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Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study
BACKGROUND: The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma. METHODS: A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I we...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929424/ https://www.ncbi.nlm.nih.gov/pubmed/36819280 http://dx.doi.org/10.21037/qims-22-689 |
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author | Ouyang, Zhi-Qiang He, Shao-Nan Zeng, Yi-Zhen Zhu, Yun Ling, Bing-Bing Sun, Xue-Jin Gu, He-Yi He, Bo Han, Dan Lu, Yi |
author_facet | Ouyang, Zhi-Qiang He, Shao-Nan Zeng, Yi-Zhen Zhu, Yun Ling, Bing-Bing Sun, Xue-Jin Gu, He-Yi He, Bo Han, Dan Lu, Yi |
author_sort | Ouyang, Zhi-Qiang |
collection | PubMed |
description | BACKGROUND: The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma. METHODS: A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma. RESULTS: Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753–0.881), 0.822 (95% CI: 0.727–0.916), and 0.845 (95% CI: 0.708–0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. CONCLUSIONS: The proposed contrast enhanced magnetic resonance imaging (MRI)–based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma. |
format | Online Article Text |
id | pubmed-9929424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-99294242023-02-16 Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study Ouyang, Zhi-Qiang He, Shao-Nan Zeng, Yi-Zhen Zhu, Yun Ling, Bing-Bing Sun, Xue-Jin Gu, He-Yi He, Bo Han, Dan Lu, Yi Quant Imaging Med Surg Original Article BACKGROUND: The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma. METHODS: A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma. RESULTS: Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753–0.881), 0.822 (95% CI: 0.727–0.916), and 0.845 (95% CI: 0.708–0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. CONCLUSIONS: The proposed contrast enhanced magnetic resonance imaging (MRI)–based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma. AME Publishing Company 2023-01-10 2023-02-01 /pmc/articles/PMC9929424/ /pubmed/36819280 http://dx.doi.org/10.21037/qims-22-689 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Ouyang, Zhi-Qiang He, Shao-Nan Zeng, Yi-Zhen Zhu, Yun Ling, Bing-Bing Sun, Xue-Jin Gu, He-Yi He, Bo Han, Dan Lu, Yi Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study |
title | Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study |
title_full | Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study |
title_fullStr | Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study |
title_full_unstemmed | Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study |
title_short | Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study |
title_sort | contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of ki-67 in meningioma: a two-center study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929424/ https://www.ncbi.nlm.nih.gov/pubmed/36819280 http://dx.doi.org/10.21037/qims-22-689 |
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