Artificial Intelligence-Driven Prediction of post Neoadjuvant Treatment Toxicities and Biomarkers Identifications in Rectal Cancer

Neoadjuvant chemotherapy (nCT) and chemoradiotherapy (nCRT) are widely used cancer treatments, however, patients’ response and subsequent clinical toxicity vary substantially. Predicting toxicity using artificial intelligence (AI) allows risk-adapted treatment and overcomes statistical models’ limitations. This study describes a novel approach using two complementary AI models to achieve three objectives. Initially, a fine-tuned pre-trained multilingual BERT model (mBERT), was used to analyse radiological reports and identify patients at risk of developing toxicities post-nCT or nCRT for rectal cancer (RC). Then, a MultiLayer Perceptron (MLP) neural network was developed to predict toxicities and identify its key biomarkers. Our results demonstrate that the mBERT model and MLP algorithm achieved strong performance in classifying patients at risk and predicting toxicities following nCT or nCRT for RC (mBERT: precision = 1, recall = 0.94 and F1-score = 0.97; MLP: accuracy = 0.90, mean squared error = 0.06 and mean absolute error = 0.16). The MLP algorithm identified toxicity biomarkers not previously reported in machine learning models. Furthermore, our study recommends using (y)pTNM staging as a biomarker for potential toxicity. In conclusion, this study presents a new AI approach to classify patients at risk and predict toxicities following nCT or nCRT for RC supporting personalized neoadjuvant therapeutic strategies.