Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma.

doi:10.1089/thy.2024.0691

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Community

Back to search

Article

Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma.

Thyroid : official journal of the American Thyroid Association - 1 Jul 2025

Marion Ingrid, Schulz Stefan, Glasner Christina, Kather Jakob Nikolas, Truhn Daniel, Eckstein Markus, Mueller Celine, Fernandez Aurélie, Marquard Simone, Oliver Metzig Marie, Roth Wilfried, Gaida Matthias Martin, Strobl Stephanie, Wagner Daniel-Christoph, Schad Arno, Jesinghaus Moritz, Hartmann Nils, Musholt Thomas Johannes, Staubitz-Vernazza Julia I, Foersch Sebastian

Abstract excerpt

Background: Papillary thyroid carcinoma (PTC) is the most common malignant tumor of the endocrine system. BRAF mutations occur in 40-60%, panRAS mutations in 10-15%, and different gene fusion events such as RET fusions in 7-35% of these neoplasms. Artificial intelligence (AI) methods could be used to predict genetic changes from conventional histopathological slides. Methods: In this retrospective study, we used...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aged

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to BRAF gene

Share to RET gene

Share to Mutation

Share to Proto-Oncogene Proteins B-raf

Share to Thyroid Cancer, Papillary

Share to Thyroid Neoplasms

Source

Open in PubMed

Identifiers

PMID: 40607934
DOI: 10.1089/thy.2024.0691

Genes

BRAF
RET

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma.PMID 40607934Read ↗

Artificial Intelligence Detected the Relationship Between Nuclear Morphological Features and Molecular Abnormalities of Papillary Thyroid Carcinoma.Semantic proximity Read ↗

Artificial intelligence analysis of nuclear morphological features and molecular abnormalities of papillary thyroid carcinomaSemantic proximity Read ↗

Classifying driver mutations of papillary thyroid carcinoma on whole slide image: an automated workflow applying deep convolutional neural network.Semantic proximity Read ↗

Trends in AI-powered classification of thyroid neoplasms based on histopathology images, a systematic reviewSemantic proximity Read ↗

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.Semantic proximity Read ↗

Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features.Semantic proximity Read ↗

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.Semantic proximity Read ↗

PAI-WSIT: a Comprehensive Curated Resource for Cancerous Pathology With Deep LearningSemantic proximity Read ↗

Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.Semantic proximity Read ↗

The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups.Semantic proximity Read ↗

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathologySemantic proximity Read ↗

Advances in Image Processing and Pattern Recognition in Cancer Detection, Prediction, Diagnosis, and PrognosisSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.