A2ST-GCM: An adaptive spatio-temporal aware graph convolutional model for predicting pathological complete response in neoadjuvant therapy

Accurate preoperative prediction of pathological complete response (pCR) following neoadjuvant immunochemotherapy is crucial for refining and customising perioperative treatment decisions. However, the challenge persists in developing reliable, interpretable, and intelligent imaging markers using early-stage computed tomography (CT). Considering the dynamic evolution of tumours during treatment can offer additional discriminative insights, modelling the spatial and temporal relationships in pre- and post-neoadjuvant treatment CT images may help address these challenges. This study proposes an adaptive spatio-temporal aware graph inference framework (A²ST-GCM) to learn potential correlations between tumour regions in the same period and across periods, generating an adaptive spatio-temporal topology, and effectively aggregating features. Specifically, the model first utilises the adaptive spatial graph convolution module (AS-GConv) to capture irregular spatial dependencies within tumour regions before and after treatment. Subsequently, the adaptive spatio-temporal graph convolution module (AST-GConv) is employed to model dynamic dependencies over time, effectively exploring the complementarity and correlation mechanisms among multiple spatio-temporal features, ultimately obtaining a spatio-temporal graph representation containing tumour heterogeneity information. Furthermore, this paper introduces a novel weighted loss function, effectively alleviating the class imbalance issue in predicting pCR. Quantitative experimental results demonstrate the outstanding performance of our model in pCR prediction. To the best of our knowledge, this paper represents the first attempt to apply graph networks to predicting pathological response in neoadjuvant immunochemotherapy. It provides a potential auxiliary tool to evaluate pathological response, aiming to identify individuals who could benefit from surgery avoidance, thereby offering significant clinical benefits for patients undergoing personalised organ-preserving treatment.