期刊
LUNG CANCER
卷 142, 期 -, 页码 90-97出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.lungcan.2020.02.018
关键词
NSCLC; Surgery; Adjuvant chemotherapy; Radiomics; Quantitative imaging
资金
- National Cancer Institute of the National Institutes of Health [1U24CA199374-01, R01CA202752-01A1, R01CA216579-01A1, R01CA220581-01A1]
- National Center for Research Resources [1C06-RR12463-01]
- United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service [IBX004121A]
- DOD Prostate Cancer Idea Development Award [W81XWH-15-1-0558]
- DOD Lung Cancer InvestigatorInitiated Translational Research Award [W81XWH-18-1-0440]
- DOD Peer Reviewed Cancer Research Program [W81XWH-16-1-0329]
- Ohio Third Frontier Technology Validation Fund the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering
- Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University
Objectives: To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). Materials and Methods: CT scans of 610 patients with early stage (IA, IB, IIA) NSCLC from four independent cohorts were evaluated. A total of 350 patients from Cleveland Clinic Foundation and University of Pennsylvania were divided into two equal sets for training (D-1) and validation set (D-2). 80 patients from The Cancer Genome Atlas Lung Adenocarcinoma and Squamous Cell Carcinoma and 195 patients from The Cancer Imaging Archive, were used as independent second (D-3) and third (D-4) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery. Results: A feature selection strategy focusing on both feature discriminability and stability resulted in the classifier having a higher discriminability on validation datasets compared to the discriminability alone criteria in discriminating cancer recurrence (D-2, AUC of 0.75 vs. 0.65; D-3, 0.74 vs. 0.62; D-4, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62). Conclusion: Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.
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