Abstract 2775: Deep-learning CT biomarker improves early efficacy detection in simulated randomized phase II NSCLC trials.

Abstract Background: Early decision-making in advanced non-small cell lung cancer (NSCLC) phase II trials is limited by the modest ability of objective response and progression-free survival (PFS) to detect early biological activity or predict overall survival (OS). Quantitative deep-learning analysis of routine CT imaging may offer a more sensitive measure that better reflects long-term benefit. We evaluated whether Serial CTRS, a fully automated CT-based deep-learning imaging biomarker, could improve early efficacy detection in simulated randomized phase II NSCLC trials. Methods: We evaluated the utility of Serial CTRS using data from the randomized phase III trial of cetuximab plus carboplatin/paclitaxel with or without bevacizumab in advanced NSCLC, which did not meet its co-primary endpoints of PFS in patients with EGFR FISH-positive cancer and OS in the entire study population (SWOG S0819; N=1275). Serial CTRS is a convolutional-neural-network pipeline, trained on a large real-world advanced NSCLC dataset, using paired baseline and follow-up thoracic CT scans to generate a continuous imaging score without manual annotation. To quantify OS surrogacy, we repeatedly sampled 1000 pairs of random 50-patient arms from the full cohort, and correlated Serial CTRS differences at 8, 16, and 24 weeks with final OS hazard ratios (HR), comparing results with best overall response (BOR) and PFS. To simulate a positive phase II trial, we constructed a balanced subset (target OS HR≈0.50) using stratified pruning matched on randomization factors. We then simulated 1000 two-arm phase II trials (n=50/arm) with realistic staggered enrollment (averaging 1 patient/day) and interim analyses (IA) at 12-48 weeks from study start. PFS was evaluated via log-rank tests and Serial CTRS differences via Wilcoxon rank-sum tests (α=0.05). False-positive rates were evaluated through null simulations using the full dataset. Results: Serial CTRS differences showed increasing concordance with OS HR across timepoints (R2=0.10, 0.23, 0.35 at 8, 16, and 24 weeks), outperforming BOR (R2 = 0.08) and PFS (R2=0.09, 0.20, 0.28). In the simulated phase II trials, the biomarker achieved 60% (95% CI 58-62%) power and 66% (63-69%) power at 36 weeks to detect a long-term survival benefit while maintaining a 5-6% false-positive rate. BOR achieved 35% (33-37%) power, and PFS achieved 49% (46-51%) and 50% (48-52%) at the same timepoints. Conclusions: A fully automated deep-learning CT biomarker provided earlier and more reliable efficacy readouts than BOR and PFS in simulated phase II NSCLC trials. These results suggest that quantitative CT biomarkers using the full thoracic scan can strengthen early drug-development decisions by improving power and reducing uncertainty around early activity signals. Ongoing work is focused on broader evaluation across tumor types, therapeutic modalities, and additional clinical datasets. Citation Format: Chiharu Sako, Brenda F. Kurland, Taly G. Schmidt, Dwight H. Owen, Arpan A. Patel, Nicholas C. Love, Olivier Gevaert, George R. Simon, Ravi B. Parikh, Petr Jordan. Deep-learning CT biomarker improves early efficacy detection in simulated randomized phase II NSCLC trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2775.