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Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer

PURPOSE: To evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer. METHOD: Retrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology...

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Autores principales: Li, Meng-ru, Liu, Ming-zhu, Ge, Ya-qiong, Zhou, Ying, Wei, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350445/
https://www.ncbi.nlm.nih.gov/pubmed/34381719
http://dx.doi.org/10.3389/fonc.2021.696780
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author Li, Meng-ru
Liu, Ming-zhu
Ge, Ya-qiong
Zhou, Ying
Wei, Wei
author_facet Li, Meng-ru
Liu, Ming-zhu
Ge, Ya-qiong
Zhou, Ying
Wei, Wei
author_sort Li, Meng-ru
collection PubMed
description PURPOSE: To evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer. METHOD: Retrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology and complete preoperative images in our hospital, including 37 and 58 cases with mutant type and wild type BRCA, respectively (total: 95 cases). The enrolled patients’ routine CT features were analyzed by two radiologists. Then, ROIs were jointly determined through negotiation, and the ITK-SNAP software package was used for 3D outlining of the third-stage images of the primary tumor lesions and obtaining texture features. For routine CT features and texture features, Mann-Whitney U tests, single-factor logistic regression analysis, minimum redundancy, and maximum correlation were used for feature screening, and the performance of individual features was evaluated by ROC curves. Multivariate logistic regression analysis was used to further screen features, find independent predictors, and establish the prediction model. The established model’s diagnostic efficiency was evaluated by ROC curve analysis, and the histogram was obtained to conduct visual analysis of the prediction model. RESULTS: Among the routine CT features, the type of peritoneal metastasis, mesenteric involvement, and supradiaphragmatic lymph node enlargement were correlated with BRCA gene mutation (P < 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P > 0.05). Multivariate logistic regression analysis retained six features, including one routine CT feature and five texture features. Among them, the type of peritoneal metastasis was used as an independent predictor (P < 0.05), which had the highest diagnostic efficiency. Its AUC, accuracy, specificity, and sensitivity were 0.74, 0.79, 0.90, and 0.62, respectively. The prediction model based on the combination of routine CT features and texture features had an AUC of 0.86 (95% CI: 0.79–0.94) and accuracy, specificity, and sensitivity of 0.80, 0.76, and 0.81, respectively, indicating a better performance than that of any single feature. CONCLUSIONS: Both routine CT features and texture features had value for predicting the mutation state of the BRCA gene, but their predictive efficiency was low. When the two types of features were combined to establish a predictive model, the model’s predictive efficiency was significantly higher than that of independent features.
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spelling pubmed-83504452021-08-10 Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer Li, Meng-ru Liu, Ming-zhu Ge, Ya-qiong Zhou, Ying Wei, Wei Front Oncol Oncology PURPOSE: To evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer. METHOD: Retrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology and complete preoperative images in our hospital, including 37 and 58 cases with mutant type and wild type BRCA, respectively (total: 95 cases). The enrolled patients’ routine CT features were analyzed by two radiologists. Then, ROIs were jointly determined through negotiation, and the ITK-SNAP software package was used for 3D outlining of the third-stage images of the primary tumor lesions and obtaining texture features. For routine CT features and texture features, Mann-Whitney U tests, single-factor logistic regression analysis, minimum redundancy, and maximum correlation were used for feature screening, and the performance of individual features was evaluated by ROC curves. Multivariate logistic regression analysis was used to further screen features, find independent predictors, and establish the prediction model. The established model’s diagnostic efficiency was evaluated by ROC curve analysis, and the histogram was obtained to conduct visual analysis of the prediction model. RESULTS: Among the routine CT features, the type of peritoneal metastasis, mesenteric involvement, and supradiaphragmatic lymph node enlargement were correlated with BRCA gene mutation (P < 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P > 0.05). Multivariate logistic regression analysis retained six features, including one routine CT feature and five texture features. Among them, the type of peritoneal metastasis was used as an independent predictor (P < 0.05), which had the highest diagnostic efficiency. Its AUC, accuracy, specificity, and sensitivity were 0.74, 0.79, 0.90, and 0.62, respectively. The prediction model based on the combination of routine CT features and texture features had an AUC of 0.86 (95% CI: 0.79–0.94) and accuracy, specificity, and sensitivity of 0.80, 0.76, and 0.81, respectively, indicating a better performance than that of any single feature. CONCLUSIONS: Both routine CT features and texture features had value for predicting the mutation state of the BRCA gene, but their predictive efficiency was low. When the two types of features were combined to establish a predictive model, the model’s predictive efficiency was significantly higher than that of independent features. Frontiers Media S.A. 2021-07-26 /pmc/articles/PMC8350445/ /pubmed/34381719 http://dx.doi.org/10.3389/fonc.2021.696780 Text en Copyright © 2021 Li, Liu, Ge, Zhou and Wei 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
Li, Meng-ru
Liu, Ming-zhu
Ge, Ya-qiong
Zhou, Ying
Wei, Wei
Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer
title Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer
title_full Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer
title_fullStr Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer
title_full_unstemmed Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer
title_short Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer
title_sort assistance by routine ct features combined with 3d texture analysis in the diagnosis of brca gene mutation status in advanced epithelial ovarian cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350445/
https://www.ncbi.nlm.nih.gov/pubmed/34381719
http://dx.doi.org/10.3389/fonc.2021.696780
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