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Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory...

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Autores principales: Maggio, Roberta, Messina, Filippo, D’Arrigo, Benedetta, Maccagno, Giacomo, Lardo, Pina, Palmisano, Claudia, Poggi, Maurizio, Monti, Salvatore, Matarazzo, Iolanda, Laghi, Andrea, Pugliese, Giuseppe, Stigliano, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9248203/
https://www.ncbi.nlm.nih.gov/pubmed/35784576
http://dx.doi.org/10.3389/fendo.2022.873189
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author Maggio, Roberta
Messina, Filippo
D’Arrigo, Benedetta
Maccagno, Giacomo
Lardo, Pina
Palmisano, Claudia
Poggi, Maurizio
Monti, Salvatore
Matarazzo, Iolanda
Laghi, Andrea
Pugliese, Giuseppe
Stigliano, Antonio
author_facet Maggio, Roberta
Messina, Filippo
D’Arrigo, Benedetta
Maccagno, Giacomo
Lardo, Pina
Palmisano, Claudia
Poggi, Maurizio
Monti, Salvatore
Matarazzo, Iolanda
Laghi, Andrea
Pugliese, Giuseppe
Stigliano, Antonio
author_sort Maggio, Roberta
collection PubMed
description New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >−275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.
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spelling pubmed-92482032022-07-02 Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan Maggio, Roberta Messina, Filippo D’Arrigo, Benedetta Maccagno, Giacomo Lardo, Pina Palmisano, Claudia Poggi, Maurizio Monti, Salvatore Matarazzo, Iolanda Laghi, Andrea Pugliese, Giuseppe Stigliano, Antonio Front Endocrinol (Lausanne) Endocrinology New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >−275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9248203/ /pubmed/35784576 http://dx.doi.org/10.3389/fendo.2022.873189 Text en Copyright © 2022 Maggio, Messina, D’Arrigo, Maccagno, Lardo, Palmisano, Poggi, Monti, Matarazzo, Laghi, Pugliese and Stigliano https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Maggio, Roberta
Messina, Filippo
D’Arrigo, Benedetta
Maccagno, Giacomo
Lardo, Pina
Palmisano, Claudia
Poggi, Maurizio
Monti, Salvatore
Matarazzo, Iolanda
Laghi, Andrea
Pugliese, Giuseppe
Stigliano, Antonio
Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan
title Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan
title_full Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan
title_fullStr Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan
title_full_unstemmed Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan
title_short Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan
title_sort machine learning-based texture analysis in the characterization of cortisol secreting vs. non-secreting adrenocortical incidentalomas in ct scan
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9248203/
https://www.ncbi.nlm.nih.gov/pubmed/35784576
http://dx.doi.org/10.3389/fendo.2022.873189
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