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Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma

Objective: To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI). Methods: Seventy-nine patients with 80 LPA and 29 patients with...

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Autores principales: Yi, Xiaoping, Guan, Xiao, Chen, Chen, Zhang, Youming, Zhang, Zhe, Li, Minghao, Liu, Peihua, Yu, Anze, Long, Xueying, Liu, Longfei, Chen, Bihong T, Zee, Chishing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171020/
https://www.ncbi.nlm.nih.gov/pubmed/30310515
http://dx.doi.org/10.7150/jca.26356
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author Yi, Xiaoping
Guan, Xiao
Chen, Chen
Zhang, Youming
Zhang, Zhe
Li, Minghao
Liu, Peihua
Yu, Anze
Long, Xueying
Liu, Longfei
Chen, Bihong T
Zee, Chishing
author_facet Yi, Xiaoping
Guan, Xiao
Chen, Chen
Zhang, Youming
Zhang, Zhe
Li, Minghao
Liu, Peihua
Yu, Anze
Long, Xueying
Liu, Longfei
Chen, Bihong T
Zee, Chishing
author_sort Yi, Xiaoping
collection PubMed
description Objective: To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI). Methods: Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study. Texture parameters were derived using imaging software (MaZda). Thirty texture features were selected and LPA was performed for the features selected. The number of positive features was used to predict results. Logistic multiple regression analysis was performed on the 30 texture features, and a predictive equation was created based on the coefficients obtained. Results: LPA yielded a misclassification rate of 19.39% in differentiating sPHEO from LPA. Our predictive model had an accuracy rate of 94.4% (102/108), with a sensitivity of 86.2% (25/29) and a specificity of 97.5% (77/79) for differentiation. When the number of positive features was greater than 8, the accuracy of prediction was 85.2% (92/108), with a sensitivity of 96.6% (28/29) and a specificity of 81% (64/79). Conclusions: Machine learning-based quantitative texture analysis of unenhanced CT may be a reliable quantitative method in differentiating sPHEO from LPA when AI is present.
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spelling pubmed-61710202018-10-11 Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma Yi, Xiaoping Guan, Xiao Chen, Chen Zhang, Youming Zhang, Zhe Li, Minghao Liu, Peihua Yu, Anze Long, Xueying Liu, Longfei Chen, Bihong T Zee, Chishing J Cancer Research Paper Objective: To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI). Methods: Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study. Texture parameters were derived using imaging software (MaZda). Thirty texture features were selected and LPA was performed for the features selected. The number of positive features was used to predict results. Logistic multiple regression analysis was performed on the 30 texture features, and a predictive equation was created based on the coefficients obtained. Results: LPA yielded a misclassification rate of 19.39% in differentiating sPHEO from LPA. Our predictive model had an accuracy rate of 94.4% (102/108), with a sensitivity of 86.2% (25/29) and a specificity of 97.5% (77/79) for differentiation. When the number of positive features was greater than 8, the accuracy of prediction was 85.2% (92/108), with a sensitivity of 96.6% (28/29) and a specificity of 81% (64/79). Conclusions: Machine learning-based quantitative texture analysis of unenhanced CT may be a reliable quantitative method in differentiating sPHEO from LPA when AI is present. Ivyspring International Publisher 2018-09-08 /pmc/articles/PMC6171020/ /pubmed/30310515 http://dx.doi.org/10.7150/jca.26356 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Yi, Xiaoping
Guan, Xiao
Chen, Chen
Zhang, Youming
Zhang, Zhe
Li, Minghao
Liu, Peihua
Yu, Anze
Long, Xueying
Liu, Longfei
Chen, Bihong T
Zee, Chishing
Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
title Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
title_full Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
title_fullStr Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
title_full_unstemmed Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
title_short Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma
title_sort adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced ct can effectively differentiate spheo from lipid-poor adrenal adenoma
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171020/
https://www.ncbi.nlm.nih.gov/pubmed/30310515
http://dx.doi.org/10.7150/jca.26356
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