Colorectal cancer is the third most common cancer worldwide and the fourth most common cause of cancer-related death. Survival is primarily determined by stage of disease and the presence of metastases. The combination of chemotherapy and liver resection remains the treatment option with the highest survival benefit for patients with liver metastases from colorectal cancer, with surgery still being the only recognized potential curative treatment; surgical locoregional treatment can also be combined with thermal ablation to enhance the possibility of complete liver clearance. Despite significant improvements in prognosis, a large proportion of patients (almost half) will still experience recurrence following treatment. There is a clinical need to identify a priori patients who are different likely to develop disease recurrence after locoregional treatment (liver resection ± thermal ablation) and to respond differently to chemotherapy, in order to refine risk-based allocation of treatments and resources. Widespread digitalization of healthcare generates a large amount of data, and together with today accessible high-performance computing, artificial intelligence technologies can be applied to overcome the current limitations in estimating colorectal cancer liver metastases recurrence and response to locoregional and chemotherapy treatments, thus achieving better treatment allocation than current practice. All radiomic features can also help in training the neural network aimed at detecting liver metastases before they become visually detectable by the radiologist. Therefore, this study aims to evaluate whether a multifactorial machine learning model (including clinical and radiomic) can identify patients with colorectal cancer liver metastases with a high risk of progression after chemotherapy and recurrence after liver resection
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Development of an ML algorithm predicting which individuals diagnosed with CRLM are most likely to experience early recurrence of disease after liver resection.
Timeframe: 6 months post-intervention