Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning.

ABSTRACT:

Background

The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.

Methods

We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations.

Results

The models were developed and optimized on 373 iCCA patients from our center and demonstrated

SUBMITTER: Ding GY 

PROVIDER: S-EPMC11229270 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature