Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma
Thyroid, vol. 35, pp. 771–780
Abstract
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 two independent cohorts of patients with PTC, totaling 662 cases for the establishment of our AI pipeline. The Cancer Genome Atlas cohort (496 cases) served as the developmental cohort, while the Mainz cohort (166 cases) served as an independent external test cohort. BRAF, panRAS, and fusion status was determined for all of these patients as target variables. Vision Transformer was trained on digitized annotated hematoxylin and eosin-stained slides for the presence of these alterations. Highest probability image tiles were used to identify new morphological criteria associated with the genetic changes. Results:The trained model resulted in an area under the receiver operating characteristic curve of 0.882 (confidence interval 0.829–0.931) for BRAF, 0.876 (0.822–0.927) for panRAS, and 0.858 (0.801–0.912) for gene fusions. Accuracy was 79.3% (72.7–85.8%) for BRAF, 89.3% (84.2–94.0%) for panRAS, and 84.7% (78.8–90.2%) for gene fusions. The performance on the validation set was almost identical to that on the test set. Analyzing the highest predictive tiles, novel morphological criteria for fusion-associated PTC could be discovered. Conclusions:Our study demonstrates that predicting genetic alterations in digitized histopathological slides using AI is feasible in patients with PTC. Our model showed high accuracy in predicting these changes, making it potentially suitable for pre-screening. Explainability approaches uncovered previously undescribed morphological patterns associated with certain genotypes. Providing pathologists with these AI-based features could improve their accuracy. Assuming further positive prospective validation, this discovery could contribute to a deeper understanding of PTC.
Authors 18
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden
Affiliation as printed
Medical Faculty Carl Gustav Carus, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
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Affiliation as printed
Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
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Friedrich-Alexander-Universität Erlangen-Nürnberg · Universitätsklinikum Erlangen
Affiliation as printed
Bavarian Cancer Research Center (Bayerisches Zentrum für Krebsforschung, BZKF), Erlangen, Germany
Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz · Translationale Onkologie an der Universitätsmedizin der Johannes Gutenberg-Universität Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
TRON, Translational Oncology at the University Medical Center, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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Philipps University of Marburg
Affiliation as printed
Institute of Pathology, Phillips University Marburg und University Hospital Marburg, Marburg, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Department of General, Visceral and Transplantation Surgery, Section Endocrine Surgery, University Medical Center Mainz, Mainz, Germany
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Sebastian Foersch corresponding
University Medical Center of the Johannes Gutenberg University Mainz
Affiliation as printed
Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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