Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. (September 2019) - Khorrami, Mohammadhadi; Jain, Prantesh; Bera, Kaustav; Alilou, Mehdi; Thawani, Rajat; Patil, Pradnya; Ahmad, Usman; Murthy, Sudish; Stephans, Kevin; Fu, Pingfu; Velcheti, Vamsidhar; Madabhushi, Anant
Highlights: Tumoral heterogeneity is associated with more aggressive tumor phenotype. Stage III N2 non-small cell lung cancer is a heterogeneous disease. Major pathologic response is associated with amended overall survival, following NAC. Compared to clinicopathologic variables, texture features are associated with MPR. Predictive features to MPR are also associated with survival and time to recurrence. Abstract: Objective: The use of a neoadjuvant chemoradiation followed by surgery in patients with stage IIIA NSCLC is controversial and the benefit of surgery is limited. There are currently no clinically validated biomarkers to select patients for such an approach. In this study we evaluate computed tomography (CT) derived intratumoral and peritumoral texture and nodule shape features in their ability to predict major pathological response (MPR). MPR being defined as ≤10% of residual viable tumor, assessed at the time of surgery. Material and methods: Ninety patients with stage III NSCLC treated with chemoradiation prior to surgical resection were selected. The patients were divided randomly into two equal sets, one for training and one for independent testing. The radiomic texture and shape features were extracted from within the nodule (intra) and from the parenchymal regions immediately surrounding the nodule (peritumoral). A univariate regression analysis was performed on the image and clinicopathologic variables and then included into a multivariable logistic regression (MLR) for binary outcome prediction of MPR. The radiomic signature risk-score was generated by using a multivariate Cox regression model and association of the signature with OS and DFS was also evaluated. Results: Thirteen stable and predictive intratumoral and peritumoral radiomic texture features were found to be predictive of MPR. The MLR classifier yielded an AUC of 0.90 ± 0.025 within the training set and a corresponding AUC = 0.86 in prediction of MPR within the test set. The radiomic signature was also significantly associated with OS (HR = 11.18, 95% CI = 3.17, 44.1; p -value = 0.008) and DFS (HR = 2.78, 95% CI = 1.11, 4.12; p-value = 0.0042) in the testing set. Conclusion: Texture features extracted within and around the lung tumor on CT images appears to be associated with the likelihood of MPR, OS and DFS to chemoradiation.