Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net.

Neoadjuvant therapy (NAT) has demonstrated considerable effectiveness in treating locally advanced non-small cell lung cancer (NSCLC). Major pathological response (MPR), defined by the proportion of viable tumor cells in the tumor bed assessed through whole slide images (WSIs), is regarded as a crucial marker for predicting patient prognosis. While artificial intelligence (AI)-assisted WSI analysis has proven to be more effective than manual inspection, the intricate nature of analyzing post-NAT NSCLC WSIs still poses substantial clinical and computational challenges. In this study, we introduce a self-supervised learning model named the Multi-Magnifier Transformer Network (MMT-Net), designed for estimating MPR in NSCLC patients with post-NAT. The MMT-Net framework comprises three main components: a CNN backbone pretrained on unannotated pathological images for feature extraction, a multi-attention mechanism for fusing multi-scale features and a transformer layer for final classification. Validation results on the curated NSCLC post-NAT dataset have demonstrated that MMT-Net is more effective than commonly used methods for estimating MPR. Additionally, attention map visualizations show strong agreement with pathologist annotations, and the model outputs significantly correlate with immunohistochemistry markers such as CK and Ki67, confirming the reliability of MMT-Net. The proposed MMT-Net holds promise for improving the efficiency of MPR estimation, further advancing the clinical application of NAT in NSCLC.