Multi-modal and sequential treatment of liver cancer and its impact on the gastrointestinal tract

Image-Guided Local Treatment for Unresectable Intrahepatic Cholangiocarcinoma-Role of Interventional Radiology

Intrahepatic cholangiocarcinoma is a highly aggressive malignancy with an increasing incidence in recent years. Prognosis is poor and most patients are not eligible for resection at the time of initial diagnosis due to the anatomic location, inadequate hepatic reserve, limiting comorbidities or metastatic disease. Several locoregional therapies from the field of interventional radiology exist for patients who are not amenable for surgery, or in case of local recurrence as a single treatment modality or combined with systemic treatment. To date, evidence is limited, with most conclusions drawn from single-center studies with small patient cohorts, often treated in the salvage situation or for local recurrence after initial resection. Nevertheless, the results are promising and suggest a survival benefit in selected patients. This narrative review focuses on the use of different locoregional treatment options for intrahepatic cholangiocarcinoma.

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available.