158 related articles for article (PubMed ID: 28268557)
1. Prediction of malignant and benign of lung tumor using a quantitative radiomic method.
Jun Wang ; Xia Liu ; Di Dong ; Jiangdian Song ; Min Xu ; Yali Zang ; Jie Tian
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1272-1275. PubMed ID: 28268557
[TBL] [Abstract][Full Text] [Related]
2. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.
Tunali I; Hall LO; Napel S; Cherezov D; Guvenis A; Gillies RJ; Schabath MB
Med Phys; 2019 Nov; 46(11):5075-5085. PubMed ID: 31494946
[TBL] [Abstract][Full Text] [Related]
3. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
Aerts HJ; Velazquez ER; Leijenaar RT; Parmar C; Grossmann P; Carvalho S; Bussink J; Monshouwer R; Haibe-Kains B; Rietveld D; Hoebers F; Rietbergen MM; Leemans CR; Dekker A; Quackenbush J; Gillies RJ; Lambin P
Nat Commun; 2014 Jun; 5():4006. PubMed ID: 24892406
[TBL] [Abstract][Full Text] [Related]
4. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative.
Wang W; Luo J; Yang X; Lin H
Acad Radiol; 2015 Apr; 22(4):488-95. PubMed ID: 25601306
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.
Lin H; Wang W; Luo J; Yang X
Acad Radiol; 2014 Dec; 21(12):1614-22. PubMed ID: 25442354
[TBL] [Abstract][Full Text] [Related]
8. Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.
Parmar C; Leijenaar RT; Grossmann P; Rios Velazquez E; Bussink J; Rietveld D; Rietbergen MM; Haibe-Kains B; Lambin P; Aerts HJ
Sci Rep; 2015 Jun; 5():11044. PubMed ID: 26251068
[TBL] [Abstract][Full Text] [Related]
9. Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses.
Choe J; Lee SM; Do KH; Lee G; Lee JG; Lee SM; Seo JB
Radiology; 2019 Aug; 292(2):365-373. PubMed ID: 31210613
[TBL] [Abstract][Full Text] [Related]
10. Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?
Digumarthy SR; Padole AM; Rastogi S; Price M; Mooradian MJ; Sequist LV; Kalra MK
Cancer Imaging; 2019 Jun; 19(1):36. PubMed ID: 31182167
[TBL] [Abstract][Full Text] [Related]
11. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X
Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487
[TBL] [Abstract][Full Text] [Related]
12. Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance.
de Sousa Costa RW; da Silva GLF; de Carvalho Filho AO; Silva AC; de Paiva AC; Gattass M
Med Biol Eng Comput; 2018 Nov; 56(11):2125-2136. PubMed ID: 29790102
[TBL] [Abstract][Full Text] [Related]
13. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer.
Wang L; Dong T; Xin B; Xu C; Guo M; Zhang H; Feng D; Wang X; Yu J
Eur Radiol; 2019 Jun; 29(6):2958-2967. PubMed ID: 30643940
[TBL] [Abstract][Full Text] [Related]
14. Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR.
Vuong D; Tanadini-Lang S; Huellner MW; Veit-Haibach P; Unkelbach J; Andratschke N; Kraft J; Guckenberger M; Bogowicz M
Med Phys; 2019 Apr; 46(4):1677-1685. PubMed ID: 30714158
[TBL] [Abstract][Full Text] [Related]
15. Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.
Wu W; Parmar C; Grossmann P; Quackenbush J; Lambin P; Bussink J; Mak R; Aerts HJ
Front Oncol; 2016; 6():71. PubMed ID: 27064691
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis.
Gong J; Liu J; Hao W; Nie S; Wang S; Peng W
Phys Med Biol; 2019 Jul; 64(13):135015. PubMed ID: 31167172
[TBL] [Abstract][Full Text] [Related]
17. Radiomic features analysis in computed tomography images of lung nodule classification.
Chen CH; Chang CK; Tu CY; Liao WC; Wu BR; Chou KT; Chiou YR; Yang SN; Zhang G; Huang TC
PLoS One; 2018; 13(2):e0192002. PubMed ID: 29401463
[TBL] [Abstract][Full Text] [Related]
18. CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features.
Wu L; Gao C; Xiang P; Zheng S; Pang P; Xu M
Front Oncol; 2020; 10():838. PubMed ID: 32537436
[No Abstract] [Full Text] [Related]
19. Unsupervised Approach for Malignancy Assessment of Lung Nodules in Computed Tomography Scans Using Radiomic Features.
Teixeira M; Pereira T; Silva F; Cunha A; Oliveira HP
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():2037-2040. PubMed ID: 36086366
[TBL] [Abstract][Full Text] [Related]
20. Measuring Interobserver Disagreement in Rating Diagnostic Characteristics of Pulmonary Nodule Using the Lung Imaging Database Consortium and Image Database Resource Initiative.
Lin H; Huang C; Wang W; Luo J; Yang X; Liu Y
Acad Radiol; 2017 Apr; 24(4):401-410. PubMed ID: 28169141
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]