Huang Chih-Ying, Peng Syu-Jyun, Wu Hsiu-Mei, Yang Huai-Che, Chen Ching-Jen, Wang Mao-Che, Hu Yong-Sin, Chen Yu-Wei, Lin Chung-Jung, Guo Wan-Yuo, Pan David Hung-Chi, Chung Wen-Yuh, Lee Cheng-Chia
1Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital.
2Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University.
J Neurosurg. 2021 Oct 1;136(5):1298-1306. doi: 10.3171/2021.4.JNS203700. Print 2022 May 1.
Gamma Knife radiosurgery (GKRS) is a common treatment modality for vestibular schwannoma (VS). The ability to predict treatment response is important in patient counseling and decision-making. The authors developed an algorithm that can automatically segment and differentiate cystic and solid tumor components of VS. They also investigated associations between the quantified radiological features of each component and tumor response after GKRS.
This is a retrospective study comprising 323 patients with VS treated with GKRS. After preprocessing and generation of pretreatment T2-weighted (T2W)/T1-weighted with contrast (T1WC) images, the authors segmented VSs into cystic and solid components by using fuzzy C-means clustering. Quantitative radiological features of the entire tumor and its cystic and solid components were extracted. Linear regression models were implemented to correlate clinical variables and radiological features with the specific growth rate (SGR) of VS after GKRS.
A multivariable linear regression model of radiological features of the entire tumor demonstrated that a higher tumor mean signal intensity (SI) on T2W/T1WC images (p < 0.001) was associated with a lower SGR after GKRS. Similarly, a multivariable linear regression model using radiological features of cystic and solid tumor components demonstrated that a higher solid component mean SI (p = 0.039) and a higher cystic component mean SI (p = 0.004) on T2W/T1WC images were associated with a lower SGR after GKRS. A larger cystic component proportion (p = 0.085) was associated with a trend toward a lower SGR after GKRS.
Radiological features of VSs on pretreatment MRI that were quantified using fuzzy C-means were associated with tumor response after GKRS. Tumors with a higher tumor mean SI, a higher solid component mean SI, and a higher cystic component mean SI on T2W/T1WC images were more likely to regress in volume after GKRS. Those with a larger cystic component proportion also trended toward regression after GKRS. Further refinement of the algorithm may allow direct prediction of tumor response.
伽玛刀放射外科治疗（GKRS）是前庭神经鞘瘤（VS）的一种常见治疗方式。预测治疗反应的能力在患者咨询和决策中很重要。作者开发了一种算法，可自动分割并区分VS的囊性和实性肿瘤成分。他们还研究了每个成分的定量放射学特征与GKRS后肿瘤反应之间的关联。
这是一项回顾性研究，纳入了323例接受GKRS治疗的VS患者。在对预处理T2加权（T2W）/增强T1加权（T1WC）图像进行预处理和生成后，作者使用模糊C均值聚类将VS分割为囊性和实性成分。提取整个肿瘤及其囊性和实性成分的定量放射学特征。实施线性回归模型，以将临床变量和放射学特征与GKRS后VS的特定生长率（SGR）相关联。
整个肿瘤放射学特征的多变量线性回归模型表明，T2W/T1WC图像上较高的肿瘤平均信号强度（SI）（p < 0.001）与GKRS后较低的SGR相关。同样，使用囊性和实性肿瘤成分放射学特征的多变量线性回归模型表明，T2W/T1WC图像上较高的实性成分平均SI（p = 0.039）和较高的囊性成分平均SI（p = 0.004）与GKRS后较低的SGR相关。较大的囊性成分比例（p = 0.085）与GKRS后SGR降低的趋势相关。
使用模糊C均值定量的预处理MRI上VS的放射学特征与GKRS后的肿瘤反应相关。T2W/T1WC图像上肿瘤平均SI较高、实性成分平均SI较高和囊性成分平均SI较高的肿瘤在GKRS后更有可能体积缩小。囊性成分比例较大的肿瘤在GKRS后也有缩小趋势。该算法的进一步完善可能允许直接预测肿瘤反应。
