A Retrospective Observational Study to Use Artificial Intelligence for Prediction of Disease REcurrence of COlorectal Cancer Liver METastasis After Hepatic Resection

ColoRectal Cancer (CRC) is the third most common cancer worldwide and the fourth most common cause of cancer-related deaths. Survival is mainly determined by disease stage and the presence of metastasis. Five-year survival among patients with metastatic CRC is 12-19% versus 90% in patients with localized disease, with ColoRectal cancer Liver Metastases (CRLM) occurring in 30-50% of patients with CRC and being responsible for two-thirds of CRC-related deaths. Recent advances in the treatment of CRLM have increased patient overall survival (OS) from 6 months to 5-year survival rates of 25-40%. The combination between chemotherapy and liver resection remains the therapeutic option with the highest survival benefit for patients with CRLM. With surgery still representing the only acknowledged potential curative treatment, on the other hand, modern chemotherapy, also thanks to the introduction of biological drugs, has led to a better response and survival rates for patients with liver metastases, also contributing to increasing the resectability rate (i.e. the number of patients made operable thanks to cytoreduction following medical therapy).

Up to 30% of patients may be cured if metastases in the liver can be completely removed (the medical term for this is "resection"). For surgery to be considered, an oncological disease must be radically resectable from the technical point of view. At the same time, an adequate amount of normal liver must be left behind after the resection to sustain life. Locoregional therapies, including thermal ablation, chemoembolization, and radiation, are also used to manage CRLM patients as alternatives to conventional curative treatment. In particular, the locoregional treatment of CRLM can benefit from thermal ablation, either radiofrequency-based or microwave-based. Indeed, even if resection remains the locoregional treatment of choice for resectable liver metastases, ablation may offer similar benefits in selected patients, helping to spare healthy liver parenchyma.

Despite these significant improvements in prognosis, a large proportion of patients (nearly half) will anyway experience recurrence following the combination of locoregional treatments. With the improvement of non-surgical therapies, patients who relapse could undergo a non-surgical treatment rather than resection. Therefore, it is of paramount importance to define at best the treatment strategy for patients based on an accurate estimate of prognosis after treatment, by balancing the perioperative risk of morbidity/mortality with the risk of recurrence. Identifying these relapsing patients in advance would be crucial to avoid futile surgery or to allocate them to pharmacological adjuvant (i.e. postoperative) treatments.

Most patients with CRLM undergo preoperative chemotherapy programs with a response probability of around 60% of cases. The healthcare and biological costs of chemotherapy programs are significant. Furthermore, several chemotherapy regimens currently exist, but there is a lack of data indicative of which regimen is effective in which patient. Identifying these responding patients in advance would be extremely determinant to avoid futile chemotherapy treatment without clinical benefit and to guide the choice of allocating them or not to pharmacological neoadjuvant (i.e. preoperative treatments) and the selection of adjuvant regimen. In turn, it is important to distinguish responders from non-responders early to select the most appropriate therapeutic approach.

The clinical characteristics of the patients and the disease, in addition to radiological imaging, are all proved to be indispensable tools that help to evaluate the extent of disease, assess response to treatment, and identify drug toxicities and recurrence. Some studies suggest that texture feature analyses may quantitatively detect liver metastases before they become visually detectable by the radiologist. However, the value of these conventional factors alone in predicting CRLM prognosis is restricted. Multifactoral prognostic scoring systems have been developed in the last years as potential recurrence predictors after liver resection, but these still host limitations in applicability, sensitivity and specificity. Novel accurate prognostic indicators in patients with CRLM are urgently needed. Specifically, there is a clinical need to identify a priori patients who have different probabilities of developing recurrence of the disease after locoregional treatment (liver resection with or without thermal ablation) and different response to chemotherapy treatment, in order to refine a risk-based allocation to treatments and of resources. As known, Artificial Intelligence (AI) is a branch of computer science that aims to simulate human intelligence and behaviors to assist humans in specific tasks. The widespread digitalization of healthcare generates a vast amount of data and, together with accessible high-performance computing, AI technologies can be applied to overcome actual limitations in the estimation of CRLM recurrence and response after locoregional and chemotherapeutic treatments, thus reaching a finest allocation to treatments with respect to the current practice.