Do changes in inflammatory markers predict hepatocellular carcinoma recurrence and survival after liver transplantation?

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

Development of preoperative and postoperative machine learning models to predict the recurrence of huge hepatocellular carcinoma following surgical resection

Resection has been commonly utilized for treating huge hepatocellular carcinoma (HCC) with a diameter of ≥10 cm; however, a high rate of mortality is reported due to recurrence. The present study was designed to predict the recurrence following resection based on preoperative and postoperative machine learning models. In total, 1,082 patients with HCC who underwent liver resection in the Eastern Hepatobiliary Surgery Hospital cohort between January 2008 and December 2016 were divided into a training cohort and an internal validation cohort. In addition, 164 patients from Mengchao Hepatobiliary Hospital cohort between January 2014 and December 2016 served as an external validation cohort. The demographic information, and serological, MRI, and pathological data were obtained from each patient prior to and following surgery, followed by evaluating the model performance using the concordance index, time-dependent receiver operating characteristic curves, prediction error cures, and a calibration curve. A preoperative random survival forest (RSF) model and a postoperative RSF model were constructed based on the training set, which outperformed the conventional models, such as the Barcelona Clinic Liver Cancer (BCLC), the 8th edition of the American Joint Committee on Cancer (AJCC 8th) staging systems, and the Chinese stage systems. In addition, the preoperative and postoperative RSF models could also re-stratify patients with BCLC stage A/B/C or AJCC 8th stage IB/II/IIIA/IIIB or Chinese stage IB/IIA/IIB/IIIA into low-risk, intermediate-risk, and high-risk groups in the training and the two validation cohorts. The preoperative and postoperative RSF models were effective for predicting recurrence in patients with huge HCC following hepatectomy.

New indexes derived from routine blood tests and their clinical application in hepatocellular carcinoma

Considerable efforts have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), but the prognosis of patients with HCC remains poor. The development of officious and easy-to-use indicators that are applicable to all levels of hospitals for the diagnosis, prognosis and risk prediction of HCC may play an important role in improving the current undesirable situation. The occurrence of HCC can cause a series of local and systemic changes, involving liver function, inflammation, immunity, and nutrition, which can be reflected in routine clinical indicators, especially laboratory metrics. A comprehensive analysis of these routine indicators is capable of providing important information for the clinical management of HCC. Routine clinical indicators are daily medical data that are readily available, easily repeatable, and highly acceptable, which has attracted clinicians to derive a number of comprehensive indexes from routine clinical indicators by means of four arithmetic operations, scoring system, and mathematical modeling. These indexes integrate several clinical indicators into a new single indicator that performs better than any of original individual indicators in the risk prediction, clinical diagnosis and prognostic evaluation of HCC and is easy to use. Herein, we reviewed recent indexes derived from routine clinical indicators for the diagnosis, prognosis and risk prediction of HCC.