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An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas
OBJECTIVES: This study investigated whether radiomic features can improve the prediction accuracy for tumor recurrence over clinicopathological features and if these features can be used to identify high-risk patients requiring adjuvant radiotherapy (ART) in WHO grade 2 meningiomas. METHODS: Preoper...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9396861/ https://www.ncbi.nlm.nih.gov/pubmed/35996160 http://dx.doi.org/10.1186/s13014-022-02090-7 |
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author | Park, Chae Jung Choi, Seo Hee Eom, Jihwan Byun, Hwa Kyung Ahn, Sung Soo Chang, Jong Hee Kim, Se Hoon Lee, Seung-Koo Park, Yae Won Yoon, Hong In |
author_facet | Park, Chae Jung Choi, Seo Hee Eom, Jihwan Byun, Hwa Kyung Ahn, Sung Soo Chang, Jong Hee Kim, Se Hoon Lee, Seung-Koo Park, Yae Won Yoon, Hong In |
author_sort | Park, Chae Jung |
collection | PubMed |
description | OBJECTIVES: This study investigated whether radiomic features can improve the prediction accuracy for tumor recurrence over clinicopathological features and if these features can be used to identify high-risk patients requiring adjuvant radiotherapy (ART) in WHO grade 2 meningiomas. METHODS: Preoperative magnetic resonance imaging (MRI) of 155 grade 2 meningioma patients with a median follow-up of 63.8 months were included and allocated to training (n = 92) and test sets (n = 63). After radiomic feature extraction (n = 200), least absolute shrinkage and selection operator feature selection with logistic regression classifier was performed to develop two models: (1) a clinicopathological model and (2) a combined clinicopathological and radiomic model. The probability of recurrence using the combined model was analyzed to identify candidates for ART. RESULTS: The combined clinicopathological and radiomics model exhibited superior performance for the prediction of recurrence compared with the clinicopathological model in the training set (area under the curve [AUC] 0.78 vs. 0.67, P = 0.042), which was also validated in the test set (AUC 0.77 vs. 0.61, P = 0.192). In patients with a high probability of recurrence by the combined model, the 5-year progression-free survival was significantly improved with ART (92% vs. 57%, P = 0.024), and the median time to recurrence was longer (54 vs. 17 months after surgery). CONCLUSIONS: Radiomics significantly contributes added value in predicting recurrence when integrated with the clinicopathological features in patients with grade 2 meningiomas. Furthermore, the combined model can be applied to identify high-risk patients who require ART. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-022-02090-7. |
format | Online Article Text |
id | pubmed-9396861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93968612022-08-24 An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas Park, Chae Jung Choi, Seo Hee Eom, Jihwan Byun, Hwa Kyung Ahn, Sung Soo Chang, Jong Hee Kim, Se Hoon Lee, Seung-Koo Park, Yae Won Yoon, Hong In Radiat Oncol Research OBJECTIVES: This study investigated whether radiomic features can improve the prediction accuracy for tumor recurrence over clinicopathological features and if these features can be used to identify high-risk patients requiring adjuvant radiotherapy (ART) in WHO grade 2 meningiomas. METHODS: Preoperative magnetic resonance imaging (MRI) of 155 grade 2 meningioma patients with a median follow-up of 63.8 months were included and allocated to training (n = 92) and test sets (n = 63). After radiomic feature extraction (n = 200), least absolute shrinkage and selection operator feature selection with logistic regression classifier was performed to develop two models: (1) a clinicopathological model and (2) a combined clinicopathological and radiomic model. The probability of recurrence using the combined model was analyzed to identify candidates for ART. RESULTS: The combined clinicopathological and radiomics model exhibited superior performance for the prediction of recurrence compared with the clinicopathological model in the training set (area under the curve [AUC] 0.78 vs. 0.67, P = 0.042), which was also validated in the test set (AUC 0.77 vs. 0.61, P = 0.192). In patients with a high probability of recurrence by the combined model, the 5-year progression-free survival was significantly improved with ART (92% vs. 57%, P = 0.024), and the median time to recurrence was longer (54 vs. 17 months after surgery). CONCLUSIONS: Radiomics significantly contributes added value in predicting recurrence when integrated with the clinicopathological features in patients with grade 2 meningiomas. Furthermore, the combined model can be applied to identify high-risk patients who require ART. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-022-02090-7. BioMed Central 2022-08-22 /pmc/articles/PMC9396861/ /pubmed/35996160 http://dx.doi.org/10.1186/s13014-022-02090-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Park, Chae Jung Choi, Seo Hee Eom, Jihwan Byun, Hwa Kyung Ahn, Sung Soo Chang, Jong Hee Kim, Se Hoon Lee, Seung-Koo Park, Yae Won Yoon, Hong In An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas |
title | An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas |
title_full | An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas |
title_fullStr | An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas |
title_full_unstemmed | An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas |
title_short | An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas |
title_sort | interpretable radiomics model to select patients for radiotherapy after surgery for who grade 2 meningiomas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9396861/ https://www.ncbi.nlm.nih.gov/pubmed/35996160 http://dx.doi.org/10.1186/s13014-022-02090-7 |
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