Category: Clinical care & management

Integrative multiomics-histopathology analysis for breast cancer classification

Yasha Ektefaie, William Yuan, Deborah A. Dillon, Nancy U. Lin, Jeffrey A. Golden, Isaac S. Kohane and Kun-Hsing Yu

npj. 2021. DOI: 10.1038/s41591-018-0300-7. DOI:10.1038/s41523-021-00357-y

Lymphocyte R value graph
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Could deep learning reveal connections between histology and multi-omics for breast cancer? 

Histopathologic evaluation of biopsy slides is a pivotal procedure in diagnosing and subtyping breast cancers, an indispensable step for the diagnosis and treatment of breast cancer. However, the connections between visual morphology and genetic statuses have never been systematically explored or interpreted.

  1. The authors associate deep learning image models from digitized histopathology slides with transcriptomic analyses to expose molecular and morphological profiles associated with hormone receptor status and genomic subtypes of breast cancer.
  2. Ektefaie et al. developed image classifiers for several classification tasks including hormone receptor (HR) status prediction in breast cancer patients using only hematoxylin-and-eosin slides. They then trained a regression model on RNA-seq profiles and identified important genes for the task of HR classification. The image classifiers did learn lymphocyte-specific morphological signals for the HR classification task.
  3. This work confirms the utility of deep learning-based image models in both clinical and research settings, through its capability to uncover connections between visual morphology and genetic statuses. This pipeline can be immediately used to examine the underlying biology of other breast cancer markers.

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Sabanayagam C, Xu D, (…), Wong TY.

Lancet Digit Health. 2020 DOI: 10.1016/S2589-7500(20)30063-7.

Kidney disease retinal photographs
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An Artificial Intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images.

  1. A retinal image DLA shows good performance in detecting chronic kidney disease.
  2. Combining DLAs with known risk factors for chronic kidney disease showed better accuracy than both used separately.
  3. This study linking the retina and the kidney underlies the feasibility of applying AI to retinal photography as a screening tool for chronic kidney disease in community populations.

Human-machine partnership with Artificial Intelligence for chest radiograph diagnosis

Patel BN, Rosenberg L, (...) Lungren M.

NPJ Digit Med. 2019. DOI: 10.1038/s41746-019-0189-7.

Chest radiograph diagnosis
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An investigation of a new AI technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms applied to chest radiographs for the diagnosis of pneumonia.

  1. Both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy of pneumonia than the human experts alone.
  2. When used in combination, the swarm-based technology and deep-learning technology outperformed either method alone.
  3. These findings have broad implications for the growth in clinical AI deployment and implementation strategies in future practice.

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