Radiomic features predict local failure-free survival in stage III NSCLC adenocarcinoma treated with chemoradiation

José Marcio Luna, Andrew R. Barsky, Russell T. Shinohara, Alexandra D. Dreyfuss, Leonid Roshkovan, Michelle Hershman, Babak Haghighi, Bardia Yousefi, Peter B. Noël, Keith A. Cengel, Sharyn Katz, Eric S. Diffenderfer, Despina Kontos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Prognosis plays a crucial role in the customization of lung cancer care. The effective prediction of treatment response is essential to tailor treatment decisions to lung cancer patients. Molecular characterization of tumors using genomics-based approaches is important for personalized treatment planning, however, repeated tumor biopsies should be performed to capture their molecular heterogeneity, putting patients at risk of procedural complications such as a pneumothorax. Furthermore, the recent addition of immunotherapy after chemoradiotherapy for patients with unresectable stage III NSCLC can improve survival outcomes. The survival benefit achieved by stage III NSCLC patients undergoing chemoradiation is of interest since currently available biomarkers are inadequate to predict which patients are most likely to benefit from immunotherapy for first-line treatment along with chemoradiation. In this study, we investigate the association between local failure-free survival and radiomic features extracted from CT scans of stage III NSCLC adenocarcinoma patients. We retrospectively analyzed a well-curated cohort of 89 non-contrast enhanced CT scans from patients receiving homogeneous chemoradiation treatment. A set of 107 radiomic features was extracted using the pyradiomics package. In univariate analysis we performed log-rank tests per feature to predict risk of local failure. In multivariate analysis we applied principal component analysis to fit a Cox model to predict local failure-free survival. Univariate analysis showed that no individual radiomic feature can predict local failure-free survival, while multivariate analysis gave a C-index = 0.70, 95% CI = [0.56,0.85]. We conclude that radiomic features from CT scans, can predict local failure-free survival in stage III NSCLC.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
ISBN (Electronic)9781510640238
StatePublished - 2021
EventMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityVirtual, Online


  • Adenocarcinoma
  • Computed tomography scans
  • Local failure-free survival
  • Non-small cell lung cancer


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