Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

Abstract

The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

Hepatocellular Carcinoma with Bile Duct Tumor Thrombus: A Case Report and Literature Review of 890 Patients Affected by Uncommon Primary Liver Tumor Presentation

“…Despite the evolving technologies in imaging diagnosis and the support of Artificial Intelligence in Hepatobiliary and Pancreatic Surgery (HPB) [ 5 , 11 , 12 , 13 , 14 ], the diagnosis of HCC with BDTT is very challenging. Its treatment is still debated due to a complex pre- and postoperative patients’ management [ 15 , 16 ].…”

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

“…In cancer treatment, tumor morphology is considered a biomarker. While it is a useful biomarker, it is not a robust one since it is unable to predict the response to treatment in patients receiving systemic therapy [ 28 ]. Additionally, different morphological parameters have been reported by different studies, but none have been confirmed by all authors.…”

“…Additionally, morphological criteria (e.g., tumor size) can be modified by chemotherapy, and it is unclear which value should be taken into consideration (prior to or after treatment). Furthermore, tumor morphology provides only a snapshot of the tumor and does not reflect its evolution over time [ 28 ].…”

“…Some studies have reported that it is possible to predict response to systemic therapy by analyzing the images at the time of diagnosis prior to chemotherapy; higher entropy and lower homogeneity in liver metastases were associated with a higher response rate. As compared to the standard RECIST criteria, texture analysis provided a more accurate and earlier prediction [ 28 ].…”

“…Furthermore, the ability to interpret some radiomics features facilitates their implementation into clinical practice. For example, entropy and heterogeneity, especially after contrast enhancement, indicate active disease with heterogeneous clones, whereas homogeneity after chemotherapy indicates tumor necrosis [ 28 ]. In addition to exploring subtle changes in tumor and liver texture before and after treatment, radiomics can also be used to evaluate the response of CRLM lesions to chemotherapy [ 22 ].…”