Deep learning radiomics model related with genomics phenotypes for pathological complete response prediction in locally advanced rectal cancer under neoadjuvant chemoradiotherapy

Background: Developing a treatment strategy after neoadjuvant chemoradiotherapy (nCRT) based on accurate pathological complete response (pCR) predictions is crucial for locally advanced rectal cancer (LARC).Objective: To determine the effectiveness of a radiomics model using diffusion-weighted imaging (DWI) and T2-weighted in predicting post-nCRT pCR efficacy after LARC.Methods: This study enrolled 237 LARC patients who had radical surgery under nCRT between February 2020 and August 2023. Tumor tissues from 54 patients were RNA sequenced. Radiomics features from preoperative MRI data were used to develop a model for deep learning via the LASSO algorithm. ROC curves were used to assess the model performance. Following Spearman's correlation, analyses of transcriptomic data were conducted to identify the genes correlated with the DPL features.Results: The DPL model, incorporating 7 features and 5 machine-learning algorithms, was used to create risk assessment models. The RF model excelled in predicting pCR, achieving an AUC of 0.91, accuracy of 0.88, and F1 score of 0.89. Furthermore, catabolic processes and pathways related to immunity were associated with selected DPL features.Conclusion: The modified MRI-based radiomics model shows good predictive efficacy in predicting pCR after nCRT in LARC patients. Additionally, we investigated the genomic phenotypes associated with DPL.