O127 Application of machine learning approaches in the diagnosis and management of colorectal cancer

Abstract

Abstract Introduction Colorectal cancer (CRC) is one of the most common cancers worldwide. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have gained traction in CRC research for their ability to efficiently analyse large volumes of variables and offer objective data driven models to optimise clinical decision making. The present review provides a contemporary summary of the current application of these approaches in CRC. Methods A literature search was performed on AI models and ML learning in CRC and relevant articles were scrutinized. Results AI aided detection systems were successful in identifying CRC, and able to outperform endoscopists in detecting adenomas (detection rate 0.29 vs. 0.20, p < 0.001). ML models based on radiomics showed promise in identifying CRC metastasis and even outperforming national guideline-based models (AUC 0.83 vs. 0.73, p<0.01) in predicting risk of T1 CRC metastasis to lymph nodes. ML models were also able to identify novel genomic targets, predict response to oncological therapy, and predict 5-year disease free survival in stage II and III CRC (AUC of 0.698). In the operative context, AI can be trained to identify surgical steps and operative actions with accuracy scores of over 80% as well as. Conclusion AI modelling takes advantage of increasing digital medical data keeping to allow rapid high-volume data analysis which has found application in the diagnosis, staging, treatment, and prognosis of CRC and may be useful in the development of intra-operative guidance systems to optimize surgical approaches and training.