Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
Can texture analysis of pre-immunotherapy CT imaging predict clinical outcomes for patients with advanced NSCLC treated with Nivolumab?
Metastatic Non-Small Cell Lung Cancer
Lung Cancer—Non-Small Cell Metastatic
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e20720)
Author(s): Benjamin Oren Spieler, Diana Saravia, Gilberto Lopes, Gregory Azzam, Deukwoo Kwon, Alan Dal Pra, Raphael Yechieli, Tejan Diwanji, Ivaylo Mihaylov; University of Miami Miller School of Medicine, Miami, FL; University of Miami Sylvester Comprehensive Cancer Center, Miami, FL; University of Miami Health System, Miami, FL
Background: Targeted therapies are ineffective in most NSCLC patients and response rates remain < 20% for patients with advanced NSCLC on immuno-monotherapy. Predictive models that distinguish responders from non-responders to immunotherapy could help guide clinical practice. Texture analysis is a data-mining tool used to identify intensity patterns in diagnostic imaging. We hypothesized that texture features on pre-immunotherapy CT imaging can be associated with clinical outcomes for patients with advanced NSCLC treated with Nivolumab. Methods: In an IRB-approved database containing 159 patients with advanced NSCLC treated with Nivolumab monotherapy, 20 patients with the longest overall survival (OS) and 20 with the shortest were selected for retrospective analysis. Patient characteristics were compared using paired t-tests. The last pre-immunotherapy PET CT for each patient was transferred to MIM software for segmentation. All FDG-avid intrathoracic tumors were delineated on the CT scan per RTOG contouring guidelines. Ninety-two texture features within each tumor were analyzed for association with the primary endpoint, OS. OS time was dichotomized to less than 1 year vs. more than 1 year. A univariate logistic regression model was used to estimate odds ratio (OR), 95% confidence interval and p-value for each feature. Multiple testing adjustments were performed using false discovery rate. Results: Eleven out of 92 texture features showed significant association with OS time (p-values from 0.009 to 0.044), of which 7 exhibited large effect (OR < 0.5 or > 1.5). Fifteen additional texture features trended toward statistical significance with p-values from .05 to .10. In all, 26 out of the 92 texture features showed significant association or trended toward significance with duration of OS. Conclusions: This preliminary study suggests that texture features on pre-immunotherapy CT imaging may help in predicting OS duration for patients with advanced NSCLC treated with Nivolumab monotherapy. We are in the process of validating a multivariate predictive model. Future directions include expansion of this study across the full database, survival analyses and correlation of texture features with tissue biology.