214 related articles for article (PubMed ID: 33947428)
1. Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study.
Wang Z; Li N; Zheng F; Sui X; Han W; Xue F; Xu X; Yang C; Hu Y; Wang L; Song W; Jiang J
J Transl Med; 2021 May; 19(1):191. PubMed ID: 33947428
[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. 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]
4. 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]
5. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.
Tu SJ; Wang CW; Pan KT; Wu YC; Wu CT
Phys Med Biol; 2018 Mar; 63(6):065005. PubMed ID: 29446758
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.
Xu Y; Lu L; Sun SH; E LN; Lian W; Yang H; Schwartz LH; Yang ZH; Zhao B
Eur Radiol; 2022 Mar; 32(3):1517-1527. PubMed ID: 34549324
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Cancer Risk in Nodules Detected at Follow-Up Lung Cancer Screening CT.
Hammer MM; Byrne SC
AJR Am J Roentgenol; 2022 Apr; 218(4):634-641. PubMed ID: 34755524
[No Abstract] [Full Text] [Related]
11. Computerized detection of lung nodules through radiomics.
Ma J; Zhou Z; Ren Y; Xiong J; Fu L; Wang Q; Zhao J
Med Phys; 2017 Aug; 44(8):4148-4158. PubMed ID: 28494110
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.
González Maldonado S; Delorme S; Hüsing A; Motsch E; Kauczor HU; Heussel CP; Kaaks R
JAMA Netw Open; 2020 Feb; 3(2):e1921221. PubMed ID: 32058555
[TBL] [Abstract][Full Text] [Related]
13. Assessment of indeterminate pulmonary nodules detected in lung cancer screening: Diagnostic accuracy of FDG PET/CT.
Garcia-Velloso MJ; Bastarrika G; de-Torres JP; Lozano MD; Sanchez-Salcedo P; Sancho L; Nuñez-Cordoba JM; Campo A; Alcaide AB; Torre W; Richter JA; Zulueta JJ
Lung Cancer; 2016 Jul; 97():81-6. PubMed ID: 27237032
[TBL] [Abstract][Full Text] [Related]
14. Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules.
Wang B; Tang Y; Chen Y; Hamal P; Zhu Y; Wang T; Sun Y; Lu Y; Bhuva MS; Meng X; Yang Y; Ai Z; Wu C; Sun X
Lung Cancer; 2020 Jan; 139():103-110. PubMed ID: 31760351
[TBL] [Abstract][Full Text] [Related]
15. An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans.
Li S; Chen M; Wang Y; Li X; Gao G; Luo X; Tang L; Liu X; Wu N
Clin Lung Cancer; 2023 Dec; 24(8):e301-e310. PubMed ID: 37596166
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening.
Horeweg N; van Rosmalen J; Heuvelmans MA; van der Aalst CM; Vliegenthart R; Scholten ET; ten Haaf K; Nackaerts K; Lammers JW; Weenink C; Groen HJ; van Ooijen P; de Jong PA; de Bock GH; Mali W; de Koning HJ; Oudkerk M
Lancet Oncol; 2014 Nov; 15(12):1332-41. PubMed ID: 25282285
[TBL] [Abstract][Full Text] [Related]
18. Radiomic Analysis of Pulmonary Nodules for Distinguishing Malignancy From Benignancy: The Value of Using Iodine Maps From Dual-Energy Computed Tomography.
Zhong Y; Xu H; Zhang W; Li H; Yu TF; Yuan M
J Comput Assist Tomogr; 2022 Nov-Dec 01; 46(6):878-883. PubMed ID: 35830384
[TBL] [Abstract][Full Text] [Related]
19. Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial.
Li Q; Balagurunathan Y; Liu Y; Qi J; Schabath MB; Ye Z; Gillies RJ
Clin Lung Cancer; 2018 Mar; 19(2):148-156.e3. PubMed ID: 29137847
[TBL] [Abstract][Full Text] [Related]
20. Predicting Malignant Nodules from Screening CT Scans.
Hawkins S; Wang H; Liu Y; Garcia A; Stringfield O; Krewer H; Li Q; Cherezov D; Gatenby RA; Balagurunathan Y; Goldgof D; Schabath MB; Hall L; Gillies RJ
J Thorac Oncol; 2016 Dec; 11(12):2120-2128. PubMed ID: 27422797
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
