###### Abstract  
This study aimed to evaluate prognostic factors associated with nidus obliteration following stereotactic radiosurgery (SRS) for cerebral arteriovenous malformations. From January 2001 to January 2018, 119 patients who underwent SRS with AVM were studied to analyze major prognostic factors (age, prescription dose (Gy), volume (mm\({}^{3}\)), nidus size (cm), and Spetzler-Martin (SM) grade) for nidus obliteration. A random forest and tree explainer was used to construct a predictive model of nidus obliteration. The prognostic factors affecting nidus obliteration from most to least important were age, nidus size, volume, total prescription dose, and SM grade, using a predictive model. In a specific case for nidus size (1.5 cm), total dose (23 Gy), and SM grade (2), the result showed a high obliteration score of 0.75 with the actual obliteration period of 6 months spent; the mean AUC was 0.90 in K-fold cross validation. The predictive model identified the main contributing factors associated with a prognostic of nidus obliteration from linear accelerator-based SRS for cerebral AVM. It was confirmed that the results, including the prognostic factors, are potentially useful for outcome prediction for patient and treatment. artierovenous malformation; explainable predictive modeling; nidus obliteration; stereotactic radiosurgery; prognostic factors +
Footnote †: journal: Medical  
of radiosurgery, factors related to the prescription dose (Gy) and factors such as homogeneity and conformity indexes are typically generated and evaluated during the process of radiosurgery planning have been reported as predictors of the nidus obliteration [10]. In addition, the effect of age on the prognosis of radiosurgery for AVM has been analyzed and reported. In elderly patients aged \(\geq\)60 years, [PERSON] et al. investigated AVM obliteration was achieved in 66.7% population with a marginal dose of at least 22 Gy; the incidence of radiation-induced complications was minimal, and the risk of bleeding during the latent period was very low [11]. Meanwhile, [PERSON] et al. reported a high obliteration rate of 65.9% and low complication rate of 8.0% in pediatric AVM radiosurgery patients, based on data collected from 20 studies with 1212 patients [12]. Considering that cerebral AVMs are associated with active angiogenesis and rapid endothelial cell turnover, the treatment response to irradiation increases with younger age, and early obliteration of the nidus has been found to be related [12]. However, large-scale clinical studies have not shown any differences related to age or sex, and a clear pathological treatment mechanism caused by differences in radiation sensitivity according to age has not yet been established [13]. Several studies have analyzed various prognostic factors for outcome prediction related to successful occlusion after radiosurgery of cerebral AVM with a conventional machine learning approach [14; 15]. In recent years, predictive models using artificial intelligence (AI) techniques have been used to predict the prognosis of AVM treatment. [PERSON] et al. (2016) reported that, by using prospective clinical data and machine learning, they obtained a specificity of 62% and a sensitivity of 85% for predicting prognosis related to occlusion of malformed vessels in SRS for cerebral AVMs [14]. [PERSON] et al. (2022) presented results of a machine learning modeling study predicting the outcome of SRS for residual arteriovenous malformations after partial embolization [15]. For 130 AVM patients, researchers used 9 machine learning models including support vector machine (SVM), K-nearest neighbor (KNN), and neuro network (NN), and reported performance of AUC 0.66-0.78. The primary outcome was the obliteration rate, 70.77% (92 of 130), with the follow-up of 43.8 months (range, 12-108 months). A favorable outcome occurred in 89 patients (68.46%), but 41 patients (31.54%) reported an unfavorable outcome. Although an attempt was made to incorporate radiomic features in this study, in-depth feature analysis was excluded.