Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy

• Published in: Scientific reports Vol. 12; no. 1; p. 6735
• Main Authors: Park, Taeyong, Yoon, Min A, Cho, Young Chul, Ham, Su Jung, Ko, Yousun, Kim, Sehee, Jeong, Heeryeol, Lee, Jeongjin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 692/698/1671; 692/699/578; 692/699/67/1344; 692/699/67/322; Algorithms; Automation; Humanities and Social Sciences; Humans; Malignancy; multidisciplinary; Neoplasms; Neural networks; Neural Networks, Computer; Patients; Prediction models; Radiomics; Science; Science (multidisciplinary); Segmentation; Spinal Fractures - diagnostic imaging; Spine; Tomography, X-Ray Computed - methods; Vertebrae
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-10807-7

Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93–0.94; cross-sectional area error, 2.66–2.97%; average surface distance, 0.40–0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.