Dataset Information

Direct image to subtype prediction for brain tumors using deep learning.

ABSTRACT:

Background

Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides.

Methods

We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients.

Results

We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively.

Conclusions

In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.

SUBMITTER: Hewitt KJ

PROVIDER: S-EPMC10724115 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

Direct image to subtype prediction for brain tumors using deep learning.

Hewitt Katherine J KJ Löffler Chiara M L CML Muti Hannah Sophie HS Berghoff Anna Sophie AS Eisenlöffel Christian C van Treeck Marko M Carrero Zunamys I ZI El Nahhas Omar S M OSM Veldhuizen Gregory P GP Weil Sophie S Saldanha Oliver Lester OL Bejan Laura L Millner Thomas O TO Brandner Sebastian S Brückmann Sascha S Kather Jakob Nikolas JN

Neuro-oncology advances 20230101 1

<h4>Background</h4>Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides.<h4>Methods</h4>We used weakly sup ...[more]

PMID: 38106649

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Dataset Information

Direct image to subtype prediction for brain tumors using deep learning.

Background

Methods

Results

Conclusions

Publications

Direct image to subtype prediction for brain tumors using deep learning.

Similar Datasets

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