[Current status and future perspectives on the methods of prognosis evaluation for intrahepatic cholangiocarcinoma].

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary malignant tumor in the liver after hepatocellular carcinoma. Its incidence and mortality rates have increased worldwide in recent years. Surgical resection is the best treatment modality for ICC;however,the overall prognosis remains poor. Accurate evaluation of post operative prognosis allows personalized treatment and improved long-term outcomes of ICC. The American Joint Commission on Cancer TNM staging manual is the basis for the standardized diagnosis and treatment of ICC;however,the contents of stage T and stage N need to be improved. The nomogram model or scoring system established in the analysis of commonly used clinicopathological parameters can provide individualized prognostic evaluation and improve prediction accuracy;however,more studies are needed to validate the results before clinical use. Meanwhile,imaging features exhibit great potential to establish the post operative prognosis evaluation system for ICC. Molecular-based classification provides an accurate guarantee for prognostic assessment as well as selection of populations that are sensitive to targeted therapy or immunotherapy. Therefore,the establishment of a prognosis evaluation system,based on clinical and pathological characteristics and centered on the combination of multidisciplinary and multi-omics,will be conducive to improving the long-term outcomes of ICC after surgical resection in the context of big medical data.

[Application value of machine learning algorithms for predicting recurrence after resection of early-stage hepatocellular carcinoma].

Objective: To compare the performance of multiple machine learning algorithms in predicting recurrence after resection of early-stage hepatocellular carcinoma(HCC). Methods: Clinical data of 882 early-stage HCC patients who were admitted to the First Affiliated Hospital of Nanjing Medical University from May 2009 to December 2019 and treated with curative surgical resection were retrospectively collected. There were 701 males and 181 females,with an age of (57.3±10.5)years(range:21 to 86 years). All patients were randomly assigned in a 2∶1 ratio, the training dataset consisted of 588 patients and the test dataset consisted of 294 patients. The construction of machine learning-based prediction models included random survival forest(RSF),gradient boosting machine,elastic net regression and Cox regression model. The prediction accuracy of the model was measured by the concordance index(C-index). The prediction error of the model was measured by the integrated Brier score. Model fit was assessed by the calibration plot. The performance of machine learning models with that of rival model and HCC staging systems was compared. All models were validated in the independent test dataset. Results: Median recurrence-free survival was 61.7 months in the training dataset while median recurrence-free survival was 61.9 months in the validation dataset, there was no significant difference between two datasets in terms of recurrence-free survival(χ²=0.029,P=0.865). The RSF model consisted of 5 commonly used clinicopathological characteristics, including albumin-bilirubin grade,serum alpha fetoprotein,tumor number,type of hepatectomy and microvascular invasion. In both training and test datasets,the RSF model provided the best prediction accuracy,with respective C-index of 0.758(95%CI:0.725 to 0.791) and 0.749(95%CI:0.700 to 0.797),and the lowest prediction error,with respective integrated Brier score of 0.171 and 0.151. The prediction accuracy of RSF model for recurrence after resection of early-stage HCC was superior to that of other machine learning models,rival model(ERASL model) as well as HCC staging systems(BCLC,CNLC and TNM staging),with statistically significant difference(P<0.01). Calibration curves demonstrated good agreement between RSF model-predicted probabilities and observed outcomes.All patients could be stratified into low-risk,intermediate-risk or high-risk group based on RSF model;statistically significant differences among three risk groups were observed in both training and test datasets(P<0.01). The risk stratification of RSF model was superior to that of TNM staging. Conclusion: The proposed RSF model assembled with 5 commonly used clinicopathological characteristics in this study can predict the recurrence risk with favorable accuracy that may facilitate clinical decision-support for patients with early-stage HCC.

[Application and challenge of radiomics technique in the era of precision medicine for hepatobiliary disease].

Radiomics, as an emerging technique of omics, shows the pathophysiological information of images via extracting innumerable quantitative features from digital medical images. In recent years, it has been an exponential increase in the number of radiomics studies. The applications of radiomics in hepatobiliary diseases at present include: assessment of liver fibrosis, discrimination of malignant from benign tumors, prediction of biological behavior, assessment of therapeutic response, and prognosis. Integrating radiomics analysis with machine learning algorithms has emerged as a non-invasive method for predicting liver fibrosis stages, microvascular invasion and post-resection recurrence in liver cancers, lymph node metastasis in biliary tract cancers as well as treatment response in colorectal liver metastasis, with high performance. Although the challenges remain in the clinical transformation of this technique, radiomics will have a broad application prospect in promoting the precision diagnosis and treatment of hepatobiliary diseases, backed by multi-center study with large sample size or multi-omics study.

[Progress in application of three-dimensional imaging technique in complex hepatobiliary surgery].

Hepatobiliary surgery is considered to be technically challenging because of complex intrahepatic and perihilar anatomical structures and variations.Nowadays, three-dimensional imaging technique plays an important role in the time of precise liver surgery.Three-dimensional images depict the spatial location of tumor, and the course, confluence pattern and variation of portal vein, hepatic artery, biliary system and hepatic vein distinctly while showing involved hepatic segments and the relationship with adjacent vessels from omnidirectional view, measuring the length of margin and future remnant liver.With the help of surgical simulation, surgeons can determine the significant vessels preoperatively.The application of three-dimensional imaging technique may improve the resectability and safety of complex hepatobiliary surgery, such as hilar cholangiocarcinoma, centrally located liver tumor, hepatolithiasis and living donor liver transplantation.Meanwhile, three-dimensional visualization facilitates the understanding of two-dimensional images and complicated surgical anatomy for surgeons.