Machine Learning Algorithm-Based Tumor-Stroma Ratio Can Stratify the Prognosis of Intrahepatic Cholangiocarcinoma.

INTRODUCTION

The study aimed to quantify tumor components through the machine learning algorithm and explore effective biomarkers to stratify the prognosis of intrahepatic cholangiocarcinoma (iCCA) after radical surgery.

METHODS

A cohort of 237 iCCA patients who underwent radical resection was recruited. The semiautomated pipeline was constructed to measure the tumor microenvironment components, including tumor, lymphocyte, and stromal cells, tumor-stroma ratio, and tumor-infiltrated lymphocytes ratio % were calculated. The overall survival (OS) and disease-free survival (DFS) were compared to evaluate their prognostic values. The predictive values for adjuvant chemotherapy were then explored.

RESULTS

The Kaplan-Meier analysis showed that high-stroma and low-tumor-infiltrated lymphocytes ratio % were associated with shorter DFS and OS, and the multivariable Cox analysis also verified the prognosis values of iCCA including DFS (hazard ratio: 1.59, 95% confidence interval: 1.10-2.30, P = 0.015) and OS (hazard ratio: 1.92, 95% confidence interval: 1.27-4.17, P < 0.001). The nomograms presented better performance than previous staging systems, including the 8th American Joint Committee on Cancer system and the Liver Cancer Study Group of Japan system. The low-stroma cohort was more likely to benefit from chemotherapy, including DFS and OS (P = 0.019 and P = 0.002).

CONCLUSIONS

Machine learning-based tumor-stroma ratio could serve as an effective prognostic biomarker for iCCA after radical surgery and potentially predict the therapeutic response of adjuvant chemotherapy.