What question did this study set out to answer?

This review aims to evaluate AI models that predict the response to neoadjuvant therapy based on digital histopathology slides.

July 6, 2026Open Access

Predicting response to neoadjuvant therapy using artificial intelligence on digitized histopathology slides: a systematic review

Key Points

This review aims to evaluate AI models that predict the response to neoadjuvant therapy based on digital histopathology slides.
Conducted a systematic search across five databases following PRISMA guidelines.
Assessed study quality using QUADAS-2 criteria.
Included 25 studies from an initial 235 based on inclusion criteria.
Most AI models showed area under the curve (AUC) values ranging from 0.70 to 0.90.
Approximately 40% of the studies included external validation cohorts.
Future work needs to focus on standardized data acquisition and patient-level validation.

Abstract

Neoadjuvant therapy (NAT) is an important treatment strategy in surgical oncology, but not all patients benefit equally from it. This systematic review is the first to evaluate artificial intelligence (AI) models predicting NAT response from hematoxylin and eosin (H&E)-stained biopsies slides of solid tumors. A systematic search across five databases was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Out of 235 studies, 25 met the inclusion criteria and were analyzed regarding their AI methodologies, data modalities, and type of NAT. Most studies reported area under the curve (AUC) ranging from 0.70 to 0.90, and approximately 40% included external validation cohorts. In conclusion, AI models show promise in predicting NAT response from pathological slides, but future work should emphasize standardized data acquisition, patient-level validation, data transparency, and code sharing.

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