Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning
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Published:2024-07-08
Issue:1
Volume:22
Page:
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ISSN:1741-7015
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Container-title:BMC Medicine
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language:en
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Short-container-title:BMC Med
Author:
Ding Guang-Yu,Tan Wei-Min,Lin You-Pei,Ling Yu,Huang Wen,Zhang Shu,Shi Jie-Yi,Luo Rong-Kui,Ji Yuan,Wang Xiao-Ying,Zhou Jian,Fan Jia,Cai Mu-Yan,Yan Bo,Gao Qiang
Abstract
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 consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration.
Conclusions
We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.
Graphical Abstract
Funder
National Natural Science Foundation of China Science and Technology Commission of Shanghai Municipality Natural Science Foundation of Fujian Province Natural Science Foundation of Shanghai Municipality
Publisher
Springer Science and Business Media LLC
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