Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR

BackgroundPancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.Materials and MethodsTotally 48 patients but 51 lesions with a pathological tumor...

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Autores principales:	Wei Li, Chao Xu, Zhaoxiang Ye
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	grade risk radiomic features MR prediction PNETs Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/739d9592cfaf48daa909e022f734d747
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spelling	oai:doaj.org-article:739d9592cfaf48daa909e022f734d7472021-11-17T04:28:30ZPrediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR2234-943X10.3389/fonc.2021.758062https://doaj.org/article/739d9592cfaf48daa909e022f734d7472021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.758062/fullhttps://doaj.org/toc/2234-943XBackgroundPancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.Materials and MethodsTotally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample t-test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model.ResultsNo significant difference between G1 and G2 groups (P > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=0.023, OR = 0.621, 95% CI: 0.433–0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695.ConclusionsThe maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.Wei LiChao XuZhaoxiang YeFrontiers Media S.A.articlegrade riskradiomic featuresMRpredictionPNETsNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	grade risk radiomic features MR prediction PNETs Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	grade risk radiomic features MR prediction PNETs Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Wei Li Chao Xu Zhaoxiang Ye Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR
description	BackgroundPancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.Materials and MethodsTotally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample t-test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model.ResultsNo significant difference between G1 and G2 groups (P > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=0.023, OR = 0.621, 95% CI: 0.433–0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695.ConclusionsThe maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.
format	article
author	Wei Li Chao Xu Zhaoxiang Ye
author_facet	Wei Li Chao Xu Zhaoxiang Ye
author_sort	Wei Li
title	Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR
title_short	Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR
title_full	Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR
title_fullStr	Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR
title_full_unstemmed	Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR
title_sort	prediction of pancreatic neuroendocrine tumor grading risk based on quantitative radiomic analysis of mr
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/739d9592cfaf48daa909e022f734d747
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Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR

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