Abstract：
OBJECTIVE: To investigate the potentialities of radiomic analysis and develop radiomic models to predict the skull dysmorphology severity and post-surgical outcome in children with isolated sagittal synostosis (ISS).
METHODS: Preoperative high-resolution CT scans of infants with ISS treated with surgical correction were retrospectively reviewed. The sagittal suture (ROI_entire) and its sections (ROI_anterior/central/posterior) were segmented. Radiomic features extracted from ROI_entire were correlated to the scaphocephalic severity, while radiomic features extracted from ROI_anterior/central/posterior were correlated to the post-surgical outcome. Logistic regression models were built from selected radiomic features and validated to predict the scaphocephalic severity and post-surgical outcome.
RESULTS: A total of 105 patients were enrolled in this study. The kurtosis was obtained from the feature selection process for both scaphocephalic severity and post-surgical outcome prediction. The model predicting the scaphocephalic severity had an area under the curve (AUC) of the receiver operating characteristic of 0.71 and a positive predictive value of 0.83 for the testing set. The model built for the post-surgical outcome showed an AUC (95% CI) of 0.75 (0.61;0.88) and a negative predictive value (95% CI) of 0.95 (0.84;0.99).
CONCLUSIONS: Our results suggest that radiomics could be useful in quantifying tissue microarchitecture along the mid-suture space and potentially provide relevant biological information about the sutural ossification processes to predict the onset of skull deformities and stratify post-surgical outcome.
METHODS: Preoperative high-resolution CT scans of infants with ISS treated with surgical correction were retrospectively reviewed. The sagittal suture (ROI_entire) and its sections (ROI_anterior/central/posterior) were segmented. Radiomic features extracted from ROI_entire were correlated to the scaphocephalic severity, while radiomic features extracted from ROI_anterior/central/posterior were correlated to the post-surgical outcome. Logistic regression models were built from selected radiomic features and validated to predict the scaphocephalic severity and post-surgical outcome.
RESULTS: A total of 105 patients were enrolled in this study. The kurtosis was obtained from the feature selection process for both scaphocephalic severity and post-surgical outcome prediction. The model predicting the scaphocephalic severity had an area under the curve (AUC) of the receiver operating characteristic of 0.71 and a positive predictive value of 0.83 for the testing set. The model built for the post-surgical outcome showed an AUC (95% CI) of 0.75 (0.61;0.88) and a negative predictive value (95% CI) of 0.95 (0.84;0.99).
CONCLUSIONS: Our results suggest that radiomics could be useful in quantifying tissue microarchitecture along the mid-suture space and potentially provide relevant biological information about the sutural ossification processes to predict the onset of skull deformities and stratify post-surgical outcome.
摘要：
目的：研究影像组学分析的潜力，并建立影像组学模型来预测孤立矢状面滑脱症（ISS）患儿的颅骨形态异常严重程度和术后结局。
方法：回顾性分析手术矫正后ISS患儿的术前高分辨率CT扫描。分割矢状缝线(ROI_整个)及其切片(ROI_前/中/后)。从ROI_entire中提取的放射学特征与头颅严重程度相关，而从ROI_前/中/后提取的影像组学特征与术后结果相关。根据选定的影像学特征建立Logistic回归模型，并对其进行验证以预测头颅严重程度和手术后结果。
结果：本研究共纳入105例患者。峰度是从特征选择过程中获得的，用于头颅严重程度和手术后结果预测。预测头颅严重程度的模型的受试者工作特性的曲线下面积（AUC）为0.71，测试集的阳性预测值为0.83。为术后结果建立的模型显示AUC（95％CI）为0.75（0.61;0.88），阴性预测值（95％CI）为0.95（0.84;0.99）。
结论：我们的结果表明，影像组学可用于量化中缝空间的组织微观结构，并可能提供有关缝合骨化过程的相关生物学信息，以预测颅骨畸形的发生和分层手术后的结果。
方法：回顾性分析手术矫正后ISS患儿的术前高分辨率CT扫描。分割矢状缝线(ROI_整个)及其切片(ROI_前/中/后)。从ROI_entire中提取的放射学特征与头颅严重程度相关，而从ROI_前/中/后提取的影像组学特征与术后结果相关。根据选定的影像学特征建立Logistic回归模型，并对其进行验证以预测头颅严重程度和手术后结果。
结果：本研究共纳入105例患者。峰度是从特征选择过程中获得的，用于头颅严重程度和手术后结果预测。预测头颅严重程度的模型的受试者工作特性的曲线下面积（AUC）为0.71，测试集的阳性预测值为0.83。为术后结果建立的模型显示AUC（95％CI）为0.75（0.61;0.88），阴性预测值（95％CI）为0.95（0.84;0.99）。
结论：我们的结果表明，影像组学可用于量化中缝空间的组织微观结构，并可能提供有关缝合骨化过程的相关生物学信息，以预测颅骨畸形的发生和分层手术后的结果。
