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AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis. | LitMetric

AI Article Synopsis

  • The study focuses on creating a deep learning digital pathology tool for accurately detecting, segmenting, and classifying nuclei in cancer tissues, addressing challenges in quantifying nuclear morphology in histologic images.
  • This tool was trained on nucleus annotations to analyze H&E-stained slides from various cancer cohorts (BRCA, LUAD, PRAD), revealing significant differences in nuclear features like shape and size linked to genomic instability and cancer prognosis.
  • Results highlighted that certain nuclear characteristics, particularly in fibroblasts, were associated with patient survival outcomes and gene expression related to tumor biology, paving the way for better understanding of cancer biomarkers.

Article Abstract

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187064PMC
http://dx.doi.org/10.1038/s41698-024-00623-9DOI Listing

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