Title | Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Khorrami M, Bera K, Leo P, Vaidya P, Patil P, Thawani R, Velu P, Rajiah P, Alilou M, Choi H, Feldman MD, Gilkeson RC, Linden P, Fu P, Pass H, Velcheti V, Madabhushi A |
Journal | Lung Cancer |
Volume | 142 |
Pagination | 90-97 |
Date Published | 2020 04 |
ISSN | 1872-8332 |
Keywords | Adenocarcinoma of Lung, Adult, Aged, Aged, 80 and over, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Female, Follow-Up Studies, Humans, Lung Neoplasms, Male, Middle Aged, Neoplasm Recurrence, Local, Pneumonectomy, Prognosis, Retrospective Studies, Survival Rate, Young Adult |
Abstract | 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) and validation set (D). 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) and third (D) 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, AUC of 0.75 vs. 0.65; D, 0.74 vs. 0.62; D, 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. |
DOI | 10.1016/j.lungcan.2020.02.018 |
Alternate Journal | Lung Cancer |
PubMed ID | 32120229 |
PubMed Central ID | PMC7141152 |
Grant List | R01 CA216579 / CA / NCI NIH HHS / United States C06 RR012463 / RR / NCRR NIH HHS / United States U24 CA199374 / CA / NCI NIH HHS / United States I01 BX004121 / BX / BLRD VA / United States R43 EB028736 / EB / NIBIB NIH HHS / United States U01 CA239055 / CA / NCI NIH HHS / United States R01 CA220581 / CA / NCI NIH HHS / United States R01 CA202752 / CA / NCI NIH HHS / United States R01 CA208236 / CA / NCI NIH HHS / United States |
Related Faculty:
Priya Velu, M.D., Ph.D.