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Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma
BACKGROUND: Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and ass...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667089/ https://www.ncbi.nlm.nih.gov/pubmed/34988174 http://dx.doi.org/10.21037/atm-21-5348 |
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author | Zhou, Lili Peng, Hong Ji, Qiang Li, Bo Pan, Lexin Chen, Feng Jiao, Zishan Wang, Yali Huang, Mengqian Liu, Gaifen Liu, Yaou Li, Wenbin |
author_facet | Zhou, Lili Peng, Hong Ji, Qiang Li, Bo Pan, Lexin Chen, Feng Jiao, Zishan Wang, Yali Huang, Mengqian Liu, Gaifen Liu, Yaou Li, Wenbin |
author_sort | Zhou, Lili |
collection | PubMed |
description | BACKGROUND: Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS: Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS: A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS: A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments. |
format | Online Article Text |
id | pubmed-8667089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-86670892022-01-04 Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma Zhou, Lili Peng, Hong Ji, Qiang Li, Bo Pan, Lexin Chen, Feng Jiao, Zishan Wang, Yali Huang, Mengqian Liu, Gaifen Liu, Yaou Li, Wenbin Ann Transl Med Original Article BACKGROUND: Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS: Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS: A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS: A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments. AME Publishing Company 2021-11 /pmc/articles/PMC8667089/ /pubmed/34988174 http://dx.doi.org/10.21037/atm-21-5348 Text en 2021 Annals of Translational Medicine. 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 Zhou, Lili Peng, Hong Ji, Qiang Li, Bo Pan, Lexin Chen, Feng Jiao, Zishan Wang, Yali Huang, Mengqian Liu, Gaifen Liu, Yaou Li, Wenbin Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma |
title | Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma |
title_full | Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma |
title_fullStr | Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma |
title_full_unstemmed | Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma |
title_short | Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma |
title_sort | radiomic signatures based on multiparametric mr images for predicting ki-67 index expression in medulloblastoma |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667089/ https://www.ncbi.nlm.nih.gov/pubmed/34988174 http://dx.doi.org/10.21037/atm-21-5348 |
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