Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features

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Title:	Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features
Authors:	Hsieh, Hsun-Ping Wu, Ding-You Hung, Kuo-Chuan Lim, Sher-Wei Chen, Tai-Yuan Yang, Fan-Chiang Ko, Ching-Chung
Contributors:	National Cheng Kung University Chi Mei Hospital Chia Nan University of Pharmacy & Science Chi Mei Hospital Chi Mei Hospital Chang Jung Christian University Chia Nan University of Pharmacy & Science National Sun Yat Sen University
Keywords:	intracranial meningiomas radiosurgery radiotherapy management survival surgery system tumors
Date:	2022
Issue Date:	2023-12-11 14:01:42 (UTC+8)
Publisher:	MDPI
Abstract:	A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrastenhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.
Relation:	JOURNAL OF PERSONALIZED MEDICINE, v.12, n.CB2, pp.CC2, pp.-,
Appears in Collections:	[Offices] 123

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