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An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas

Authors
 Chae Jung Park  ;  Seo Hee Choi  ;  Jihwan Eom  ;  Hwa Kyung Byun  ;  Sung Soo Ahn  ;  Jong Hee Chang  ;  Se Hoon Kim  ;  Seung-Koo Lee  ;  Yae Won Park  ;  Hong In Yoon 
Citation
 RADIATION ONCOLOGY, Vol.17(1) : 147, 2022-08 
Journal Title
RADIATION ONCOLOGY
Issue Date
2022-08
MeSH
Area Under Curve ; Humans ; Magnetic Resonance Imaging / methods ; Meningeal Neoplasms* / diagnostic imaging ; Meningeal Neoplasms* / radiotherapy ; Meningeal Neoplasms* / surgery ; Meningioma* / diagnostic imaging ; Meningioma* / radiotherapy ; Meningioma* / surgery ; Retrospective Studies ; World Health Organization
Keywords
Magnetic resonance imaging ; Meningioma ; Prognosis ; Radiomics ; Radiotherapy
Abstract
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.
Files in This Item:
T202204734.pdf Download
DOI
10.1186/s13014-022-02090-7
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Se Hoon(김세훈) ORCID logo https://orcid.org/0000-0001-7516-7372
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Park, Chae Jung(박채정) ORCID logo https://orcid.org/0000-0002-5567-8658
Byun, Hwa Kyung(변화경) ORCID logo https://orcid.org/0000-0002-8964-6275
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Yoon, Hong In(윤홍인) ORCID logo https://orcid.org/0000-0002-2106-6856
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Chang, Jong Hee(장종희) ORCID logo https://orcid.org/0000-0003-1509-9800
Choi, Seo Hee(최서희) ORCID logo https://orcid.org/0000-0002-4083-6414
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191889
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