期刊
NATURE MACHINE INTELLIGENCE
卷 3, 期 4, 页码 355-366出版社
NATURE PORTFOLIO
DOI: 10.1038/s42256-021-00303-4
关键词
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资金
- Charite Institute of Pathology, Berlin
- Technical University of Berlin
- Human Frontier Science Program (HFSP) Young Investigator Grant
- Einstein Foundation Berlin
- German Research Foundation [DFG SFB-TR84]
- German Consortium for Translational Cancer Research (DKTK)
- German Ministry for Education and Research (BMBF) within the Berlin Institute for the Foundations of Learning and Data (BIFOLD) [01IS18025D, 01IS18037E]
- German Ministry for Education and Research (BMBF) within the clinical mass spectrometry centre MSTARS [031L0220A]
- German Ministry for Education and Research (BMBF) within the CompLS Patho234 [031L0207B]
- European Research Council under Horizon 2020 of the EU Framework Programme for Research and Innovation [647257]
- Ministry of Education AcRF Tier 2 grant [MOE2016-T2-2-154]
- SUTD grant [SGPAIRS1811]
- University Medical Center Hamburg-Eppendorf
- Institute of Information and Communications Technology Planning and Evaluation (IITP) - Korean government [2017-0-00451, 2019-0-00079]
- German Ministry for Education and Research (BMBF) [01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D, 01IS18037A]
- German Research Foundation (DFG) under grant Math+ [EXC 2046/1, 390685689]
- European Research Council (ERC) [647257] Funding Source: European Research Council (ERC)
The study introduces an explainable machine-learning approach for integrated profiling of morphological, molecular, and clinical features from breast cancer histology. By detecting cancer cells, predicting molecular features, and assessing the link between morphological and molecular properties, the approach aims to promote basic cancer research and precision medicine.
Recent advances in cancer research and diagnostics largely rely on new developments in microscopic or molecular profiling techniques, offering high levels of detail with respect to either spatial or molecular features, but usually not both. Here, we present an explainable machine-learning approach for the integrated profiling of morphological, molecular and clinical features from breast cancer histology. First, our approach allows for the robust detection of cancer cells and tumour-infiltrating lymphocytes in histological images, providing precise heatmap visualizations explaining the classifier decisions. Second, molecular features, including DNA methylation, gene expression, copy number variations, somatic mutations and proteins are predicted from histology. Molecular predictions reach balanced accuracies up to 78%, whereas accuracies of over 95% can be achieved for subgroups of patients. Finally, our explainable AI approach allows assessment of the link between morphological and molecular cancer properties. The resulting computational multiplex-histology analysis can help promote basic cancer research and precision medicine through an integrated diagnostic scoring of histological, clinical and molecular features. Cancers are complex diseases that are increasingly studied using a diverse set of omics data. At the same time, histological images show the interaction of cells, which is not visible with bulk omics methods. Binder and colleagues present a method to learn from both kinds of data, such that molecular markers can be associated with visible patterns in the tissue samples and be used for more accurate breast cancer diagnosis.
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