TY - GEN
T1 - Radiomic features predict local failure-free survival in stage III NSCLC adenocarcinoma treated with chemoradiation
AU - Luna, José Marcio
AU - Barsky, Andrew R.
AU - Shinohara, Russell T.
AU - Dreyfuss, Alexandra D.
AU - Roshkovan, Leonid
AU - Hershman, Michelle
AU - Haghighi, Babak
AU - Yousefi, Bardia
AU - Noël, Peter B.
AU - Cengel, Keith A.
AU - Katz, Sharyn
AU - Diffenderfer, Eric S.
AU - Kontos, Despina
N1 - Funding Information:
This work was partially supported by an award granted by the Emerson Collective Research Fund. We acknowledge the Penn Center for Precision Medicine and the Abramson Cancer Center at Penn for their support with pilot funding. We also thank Drs. Nariman Jahani, Lyle Ungar and Daniel Pryma for suggestions that greatly improved the manuscript. Special thanks to Lauren Pantalone and Dr. Walter Mankowski for their assistance acquiring the studies.
Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Adenocarcinoma
KW - Computed tomography scans
KW - Local failure-free survival
KW - Non-small cell lung cancer
UR - http://www.scopus.com/inward/record.url?scp=85103692999&partnerID=8YFLogxK
U2 - 10.1117/12.2581873
DO - 10.1117/12.2581873
M3 - Conference contribution
AN - SCOPUS:85103692999
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Mazurowski, Maciej A.
A2 - Drukker, Karen
PB - SPIE
T2 - Medical Imaging 2021: Computer-Aided Diagnosis
Y2 - 15 February 2021 through 19 February 2021
ER -