Radiomics analysis based on lumbar spine CT to detect osteoporosis

Abstract

Objectives Undiagnosed osteoporosis may lead to severe complications after spinal surgery. This study aimed to construct and validate a radiomic signature based on CT scans to screen for lumbar spine osteoporosis. Methods Using a stratified random sample method, 386 vertebral bodies were randomly divided into a training set (n = 270) and a test set (n = 116). A total of 1040 radiomics features were automatically retracted from lumbar spine CT scans using the 3D slicer pyradiomics module, and a radiomic signature was created. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the Hounsfield and radiomics signature models were calculated. The AUCs of the two models were compared using the DeLong test. Their clinical usefulness was assessed using a decision curve analysis. Results Twelve features were chosen to establish the radiomic signature. The AUCs of the radiomics signature and Hounsfield models were 0.96 and 0.88 in the training set and 0.92 and 0.84 in the test set, respectively. According to the DeLong test, the AUCs of the two models were significantly different (p < 0.05). The radiomics signature model indicated a higher overall net benefit than the Hounsfield model, as determined by decision curve analysis. Conclusions The CT-based radiomic signature can differentiate patients with/without osteoporosis prior to lumbar spinal surgery. Without additional medical cost and radiation exposure, the radiomics method may provide valuable information facilitating surgical decision-making. Key Points • The goal of the study was to evaluate the efficacy of a radiomics signature model based on routine preoperative lumbar spine CT scans in screening osteoporosis. • The radiomics signature model demonstrated excellent prediction performance in both the training and test sets. • This radiomics method may provide valuable information and facilitate surgical decision-making without additional medical costs and radiation exposure.

The frequency of osteoporosis in the thoracic surgery patient population: An opportunity assessment from thorax computed tomography scans

“…A decline in bone density and a worsening of the quality of bone microarchitecture are two characteristics of osteoporosis, a disorder affecting the bones. Dual-energy X-ray absorptiometry (DEXA) is the most widely used method of disease screening (12). It provides an accurate estimate of the risk of fracture when combined with other clinical indicators (9).…”

“…The percentage of osteoporosis screening is reported to be low. Due to the costs of care and radiation exposure, DEXA is not typically performed prior to surgeries in clinical practice (12). But before many surgeries, CT scans are acquired.…”

“…Additionally, during pulmonary resections, accidental rib fractures, which can happen in thoracotomy, are frequently linked to higher postoperative morbidity (6). Increased screening rates might lessen difficulties brought on by osteoporosis (12). Additionally, without additional radiation exposure or economic cost, HU measurements enable practitioners to perform preoperative osteoporosis screenings (12).…”

Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study

“…Since 2012, radiological images have been analysed with radiomics. The underlying rationale is that clinical images contain quantitative information, reflecting the underlying pathophysiology of the examined tissue [ 1 , 2 ]. The image substructures are analysed mathematically, resulting in quantifiable features with different levels of complexity [ 3 ].…”

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors

“…16 Currently, studies have demonstrated the potential of radiomics during the differential diagnosis of spinal diseases, including spinal tumors, spinal fractures, and osteoporosis. [17][18][19] Combining the radiomics and clinical factors can improve disease diagnosis accuracy. 20 However, there have been no reports of imaging biomarkers that could distinguish TS from PS depending on radiomics approaches.…”