Colorectal Liver Metastasis (CRLM)

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

The goal of this clinical trial is to learn if a new injectable drug (IP-001), administered after standard liver tumor ablation, can help prevent cancer from returning in people (males/females, ≥18 years old) with colorectal cancer that has spread only to the liver. The study will determine if injecting IP-001 into a liver tumor(s) after ablation will reduce the risk of cancer coming back in the liver and from spreading elsewhere in the body, will stimulate the immune system, will have any side effects, and will help improve a patient's response to other cancer therapies. Researchers will compare a standard of care liver ablation alone (microwave ablation \[MWA\], a technique that destroys tumors using heat), with MWA plus a high-dose IP-001 or MWA with a low-dose IP-001. During the treatment procedures, the doctor first performs the standard microwave ablation to destroy the tumor. Then, in the experimental-drug arms, IP-001 is injected in and around the treated tumor area to activate the immune system locally so that the body is more likely to find and eliminate any remaining cancer cells.

The goal of this observational study is to investigate whether circulating tumor-derived DNA (ctDNA) can be reliably detected and analyzed in blood obtained through automated capillary sampling in adult patients (≥21 years) with colorectal liver metastases (CRLM). The main question it aims to answer is: * Can ctDNA be detected in small volumes of capillary blood collected using an automated sampling device? * Do ctDNA levels measured in capillary blood agree with those measured in venous blood from the same patients? Researchers will compare ctDNA measurements from capillary blood to those from venous blood (reference standard) to see if capillary sampling provides reliable results for ctDNA analysis. Participants will: * Provide a small blood sample through automated capillary collection. * Provide a venous blood sample during the same study visit for comparison.

This study evaluates the impact of a novel computer-aided prognostic prediction tool for colorectal liver metastases (CRLM) on clinician performance. Colorectal cancer is a leading cause of cancer-related mortality worldwide, with 20-30% of patients presenting synchronous liver metastases, which are associated with poor prognosis and high postoperative recurrence rates. Simultaneous resection of primary tumor and liver metastases is a preferred treatment for selected patients but outcomes vary significantly. The latest web-based tool uses Random Forest models integrating demographic, clinical, laboratory, and genetic data to predict postoperative recurrence and mortality specifically for CRLM patients undergoing simultaneous resection. This multiple-reader, multiple-case (MRMC) study will assess 12 physicians who will predict 1-, 3-, and 5-year recurrence and mortality risks in 166 retrospective cases, with and without the tool's aid, separated by a washout period. The primary focus is to determine whether the tool improves prediction accuracy for 3-year postoperative mortality, measured by AUC-ROC. Secondary and exploratory endpoints include other time points, sensitivity, specificity, inter-rater reliability, decision-making confidence, and evaluation time. By enabling individualized risk assessment, this tool aims to support optimized clinical decision-making and tailored treatment strategies for CRLM patients undergoing simultaneous resection.

Try FOLFOX-HAIC combining bevacizumab or cetuximab for initially unresectable colorectal liver metastasis patients to increase the conversion to resection rate to improve long-term survival outcomes