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Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center

Accurate classification of laryngeal cancer is a critical step for diagnosis and appropriate treatment. Radiomics is a rapidly advancing field in medical image processing that uses various algorithms to extract many quantitative features from radiological images. The high dimensional features extrac...

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Detalles Bibliográficos
Autores principales: Rao, Divya, Koteshwara, Prakashini, Singh, Rohit, Jagannatha, Vijayananda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235219/
https://www.ncbi.nlm.nih.gov/pubmed/37275092
http://dx.doi.org/10.1007/s12070-022-03239-2
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author Rao, Divya
Koteshwara, Prakashini
Singh, Rohit
Jagannatha, Vijayananda
author_facet Rao, Divya
Koteshwara, Prakashini
Singh, Rohit
Jagannatha, Vijayananda
author_sort Rao, Divya
collection PubMed
description Accurate classification of laryngeal cancer is a critical step for diagnosis and appropriate treatment. Radiomics is a rapidly advancing field in medical image processing that uses various algorithms to extract many quantitative features from radiological images. The high dimensional features extracted tend to cause overfitting and increase the complexity of the classification model. Thereby, feature selection plays an integral part in selecting relevant features for the classification problem. In this study, we explore the predictive capabilities of radiomics on Computed Tomography (CT) images with the incidence of laryngeal cancer to predict the histopathological grade and T stage of the tumour. Working with a pilot dataset of 20 images, an experienced radiologist carefully annotated the supraglottic lesions in the three-dimensional plane. Over 280 radiomic features that quantify the shape, intensity and texture were extracted from each image. Machine learning classifiers were built and tested to predict the stage and grade of the malignant tumour based on the calculated radiomic features. To investigate if radiomic features extracted from CT images can be used for the classification of laryngeal tumours. Out of 280 features extracted from every image in the dataset, it was found that 24 features are potential classifiers of laryngeal tumour stage and 12 radiomic features are good classifiers of histopathological grade of the laryngeal tumor. The novelty of this paper lies in the ability to create these classifiers before the surgical biopsy procedure, giving the clinician valuable, timely information.
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spelling pubmed-102352192023-06-03 Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center Rao, Divya Koteshwara, Prakashini Singh, Rohit Jagannatha, Vijayananda Indian J Otolaryngol Head Neck Surg Original Article Accurate classification of laryngeal cancer is a critical step for diagnosis and appropriate treatment. Radiomics is a rapidly advancing field in medical image processing that uses various algorithms to extract many quantitative features from radiological images. The high dimensional features extracted tend to cause overfitting and increase the complexity of the classification model. Thereby, feature selection plays an integral part in selecting relevant features for the classification problem. In this study, we explore the predictive capabilities of radiomics on Computed Tomography (CT) images with the incidence of laryngeal cancer to predict the histopathological grade and T stage of the tumour. Working with a pilot dataset of 20 images, an experienced radiologist carefully annotated the supraglottic lesions in the three-dimensional plane. Over 280 radiomic features that quantify the shape, intensity and texture were extracted from each image. Machine learning classifiers were built and tested to predict the stage and grade of the malignant tumour based on the calculated radiomic features. To investigate if radiomic features extracted from CT images can be used for the classification of laryngeal tumours. Out of 280 features extracted from every image in the dataset, it was found that 24 features are potential classifiers of laryngeal tumour stage and 12 radiomic features are good classifiers of histopathological grade of the laryngeal tumor. The novelty of this paper lies in the ability to create these classifiers before the surgical biopsy procedure, giving the clinician valuable, timely information. Springer India 2022-11-24 2023-06 /pmc/articles/PMC10235219/ /pubmed/37275092 http://dx.doi.org/10.1007/s12070-022-03239-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Rao, Divya
Koteshwara, Prakashini
Singh, Rohit
Jagannatha, Vijayananda
Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center
title Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center
title_full Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center
title_fullStr Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center
title_full_unstemmed Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center
title_short Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center
title_sort exploring radiomics for classification of supraglottic tumors: a pilot study in a tertiary care center
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235219/
https://www.ncbi.nlm.nih.gov/pubmed/37275092
http://dx.doi.org/10.1007/s12070-022-03239-2
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