Multi-center real-world data curation and assessment of tumor growth rate and overall survival in advanced NSCLC treated with PD-(L)1 immune checkpoint inhibitor therapy.
Authors: Sako Chiharu , Jordan Petr , McCall Ross , Patel Arpan , Owen Dwight Hall , Amini Arya , Liu An , Curti Brendan D. , Weerasinghe Roshanthi K. , Lee Soohee , Page Ray D. , Swalduz Aurélie , Beregi Jean-Paul , Sanchez Stéphane , Gevaert Olivier , Parikh Ravi Bharat , Simon George R. , Aerts Hugo 
Published: June 2023

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Background: While Immune Checkpoint Inhibitors (ICI) have become the standard-of-care for advanced non-small cell lung cancer (NSCLC) for certain patient populations, durable responses only occur in a small subset of patients. Therefore, there is an unmet need to identify patients that will respond before and during treatment to optimize treatment strategies. Multiple studies have shown promise that quantitative imaging biomarkers can predict response to ICI therapy. Studies have also shown that tumor growth parameters derived from time series data can provide early indications of long-term response. However, most studies to date have investigated single institution or clinical trial datasets, which are inadequate to capture the broad spectrum of real-world diversity of ICI treatments. In this study, we aimed to build a generalizable, well-validated imaging biomarker for treatment response. Methods: We have curated a retrospective dataset including serial imaging data of 1,215 advanced NSCLC patients who received ICIs from nine institutions across the US and Europe, capturing the diversity of the imaging data, such as CT scanner models, image acquisition parameters, reconstruction algorithms, as well as diversity in patient population, performance status, and treatment settings. Expert lesion annotations and manual volumetric segmentations were performed on all visible lesions (ranging from 1-18 lesions per subject, totaling 6,441 lesions) across the serial imaging data (ranging from 1-18 follow-up scans per subject), which enables lesion and organ-specific tumor burden assessment. We stratified overall survival based on volumetric response and tumor growth rate using a model defined by the sum of exponential growth ( g) and decay ( d), vol(t) = vol(0)*(egt + e-dt -1). Results: Survival analysis showed that the 3-month volumetric response (N = 875) was significantly associated with overall survival (OS) for the lowest quartile (Q1; log-rank p-value, P = 2.1e-4) and highest quartile (Q4; P = 9.8e-8) compared to the middle quartiles (Q2+Q3). The median overall survival (OS) stratified by the volumetric response was 547.7, 450.9, and 332.0 days in the Q1, Q2+Q3, Q4 quadrants, respectively. Among patients having a minimum of 2 follow-up scans in the first 18 weeks (N = 264), we found that patients with the tumor growth parameter ( g) in the highest quartile had significantly lower OS compared to the patients in the Q2+Q3 quadrants (P = 7.2e-5). Conclusions: Our results demonstrate the potential of large multi-center real-world imaging datasets in investigating novel early response assessment methodologies, such as volumetric tumor burden quantification and tumor growth modeling. In our further work, we will investigate response patterns at individual lesion levels and correlations with overall patient-level response.