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Artificial Intelligence-Based Assessment of Pathologic Response to Neoadjuvant Chemoimmunotherapy in Non-Small Cell
Daniel Curto1, Susana Hernandez1, Marta Alonso1
1Department of Pathology, Hospital Universitario 12 de Octubre, Molecular and Computational Pathology Group, Research Institute Hospital, 12 de Octubre (imas12), Madrid, Spain.
Background:
Pathologic response (PR), expressed as the percentage of residual viable tumor (%RVT), has emerged as a surrogate endpoint after neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC). However, PR assessment remains subject to interobserver variability and is insufficiently standardized across institutions and clinical trials. We hypothesized that artificial intelligence (AI)-based %RVT scoring would be concordant with expert manual assessment and consistent across real-world, multi-scanner cohorts.
Materials And Methods:
Four retrospective NSCLC cohorts treated with neoadjuvant chemoimmunotherapy and surgery were analyzed. The internal cohort included 38 patients; three external cohorts from China, Germany, and Spain contributed 97 additional patients. Weighted consensus manual %RVT served as the reference standard for benchmarking an AI algorithm. Discordant cases were interrogated for algorithmic bias.
Results:
Overall, 135 patients and 1,344 primary-tumor H&E slides were evaluable. Concordance between AI-based and manual assessment was at least moderate across all cohorts. For major pathologic response (MPR) classification, the AI algorithm achieved a pooled accuracy of 91% (95% CI, 85-95), sensitivity of 90% (82-95), specificity of 93% (81-99), and an area under the curve ≥0.95 in all cohorts. Discordant classifications occurred in 8.9% of cases and were mainly attributable to stromal underrepresentation and misclassification of non-neoplastic elements.
Conclusions:
AI-based %RVT scoring was concordant with expert pathologist consensus and consistent across real-world, multicenter, multi-scanner cohorts, with most algorithmic errors mirroring those encountered in human practice. By providing explainable %RVT grounded in international recommendations, our framework may facilitate harmonization of PR assessment for perioperative therapies.