A summary of the Russian clinical guidelines on the diagnosis and treatment of osteoporosis

Key statements of the Russian clinical guidelines on the diagnosis and treatment of osteoporosis are summarized. They were developed by a task force representing the key Russian professional associations involved in the management of osteoporosis and approved by the Russian Ministry of Health.

PURPOSE

To summarize key statements of the Russian clinical practice guidelines for the diagnosis and treatment of osteoporosis.

METHODS

The Russian clinical guidelines on the diagnosis and treatment of osteoporosis were developed by a task force representing the key Russian professional associations involved in the management of osteoporosis: These comprised the Russian Association of Endocrinologists, the Russian Association for Osteoporosis, the Association of Rheumatologists of Russia, the Association of Orthopedic surgeons and Traumatologists of Russia, the Russian Association of Gynecologists-Endocrinologists, and the Russian Association of Gerontologists and Geriatrics. The guidelines are based on a systematic literature review and principles of evidence-based medicine and were compiled in accordance with the requirements for clinical recommendations developed by the Ministry of Health of the Russian Federation.

RESULTS

Key statements included in the Russian guidelines of osteoporosis approved by the Russian Ministry of Health in 2021 are summarized. The statements are graded based on levels of evidence and supported by short comments. The guidelines are focused on the current approach to screening, diagnosis, differential diagnosis, and treatment of osteoporosis.

CONCLUSION

These guidelines are a practical tool for general practitioners, as well as medical specialists, primarily endocrinologists, rheumatologists, orthopedic surgeons, and other physicians who are involved in the management of patients with osteoporosis.

Interpretable vertebral fracture quantification via anchor-free landmarks localization

Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to the current clinical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an interpretable and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1mm), vertebrae 2D detection (precision and recall are 0.99), and fracture identification (ROC AUC at the patient level is up to 0.96). Our anchor-free vertebra detection network shows excellent generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85, specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.

Diffusion processes modeling in magnetic resonance imaging

Background

The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied.

Material and method

To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T₂: cyclomethicone and caprylyl methicone. For quantitative assessment of our phantom, we performed DWI on 1.5T magnetic resonance scanner with various fat suppression techniques. We assessed water-in-oil emulsion as an extracorporeal source signal by simultaneously scanning a patient in whole-body DWI sequence.

Results

We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm²/s. The ADC values of polymer solutions are well relevant to the mono-exponential equation with the mean relative difference of 0.91%.

Conclusion

The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.