CheckpointPx: A predictive radiology AI model of immune checkpoint inhibitor (ICI) benefit in non-small cell lung cancer (NSCLC).
Authors: Lee Seyoung , Hiremath Amogh , Lee Jeeyeon , Kim Haseok , Zhang Kai , Lee Salie , Yadav Monica , Chung Liam IL Young , Kim Hye Sung , Djunadi Trie Arni , Kim Yuchan , Hong Ilene , Kang Grace , Cho Amy , Velichko Yury , Gupta Amit , Velcheti Vamsidhar , Madabhushi Anant , Braman Nathaniel , Chae Young Kwang 
Published: June 2024

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Background: Immune checkpoint inhibitors (ICIs) have dramatically transformed the field of non-small cell lung cancer (NSCLC) treatment. Despite the widespread use of PD-L1 as a biomarker in NSCLC, it has significant limitations as a reliable predictive biomarker for ICI response. Addressing this limitation, we have developed CheckpointPx, a non-invasive radiology AI tool to assist in ICI treatment selection for patients using only baseline CT scans. Here we demonstrate that CheckpointPx can predict improved outcomes specific to treatment with ICIs, presenting a new tool to address the shortage of reliable predictive ICI biomarkers in NSCLC and ultimately improve outcomes for patients undergoing immunotherapeutic interventions. Methods: CheckpointPx (v1.11) was trained on pre-treatment CT scans to predict response to ICIs from training data (D1: n=252 ICI recipients) from three institutions (A-C). In this study, we evaluated its performance in predicting response in two validation cohorts: a Treatment Dataset of patients receiving immunotherapy (D2: n=224, institutions B-D) and a Control Dataset of patients receiving platinum-based chemotherapy alone (D3: n=76, institution B). The response was defined as disease control per RECIST v1.1. Experienced radiologists and physicians delineated target lesions, and a deep learning model segmented pulmonary vessels. From these segmented regions, radiomic features were extracted using the Picture Health Px platform and were used to generate a radiomics benefit score and corresponding benefit groups using D1. The ability of benefit groups to stratify patients by progression-free survival (PFS) was compared across D2 and D3 to evaluate its utility in identifying patients who would benefit from ICI over chemotherapy. Results: D2 and D3 contained a mix of treatment lines (1st-4th) and predominantly late-stage tumors (Stage 3/4, >85%). CheckpointPx included 16 features, such as measurements of intra-tumoral heterogeneity and vessel twistedness and branching patterns. Within the treatment dataset (D2), CheckpointPx was found to significantly stratify ICI patients by progression-free survival (PFS) with HR=0.68 (95% CI: 0.52 - 0.93, p=0.019). When applied to the control dataset, D3, benefit groups failed to stratify patients treated with chemotherapy by outcome (HR=0.91 [95% CI: 0.51-1.61], p=0.740), indicating that the signature was specific to ICI response. Conclusions: CheckpointPx demonstrated the ability to identify NSCLC patients who would benefit from ICI over chemo. The model’s association with PFS among ICI recipients, but not patients receiving chemotherapy alone, suggests that the signature is predictive of immunotherapy-related outcomes rather than generally prognostic. Additional independent, multi-site and prospective validation is warranted.