Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypo...

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Autores principales:	Urszula Smyczynska, Szymon Grabia, Zuzanna Nowicka, Anna Papis-Ubych, Robert Bibik, Tomasz Latusek, Tomasz Rutkowski, Jacek Fijuth, Wojciech Fendler, Bartlomiej Tomasik
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	radiomics radiation-induced hypothyroidism NTCP head neck cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/8323964c7a5042faa47514bc6845f293
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spelling	oai:doaj.org-article:8323964c7a5042faa47514bc6845f2932021-11-11T15:36:09ZPrediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models10.3390/cancers132155842072-6694https://doaj.org/article/8323964c7a5042faa47514bc6845f2932021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5584https://doaj.org/toc/2072-6694State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (<i>p</i> > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.Urszula SmyczynskaSzymon GrabiaZuzanna NowickaAnna Papis-UbychRobert BibikTomasz LatusekTomasz RutkowskiJacek FijuthWojciech FendlerBartlomiej TomasikMDPI AGarticleradiomicsradiation-induced hypothyroidismNTCPheadneck cancerNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5584, p 5584 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiomics radiation-induced hypothyroidism NTCP head neck cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	radiomics radiation-induced hypothyroidism NTCP head neck cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Urszula Smyczynska Szymon Grabia Zuzanna Nowicka Anna Papis-Ubych Robert Bibik Tomasz Latusek Tomasz Rutkowski Jacek Fijuth Wojciech Fendler Bartlomiej Tomasik Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
description	State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (<i>p</i> > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.
format	article
author	Urszula Smyczynska Szymon Grabia Zuzanna Nowicka Anna Papis-Ubych Robert Bibik Tomasz Latusek Tomasz Rutkowski Jacek Fijuth Wojciech Fendler Bartlomiej Tomasik
author_facet	Urszula Smyczynska Szymon Grabia Zuzanna Nowicka Anna Papis-Ubych Robert Bibik Tomasz Latusek Tomasz Rutkowski Jacek Fijuth Wojciech Fendler Bartlomiej Tomasik
author_sort	Urszula Smyczynska
title	Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
title_short	Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
title_full	Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
title_fullStr	Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
title_full_unstemmed	Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
title_sort	prediction of radiation-induced hypothyroidism using radiomic data analysis does not show superiority over standard normal tissue complication models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8323964c7a5042faa47514bc6845f293
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Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

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