A Novel Prognostic Scoring System of Intrahepatic Cholangiocarcinoma With Machine Learning Basing on Real-World Data

Abstract

Background and ObjectivesCurrently, the prognostic performance of the staging systems proposed by the 8th edition of the American Joint Committee on Cancer (AJCC 8th) and the Liver Cancer Study Group of Japan (LCSGJ) in resectable intrahepatic cholangiocarcinoma (ICC) remains controversial. The aim of this study was to use machine learning techniques to modify existing ICC staging strategies based on clinical data and to demonstrate the accuracy and discrimination capacity in prognostic prediction.Patients and MethodsThis is a retrospective study based on 1,390 patients who underwent surgical resection for ICC at Eastern Hepatobiliary Surgery Hospital from 2007 to 2015. External validation was performed for patients from 2015 to 2017. The ensemble of three machine learning algorithms was used to select the most important prognostic factors and stepwise Cox regression was employed to derive a modified scoring system. The discriminative ability and predictive accuracy were assessed using the Concordance Index (C-index) and Brier Score (BS). The results were externally validated through a cohort of 42 patients operated on from the same institution.ResultsSix independent prognosis factors were selected and incorporated in the modified scoring system, including carcinoembryonic antigen, carbohydrate antigen 19-9, alpha-fetoprotein, prealbumin, T and N of ICC staging category in 8th edition of AJCC. The proposed scoring system showed a more favorable discriminatory ability and model performance than the AJCC 8th and LCSGJ staging systems, with a higher C-index of 0.693 (95% CI, 0.663–0.723) in the internal validation cohort and 0.671 (95% CI, 0.602–0.740) in the external validation cohort, which was then confirmed with lower BS (0.103 in internal validation cohort and 0.169 in external validation cohort). Meanwhile, machine learning techniques for variable selection together with stepwise Cox regression for survival analysis shows a better prognostic accuracy than using stepwise Cox regression method only.ConclusionsThis study put forward a modified ICC scoring system based on prognosis factors selection incorporated with machine learning, for individualized prognosis evaluation in patients with ICC.

Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study

“… 6 – 9 , 18 – 44 Twenty-six studies 6 – 9 , 18 – 31 , 33 – 38 , 42 , 44 developed their own original prediction models (Supplemental Table 1, http://links.lww.com/AOSO/A246 ), and 5 studies 32 , 39 – 41 , 43 conducted external validations on previously published models ( Table 1 ). Eighteen studies (69%) published nomograms, 8 , 18 , 19 , 21 – 30 , 33 , 36 , 37 , 42 , 44 7 studies (27%) published scoring systems, 6 , 9 , 20 , 31 , 34 , 35 , 38 and 1 study (4%) published an online calculator. 7 All 26 original models were developed using a retrospective cohort, and 9 studies (35%) used single-center data to develop the models.…”

Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

“…While most machine learning-based models have been applied for cancer diagnosis and risk assessment, their application in survival prediction has been limited 28 . Furthermore, most machine learning-based survival analyses have been based on gene expression data from databases such as The Cancer Genome Atlas (TCGA) 18 , 29 or multi-omics data 30 , with few studies utilizing high-dimensional real-world survival data 31 , 32 , thus limiting their applicability to the current practice. Recent research by Abuhelwa et al 10 demonstrated the feasibility and effectiveness of machine learning-based approaches for survival prediction in urothelial cancer patients treated with atezolizumab.…”

Application of AI on cholangiocarcinoma

“…Preoperative biomarkers, including CA 19-9, CRP, and CAR (ratio between CRP and albumin), were used for predicting OS and RFS (91). on XGBoost, RF, and GBDT to evaluate the prognosis by biomarkers, with a C-index of 0.693 (94). The psoas muscle index, defined as the area of the psoas muscle at the L3 vertebra level divided by the squared body height, combined with the features of tumor burden and hepatic reserve in ANN, with an AUC of 0.89 in the 1-year survival prediction in ICC, was significantly higher than the Fudan score (95,96).…”