142 related articles for article (PubMed ID: 35933282)
1. Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification.
Masquelin AH; Alshaabi T; Cheney N; Estépar RSJ; Bates JHT; Kinsey CM
Acad Radiol; 2023 Jun; 30(6):1073-1080. PubMed ID: 35933282
[TBL] [Abstract][Full Text] [Related]
2. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
Liu A; Wang Z; Yang Y; Wang J; Dai X; Wang L; Lu Y; Xue F
Cancer Commun (Lond); 2020 Jan; 40(1):16-24. PubMed ID: 32125097
[TBL] [Abstract][Full Text] [Related]
3. Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.
Sun H; Zhang C; Ouyang A; Dai Z; Song P; Yao J
Biomed Eng Online; 2023 Nov; 22(1):112. PubMed ID: 38037082
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis.
Garau N; Paganelli C; Summers P; Choi W; Alam S; Lu W; Fanciullo C; Bellomi M; Baroni G; Rampinelli C
Med Phys; 2020 Sep; 47(9):4125-4136. PubMed ID: 32488865
[TBL] [Abstract][Full Text] [Related]
7. Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.
Warkentin MT; Al-Sawaihey H; Lam S; Liu G; Diergaarde B; Yuan JM; Wilson DO; Atkar-Khattra S; Grant B; Brhane Y; Khodayari-Moez E; Murison KR; Tammemagi MC; Campbell KR; Hung RJ
Thorax; 2024 Mar; 79(4):307-315. PubMed ID: 38195644
[TBL] [Abstract][Full Text] [Related]
8. Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters.
Liang L; Zhang H; Lei H; Zhou H; Wu Y; Shen J
Technol Cancer Res Treat; 2022; 21():15330338221119748. PubMed ID: 36259167
[No Abstract] [Full Text] [Related]
9. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.
Choi W; Oh JH; Riyahi S; Liu CJ; Jiang F; Chen W; White C; Rimner A; Mechalakos JG; Deasy JO; Lu W
Med Phys; 2018 Apr; 45(4):1537-1549. PubMed ID: 29457229
[TBL] [Abstract][Full Text] [Related]
10. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.
Uthoff J; Stephens MJ; Newell JD; Hoffman EA; Larson J; Koehn N; De Stefano FA; Lusk CM; Wenzlaff AS; Watza D; Neslund-Dudas C; Carr LL; Lynch DA; Schwartz AG; Sieren JC
Med Phys; 2019 Jul; 46(7):3207-3216. PubMed ID: 31087332
[TBL] [Abstract][Full Text] [Related]
11. Can Peritumoral Radiomics Improve the Prediction of Malignancy of Solid Pulmonary Nodule Smaller Than 2 cm?
Wu S; Zhang N; Wu Z; Ren J; E L
Acad Radiol; 2022 Feb; 29 Suppl 2():S47-S52. PubMed ID: 33189549
[TBL] [Abstract][Full Text] [Related]
12. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules.
Lin RY; Zheng YN; Lv FJ; Fu BJ; Li WJ; Liang ZR; Chu ZG
Med Phys; 2023 May; 50(5):2835-2843. PubMed ID: 36810703
[TBL] [Abstract][Full Text] [Related]
13. Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction.
Zhang R; Shi J; Liu S; Chen B; Li W
BMC Pulm Med; 2023 Apr; 23(1):132. PubMed ID: 37081469
[TBL] [Abstract][Full Text] [Related]
14. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.
Wu G; Woodruff HC; Shen J; Refaee T; Sanduleanu S; Ibrahim A; Leijenaar RTH; Wang R; Xiong J; Bian J; Wu J; Lambin P
Radiology; 2020 Nov; 297(2):451-458. PubMed ID: 32840472
[TBL] [Abstract][Full Text] [Related]
15. Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact.
Jiang Y; Che S; Ma S; Liu X; Guo Y; Liu A; Li G; Li Z
Cancer Imaging; 2021 Jan; 21(1):1. PubMed ID: 33407884
[TBL] [Abstract][Full Text] [Related]
16. A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules.
Cai J; Liu H; Yuan H; Wu Y; Xu Q; Lv Y; Li J; Fu J; Ye J
Clin Radiol; 2021 Feb; 76(2):143-151. PubMed ID: 33187676
[TBL] [Abstract][Full Text] [Related]
17. A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.
Xue LM; Li Y; Zhang Y; Wang SC; Zhang RY; Ye JD; Yu H; Qiang JW
Eur Radiol; 2022 Apr; 32(4):2672-2682. PubMed ID: 34677668
[TBL] [Abstract][Full Text] [Related]
18. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.
Peters AA; Weinheimer O; von Stackelberg O; Kroschke J; Piskorski L; Debic M; Schlamp K; Welzel L; Pohl M; Christe A; Ebner L; Kauczor HU; Heußel CP; Wielpütz MO
Eur Radiol; 2023 Jun; 33(6):3908-3917. PubMed ID: 36538071
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
