Please cite this article in press as: Rastegar S, et al. Radiomics for classification of bone mineral loss: A machine learning study. Diagnostic and Interventional Imaging (2020),

Background Radiomics features reproducibility assessment is a critical issue in imaging biomarker development era. In the present study, we aimed to assess test–retest reproducibility analysis of bone mineral densitometry (BMD) image radiomics features. Methods In this prospective research work, eighteen patients were included and were subjected to DXA BMD scans acquired within 10 min of each other under an approved protocol. Seven regions of interest (ROIs) including four lumbar spine regions (L1-L4) and three hip regions (trochanteric, inter trochanteric and neck) in both test and re-test images were segmented and 107 radiomics features from seven different feature sets were extracted. Intra-class correlation coefficient (ICC) were initially used to estimate radiomics features reproducibility. Results We showed that there is no radiomics feature with 90% < ICC < 100% in all ROIs, but there are three feature including Strength (from NGTDM feature set), SALGLE (Small Area Low Gray Level Emphasis) (from GLSZM feature set) and Busyness (from NGTDM feature set) with ICC < 70% in all eight ROIs. Shape features has features with ICC < 70%. Conclusion Our study on test–retest reproducibility analysis of bone mineral densitometry radiomics features shows radiomics features have several variations against changes of time of image acquisition. The reproducible features may be used as imaging biomarkers in the field of clinical densitometry. The results of this study may be repeated by more radiomics features and more BMD scanners as first line for bone mineral biomarker discovery.

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Background: Auditing the treatment planning system (TPS) software for a radiotherapy unit is of paramount importance in any radiation therapy department. A Plexiglas phantom was proposed to measure the ionization of 60Co high dose rate (HDR) source and compare dose points in the planning system for auditing and verifying TPS. Methods: Auditing was performed using a Plexiglas phantom in an end-to-end test, and relative dose points were detected by a farmer-type ionization chamber and compared with the relative dose of similar points in TPS. The audit results were determined as pass optimal level (<3.3%), pass action level (between 3.3% and 5%), and out of tolerance (>5%). Results: The comparison of the collected data revealed that 80% of the measured values were ≤5% in the pass level, and 20% of the points were out of tolerance (between 5% and 6.99%). Conclusion: This study documented the appropriateness of the dosimetry audit test and this phantom design for the HDR brachytherapy TPS.