Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images

Abstract

Intrahepatic cholangiocarcinoma (ICC) is cancer that originates from the liver's secondary ductal epithelium or branch. Due to the lack of early-stage clinical symptoms and very high mortality, the 5-year postoperative survival rate is only about 35%. A critical step to improve patients' survival is accurately predicting their survival status and giving appropriate treatment. The tumor microenvironment of ICC is the immediate environment on which the tumor cell growth depends. The differentiation of tumor glands, the stroma status, and the tumor-infiltrating lymphocytes in such environments are strictly related to the tumor progress. It is crucial to develop a computerized system for characterizing the tumor environment. This work aims to develop the quantitative histomorphological features that describe lymphocyte density distribution at the cell level and the different components at the tumor's tissue level in H&E-stained whole slide images (WSIs). The goal is to explore whether these features could stratify patients' survival. This study comprised of 127 patients diagnosed with ICC after surgery, where 78 cases were randomly chosen as the modeling set, and the rest of the 49 cases were testing set. Deep learning-based models were developed for tissue segmentation and lymphocyte detection in the WSIs. A total of 107-dimensional features, including different type of graph features on the WSIs were extracted by exploring the histomorphological patterns of these identified tumor tissue and lymphocytes. The top 3 discriminative features were chosen with the mRMR algorithm via 5-fold cross-validation to predict the patient's survival. The model's performance was evaluated on the independent testing set, which achieved an AUC of 0.6818 and the log-rank test p-value of 0.03. The Cox multivariable test was used to control the TNM staging, γ-Glutamytransferase, and the Peritumoral Glisson's Sheath Invasion. It showed that our model could independently predict survival risk with a p-value of 0.048 and HR (95% confidence interval) of 2.90 (1.01-8.32). These results indicated that the composition in tissue-level and global arrangement of lymphocytes in the cell-level could distinguish ICC patients' survival risk.

Keywords: Deep learning; Disease-free survival; Intrahepatic cholangiocarcinoma; Overall survival; Survival analysis; Tumor-infiltrating lymphocytes.