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Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

OBJECTIVE: The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma. METHODS: Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted im...

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Autores principales: Shao, Xian, An, Li, Liu, Hui, Feng, Hui, Zheng, Liyun, Dai, Yongming, Yu, Bin, Zhang, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036957/
https://www.ncbi.nlm.nih.gov/pubmed/35480091
http://dx.doi.org/10.3389/fonc.2022.851677
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author Shao, Xian
An, Li
Liu, Hui
Feng, Hui
Zheng, Liyun
Dai, Yongming
Yu, Bin
Zhang, Jin
author_facet Shao, Xian
An, Li
Liu, Hui
Feng, Hui
Zheng, Liyun
Dai, Yongming
Yu, Bin
Zhang, Jin
author_sort Shao, Xian
collection PubMed
description OBJECTIVE: The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma. METHODS: Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons. RESULTS: Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829). CONCLUSION: The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.
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spelling pubmed-90369572022-04-26 Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings Shao, Xian An, Li Liu, Hui Feng, Hui Zheng, Liyun Dai, Yongming Yu, Bin Zhang, Jin Front Oncol Oncology OBJECTIVE: The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma. METHODS: Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons. RESULTS: Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829). CONCLUSION: The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma. Frontiers Media S.A. 2022-04-05 /pmc/articles/PMC9036957/ /pubmed/35480091 http://dx.doi.org/10.3389/fonc.2022.851677 Text en Copyright © 2022 Shao, An, Liu, Feng, Zheng, Dai, Yu and Zhang 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 Oncology
Shao, Xian
An, Li
Liu, Hui
Feng, Hui
Zheng, Liyun
Dai, Yongming
Yu, Bin
Zhang, Jin
Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings
title Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings
title_full Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings
title_fullStr Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings
title_full_unstemmed Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings
title_short Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings
title_sort cervical carcinoma: evaluation using diffusion mri with a fractional order calculus model and its correlation with histopathologic findings
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036957/
https://www.ncbi.nlm.nih.gov/pubmed/35480091
http://dx.doi.org/10.3389/fonc.2022.851677
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