P57.04 Predicting Treatment Response to 1st- line Pembrolizumab in Advanced Non-Small Cell Lung Cancer (NSCLC) Patients with High PDL1 Expression

Abstract

Combination of pembrolizumab and chemotherapy has shown improved survival in advanced NSCLC compared to chemotherapy alone. However, the overall response rate has only increased from 45% to 60% by adding chemotherapy to pembrolizumab alone for patients with high PDL1 expression (PD-L1 ≥50%). Currently no biomarker or clinicopathological features can be used to identify the subgroup with tumors PD-L1 ≥ 50% who would respond well to pembrolizumab alone in the 1st-line setting. We hypothesize a machine learning model trained on pre-treatment CT image radiomic features can be used as a digital biomarker to select the aforementioned subgroup, thereby reducing chemotherapy related toxicity and mitigating health care expenditure. We retrospectively identified stage III/IV NSCLC patients who received 1st-line single pembrolizumab at British Columbia Cancer (Vancouver, Canada), with PDL1 ≥50% and all had baseline staging CT within 6 weeks prior to starting immunotherapy and the 1st follow-up (FU) CT within 12 weeks after treatment initiation. The baseline and 1st follow-up CTs were reviewed by two oncological radiologists to identified the lung tumors on the baseline CT images and assess tumor response (i.e., partial response, PR vs progressive disease, PD) using RECIST 1.1 criteria. Utilizing an in-house CT Otsu based thresholding segmentation program and radiomic feature extraction pipeline, we identified potentially discriminating features from CT lung tumor images. We performed sequential forward feature selection to identify the 10 most highly classifying features from discrete shape, texture, and intensity radiomic features generated for 3 distinct tumor segmentation masks (lesion core, core with perimeter transition pixels, and the ring of parenchymal tissue surrounding the lesion). In this study, we leveraged an 8-Xfold validation Linear Discriminate Analysis (LDA), a simple machine learning method trained on these 10 features, to discriminate tumor response to 1st-line pembrolizumab as evaluated at FU CT scans. Sixty-eight patients were included (44% male, 73±6 yrs, 52ES/12CS/4NS; pack years: 43±18; ECOG range: 0-3). Twenty-eight showed response to the 1st-line pembrolizumab and forty had progressive disease. We identified eighty-four lung tumors in these patients which we extracted radiomic features from the five central slices, providing us with a pseudo-volumetric CT image training and test set (N=380). ROC analysis of the LDA model resulted in an accuracy of 79.4% [SN: 0.882, SP: 0.684] (AUC: 0.79) for our patient dataset. Given our class label imbalance, we performed precision-recall (PPV vs Sensitivity) analysis to identify sources of class bias (AUC vs No-skill AUC: 0.80 vs 0.59). Our data showed that a rudimentary and interpretable machine learning method can lead to appropriate treatment paths for advanced stage lung cancers. A combination of all mask features performed well on this task with a majority of the features selected being texture and intensity features from the core plus edge transitionary boundary pixels. Future analysis will be conducted using other machine learning methods to validate these findings, while keeping the LDA as the primary analysis method.