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Nat Commun: Deep learning helps histopathological identification

 Last Update: 2021-03-20
 Source: Internet
 Author: User

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At present, manual examination of histopathological sections is still the gold standard for evaluating malignant tumors, tumor subtypes, and cancer treatment options, but pathologists and oncologists are increasingly relying on molecular assays to guide personalized cancer treatment.

diagnosis

Recently, computational methods have made substantial progress in improving the accuracy and throughput of pathological diagnosis, prognosis, and genome prediction workflows.

Recently, researchers have proposed a method that uses human interpretable image features (HIFs) to predict clinically relevant molecular phenotypes from entire histopathological images.

Human Interpretable Image Features (HIFs) Human Interpretable Image Features (HIFs) Notes from 1.

The model outputs of cell and tissue types were combined into 607 HIFs, which quantified the specific and biologically relevant characteristics of the five cancer types .

Quantify the specific and relevant biological characteristics of five kinds of cancer types quantify the specific characteristics and the biological relevance of the five types of cancer these HIFs tumor microenvironment known markers related to these HIFs tumor microenvironment known markers related to immunity

In short, the study confirmed that the HIF-based method can provide a comprehensive, quantitative, and interpretable window to understand the composition and spatial structure of the tumor microenvironment.

Original source:

James A.

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