CT-based radiomics modeling for skull dysmorphology severity and surgical outcome prediction in children with isolated sagittal synostosis: a hypothesis-generating study

• Published in: Radiologia medica Vol. 127; no. 6; pp. 616 - 626
• Main Authors: Calandrelli, Rosalinda, Boldrini, Luca, Tran, Huong Elena, Quinci, Vincenzo, Massimi, Luca, Pilato, Fabio, Lenkowicz, Jacopo, Votta, Claudio, Colosimo, Cesare
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01.06.2022; Springer Nature B.V
• Subjects: Child; Computed Tomography; Computer architecture; Craniosynostoses - diagnostic imaging; Craniosynostoses - surgery; Diagnostic Radiology; Feature extraction; Humans; Imaging; Infant; Interventional Radiology; Kurtosis; Medicine; Medicine & Public Health; Neuroradiology; Predictions; Radiology; Radiomics; Regression models; Retrospective Studies; Skull - diagnostic imaging; Skull - surgery; Surgical outcomes; Sutures; Tomography, X-Ray Computed - methods; Treatment Outcome; Ultrasound; Sagittal synostosis; Scaphocephalic severity; High-resolution CT; Predictive model; Radiomics
• ISSN: 1826-6983; 0033-8362; 1826-6983
• DOI: 10.1007/s11547-022-01493-6

Purpose 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). Materials and 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). Conclusion 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.