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Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning

Abstract

Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist’s judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly using hematoxylin-eosin (HE) staining whole-slide images (WSIs). WSIs from 467 patients were divided into three cohorts: the training cohort with 348 annotated WSIs, the testing cohort with 88 annotated WSIs, and the integration testing cohort with 31 original WSIs without tumor contour annotation. First, the DL models comprehensibly achieved tumor differentiation recognition with an F1 values of 0.8615 and 0.8977 for poorly differentiated adenocarcinoma (PDA) and well-differentiated adenocarcinoma (WDA) classes. Its ability to extract pathological features about the glandular structure formation, which is the key to distinguishing between PDA and WDA, increased the interpretability of the DL models. Second, the DL models achieved MSI status recognition with a patient-level accuracy of 86.36% directly from HE-stained WSIs in the testing cohort. Finally, the integrated end-to-end system achieved patient-level MSI recognition from original HE staining WSIs with an accuracy of 83.87% in the integration testing cohort with no tumor contour annotation. The proposed system, therefore, demonstrated high accuracy and interpretability, which can potentially promote the implementation of artificial intelligence healthcare.

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Fig. 1: Construction of the DL system for tumor and MSI diagnosis.
Fig. 2: Development and validation of DL systems for tumor differentiation grade recognition.
Fig. 3: Development and validation of DL systems for MSI status recognition.
Fig. 4: Integration testing of the DL system for tumor and MSI diagnosis in unannotated WSIs.

Data availability

The dataset was collected and held by the Beijing Cancer Hospital. Annotated image tile datasets are available for noncommercial use at https://zenodo.org/record/5155995#.YQoeccgl3RR. Other datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Code availability

Source codes are available at https://github.com/Benson0704/MSI_MSS_Prediction.

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Funding

This work was supported by the National Key Research and Development Program of China (2018YFC0910700), the Major Program of National Natural Science Foundation of China (91959205).

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Contributions

Conceptualization: Y.S., J.-F.J.; Methodology: F.S., J.L., X.Z., and B.W.; Formal analysis and investigation: F.S., J.L., X.Z., B.W., Y.H., and Y.S.; Writing, review, and/or revision of the manuscript: F.S., J.L., X.Z., B.W., Y.H., Y.S., and J.-F.J.; Funding acquisition: JL, YS; Resources: Y.S., J.-F.J.; Supervision: Y.S., J.L., J.-F.J.

Corresponding authors

Correspondence to Yu Sun or Jiafu Ji.

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The authors declare no competing interests.

Ethics Approval/Consent to Participate

The study was performed in accordance with relevant guidelines and regulations and approved by the institutional review board at Beijing Cancer Hospital. All patients provided written informed consent to participate in the institutional review board-approved protocol.

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Su, F., Li, J., Zhao, X. et al. Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning. Lab Invest 102, 641–649 (2022). https://doi.org/10.1038/s41374-022-00742-6

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  • DOI: https://doi.org/10.1038/s41374-022-00742-6

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