Dynamic evaluation of postoperative survival in intrahepatic cholangiocarcinoma patients who did not undergo lymphadenectomy: a multicenter study.

BACKGROUND: The prognosis of Intrahepatic cholangiocarcinoma (ICC) patients who did not undergo lymphadenectomy is difficult to assess. This study aims to have a dynamic evaluation on the postoperative survival of ICC patients by calculating conditional survival. METHODS: Relevant data were from patients treated in 12 large-scale hospitals from December 2011 to December 2017. The influence of relevant clinical baseline data on the prognosis of ICC patients was analyzed by Cox regression. Conditional survival (CS) is a method that may predict the prognostic probability dynamically. For a patient with x years of survival, the 1-year CS (CS1) may be calculated as CS1= OS(x + 1)/OS(x). RESULT: A total of 361 patients who met the criteria were included in the study. Conditional survival (CS) means that the patients' prognosis varies with survival time, meanwhile, relevant factors affecting the prognosis have a time-varying effect. The probability of survival assessed by CS1 increased year by year and the 1,2,3-year survival improved from 68.4% to 87.8%, while the postoperative actuarial OS decreased from 69.4% at 1 years to 36.9% at 3 years. CONCLUSIONS: In terms of CS, the estimated survival for ICC varies with the increase of survival time after excision. Patients who live longer were likely to live longer. At the same time, with the passage of time, the role of the original adverse factors of the tumor would gradually decrease. Conditional survival allows a more accurate assessment of ICC patients who did not undergo lymphadenectomy.

Preoperative prediction of intrahepatic cholangiocarcinoma lymph node metastasis by means of machine learning: a multicenter study in China.

BACKGROUND: Hepatectomy is currently the most effective modality for the treatment of intrahepatic cholangiocarcinoma (ICC). The status of the lymph nodes directly affects the choice of surgical method and the formulation of postoperative treatment plans. Therefore, a preoperative judgment of lymph node status is of great significance for patients diagnosed with this condition. Previous prediction models mostly adopted logistic regression modeling, and few relevant studies applied random forests in the prediction of ICC lymph node metastasis (LNM). METHODS: A total of 149 ICC patients who met clinical conditions were enrolled in the training group. Taking into account preoperative clinical data and imaging features, 21 indicators were included for analysis and modeling. Logistic regression was used to filter variables through multivariate analysis, and random forest regression was used to rank the importance of these variables through the use of algorithms. The model's prediction accuracy was assessed by the concordance index (C-index) and calibration curve and validated with external data. RESULT: Multivariate analysis shows that Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), and lymphadenopathy on imaging are independent risk factors for lymph node metastasis. The random forest algorithm identifies the top four risk factors as CEA, CA19-9, and lymphadenopathy on imaging and Aspartate Transaminase (AST). The predictive power of random forest is significantly better than the nomogram established by logistic regression in both the validation group and the training group (Area Under Curve reached 0.758 in the validation group). CONCLUSIONS: We constructed a random forest model for predicting lymph node metastasis that, compared with the traditional nomogram, has higher prediction accuracy and simultaneously plays an auxiliary role in imaging examinations.