216-224. PubMed ID: 32396042
[TBL] [Abstract][Full Text] [Related]
3. Use of a prognostic risk score that aggregates the FDG-PET/CT SUVmax, tumor size, and histologic group for predicting the prognosis of pStage I lung adenocarcinoma.
Kawakita N; Toba H; Kawakami Y; Takizawa H; Bando Y; Otuska H; Matsumoto D; Takashima M; Tsuboi M; Yoshida M; Kondo K; Tangoku A
Int J Clin Oncol; 2020 Jun; 25(6):1079-1089. PubMed ID: 32130543
[TBL] [Abstract][Full Text] [Related]
4. Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.
Cho HH; Lee G; Lee HY; Park H
Eur Radiol; 2020 May; 30(5):2984-2994. PubMed ID: 31965255
[TBL] [Abstract][Full Text] [Related]
5. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.
Wu YJ; Liu YC; Liao CY; Tang EK; Wu FZ
Sci Rep; 2021 Jan; 11(1):66. PubMed ID: 33462251
[TBL] [Abstract][Full Text] [Related]
6. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.
Zhong Y; Cai C; Chen T; Gui H; Chen C; Deng J; Yang M; Yu B; Song Y; Wang T; Chen Y; Shi H; Xie D; Chen C; She Y
Eur J Nucl Med Mol Imaging; 2024 Jan; 51(2):521-534. PubMed ID: 37725128
[TBL] [Abstract][Full Text] [Related]
7. Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.
Lee T; Lee KH; Lee JH; Park S; Kim YT; Goo JM; Kim H
Eur Radiol; 2024 May; 34(5):3431-3443. PubMed ID: 37861801
[TBL] [Abstract][Full Text] [Related]
8. Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics.
Choe J; Lee SM; Do KH; Kim S; Choi S; Lee JG; Seo JB
Eur Radiol; 2020 Sep; 30(9):4952-4963. PubMed ID: 32356158
[TBL] [Abstract][Full Text] [Related]
9. Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature.
Zhang X; Lu B; Yang X; Lan D; Lin S; Zhou Z; Li K; Deng D; Peng P; Zeng Z; Long L
Eur Radiol; 2023 Feb; 33(2):825-835. PubMed ID: 36166088
[TBL] [Abstract][Full Text] [Related]
10. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
[TBL] [Abstract][Full Text] [Related]
11. CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study.
Zuo Z; Li Y; Peng K; Li X; Tan Q; Mo Y; Lan Y; Zeng W; Qi W
Clin Radiol; 2022 Mar; 77(3):e215-e221. PubMed ID: 34916048
[TBL] [Abstract][Full Text] [Related]
12. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
[TBL] [Abstract][Full Text] [Related]
13. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.
Yuan M; Zhang YD; Pu XH; Zhong Y; Li H; Wu JF; Yu TF
Eur Radiol; 2017 Nov; 27(11):4857-4865. PubMed ID: 28523350
[TBL] [Abstract][Full Text] [Related]
14. Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.
Zhao M; Kluge K; Papp L; Grahovac M; Yang S; Jiang C; Krajnc D; Spielvogel CP; Ecsedi B; Haug A; Wang S; Hacker M; Zhang W; Li X
Eur Radiol; 2022 Oct; 32(10):7056-7067. PubMed ID: 35896836
[TBL] [Abstract][Full Text] [Related]
15. Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.
Wang X; Zhang L; Yang X; Tang L; Zhao J; Chen G; Li X; Yan S; Li S; Yang Y; Kang Y; Li Q; Wu N
Eur J Radiol; 2020 Aug; 129():109150. PubMed ID: 32604042
[TBL] [Abstract][Full Text] [Related]
16. Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study.
Jiang Y; Liang X; Han Z; Wang W; Xi S; Li T; Chen C; Yuan Q; Li N; Yu J; Xie Y; Xu Y; Zhou Z; Poultsides GA; Li G; Li R
Lancet Digit Health; 2021 Jun; 3(6):e371-e382. PubMed ID: 34045003
[TBL] [Abstract][Full Text] [Related]
17. Computed Tomography-Based Score Indicative of Lung Cancer Aggression (SILA) Predicts the Degree of Histologic Tissue Invasion and Patient Survival in Lung Adenocarcinoma Spectrum.
Varghese C; Rajagopalan S; Karwoski RA; Bartholmai BJ; Maldonado F; Boland JM; Peikert T
J Thorac Oncol; 2019 Aug; 14(8):1419-1429. PubMed ID: 31063863
[TBL] [Abstract][Full Text] [Related]
18. Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT.
Depeursinge A; Yanagawa M; Leung AN; Rubin DL
Med Phys; 2015 Apr; 42(4):2054-63. PubMed ID: 25832095
[TBL] [Abstract][Full Text] [Related]
19. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer.
Zhu X; Dong D; Chen Z; Fang M; Zhang L; Song J; Yu D; Zang Y; Liu Z; Shi J; Tian J
Eur Radiol; 2018 Jul; 28(7):2772-2778. PubMed ID: 29450713
[TBL] [Abstract][Full Text] [Related]
20. A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.
Chen X; Feng B; Chen Y; Liu K; Li K; Duan X; Hao Y; Cui E; Liu Z; Zhang C; Long W; Liu X
Cancer Imaging; 2020 Jul; 20(1):45. PubMed ID: 32641166
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
