Prediction of Patients at Risk of Pelvic Insufficiency Fractures Following Pelvic Radiotherapy

Abstract

<h3>Purpose/Objective(s)</h3> Radiotherapy-induced pelvic insufficiency fractures (PIFs), historically under-reported, can lead to significant morbidity. Using deep-learning techniques, this study develops a fully automated predictive tool to identify patients who are at high-risk of developing PIFs following radiotherapy for gynecological cancer. <h3>Materials/Methods</h3> Retrospective clinical, dosimetry and imaging data from 330 patients receiving pelvic radiotherapy for any primary gynecological cancer between January 2012 and January 2021 was collected (study approved by local ethical committee). Patient electronic records were reviewed; PIFs were diagnosed radiologically using computed tomography (CT) and magnetic resonance imaging (MRI). An auto-segmentation model for the sacrum and whole pelvis on CT-imaging was developed using a UNETR architecture to aid evaluation of pelvic radiotherapy dose and CT density. The model was validated by comparing the derived dose-volume histograms (DVHs) with those derived from manual contours. Chi-squared and Mann-Whitney tests were performed to determine associations between derived potential risk factors and PIF. Using Elastic Net Logistic Regression modelling a predictive tool to identify patients at risk of developing PIF was developed and internally validated. <h3>Results</h3> Among the 330 patients, the median follow-up was 36.6 months, and median age was 65 years. The PIF incidence was 10.9% and median interval from radiotherapy to PIF was 16 months. 86% of fractures occurred in the sacrum, 56% of patients had multiple fractures and 64% of patients were symptomatic. The auto-contouring tool of the sacrum and whole pelvis was clinically validated and, against gold-standard contours, achieved a mean DICE coefficient of 0.94, Average Surface Distance (ASD) of 0.65, and maximum absolute error for DVH curves of 3.1%. Univariant analysis found that site of primary disease, body mass index, prior diagnosis of osteopenia or osteoporosis, use of concurrent chemotherapy, V_55Gy (volume of the sacrum and whole pelvis receiving 55 Gy), and lower CT density were statistically associated with the development of PIF (p<0.05). The fully automated predictive tool to identify patients at risk of PIF using solely imaging and dosimetry data had similar accuracy to models including additional clinical features; both had a receiver-operating-characteristic (ROC) area-under-the-curve (AUC) of 0.761. The prediction tool has a sensitivity of 66%, specificity of 72% and a negative predictive value of 95%. <h3>Conclusion</h3> PIF is not uncommon in this setting, often symptomatic and strongly associated with low bone density on CT. Prediction and prevention of PIF would have an immense impact on patients and healthcare systems. We propose an automated predictive tool to identify patients at high risk, for whom effective prophylactic treatments are needed.