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Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images

Background and objectives: Telomerase reverse transcriptase (TERT) promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting TERT promoter mutation is thus necessary for the prognostic stratification of thyroid cancer p...

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Autores principales: Kim, Jinhee, Ko, Seokhwan, Kim, Moonsik, Park, Nora Jee-Young, Han, Hyungsoo, Cho, Junghwan, Park, Ji Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055833/
https://www.ncbi.nlm.nih.gov/pubmed/36984536
http://dx.doi.org/10.3390/medicina59030536
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author Kim, Jinhee
Ko, Seokhwan
Kim, Moonsik
Park, Nora Jee-Young
Han, Hyungsoo
Cho, Junghwan
Park, Ji Young
author_facet Kim, Jinhee
Ko, Seokhwan
Kim, Moonsik
Park, Nora Jee-Young
Han, Hyungsoo
Cho, Junghwan
Park, Ji Young
author_sort Kim, Jinhee
collection PubMed
description Background and objectives: Telomerase reverse transcriptase (TERT) promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting TERT promoter mutation is thus necessary for the prognostic stratification of thyroid cancer patients. Materials and Methods: In this study, we evaluate TERT promoter mutation status in thyroid cancer through the deep learning approach using histologic images. Our analysis included 13 consecutive surgically resected thyroid cancers with TERT promoter mutations (either C228T or C250T) and 12 randomly selected surgically resected thyroid cancers with a wild-type TERT promoter. Our deep learning model was created using a two-step cascade approach. First, tumor areas were identified using convolutional neural networks (CNNs), and then TERT promoter mutations within tumor areas were predicted using the CNN–recurrent neural network (CRNN) model. Results: Using the hue–saturation–value (HSV)-strong color transformation scheme, the overall experiment results show 99.9% sensitivity and 60% specificity (improvements of approximately 25% and 37%, respectively, compared to image normalization as a baseline model) in predicting TERT mutations. Conclusions: Highly sensitive screening for TERT promoter mutations is possible using histologic image analysis based on deep learning. This approach will help improve the classification of thyroid cancer patients according to the biologic behavior of tumors.
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spelling pubmed-100558332023-03-30 Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images Kim, Jinhee Ko, Seokhwan Kim, Moonsik Park, Nora Jee-Young Han, Hyungsoo Cho, Junghwan Park, Ji Young Medicina (Kaunas) Article Background and objectives: Telomerase reverse transcriptase (TERT) promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting TERT promoter mutation is thus necessary for the prognostic stratification of thyroid cancer patients. Materials and Methods: In this study, we evaluate TERT promoter mutation status in thyroid cancer through the deep learning approach using histologic images. Our analysis included 13 consecutive surgically resected thyroid cancers with TERT promoter mutations (either C228T or C250T) and 12 randomly selected surgically resected thyroid cancers with a wild-type TERT promoter. Our deep learning model was created using a two-step cascade approach. First, tumor areas were identified using convolutional neural networks (CNNs), and then TERT promoter mutations within tumor areas were predicted using the CNN–recurrent neural network (CRNN) model. Results: Using the hue–saturation–value (HSV)-strong color transformation scheme, the overall experiment results show 99.9% sensitivity and 60% specificity (improvements of approximately 25% and 37%, respectively, compared to image normalization as a baseline model) in predicting TERT mutations. Conclusions: Highly sensitive screening for TERT promoter mutations is possible using histologic image analysis based on deep learning. This approach will help improve the classification of thyroid cancer patients according to the biologic behavior of tumors. MDPI 2023-03-09 /pmc/articles/PMC10055833/ /pubmed/36984536 http://dx.doi.org/10.3390/medicina59030536 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Jinhee
Ko, Seokhwan
Kim, Moonsik
Park, Nora Jee-Young
Han, Hyungsoo
Cho, Junghwan
Park, Ji Young
Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
title Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
title_full Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
title_fullStr Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
title_full_unstemmed Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
title_short Deep Learning Prediction of TERT Promoter Mutation Status in Thyroid Cancer Using Histologic Images
title_sort deep learning prediction of tert promoter mutation status in thyroid cancer using histologic images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055833/
https://www.ncbi.nlm.nih.gov/pubmed/36984536
http://dx.doi.org/10.3390/medicina59030536
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