Validating the screening criteria for bone metastases in treatment-naïve unfavorable intermediate and high-risk prostate cancer - the prevalence and location of bone- and lymph node metastases

Abstract Objective The European Association of Urology (EAU) recommends a bone scan for newly diagnosed unfavorable intermediate- and high-risk prostate cancer. We aimed to validate the screening criteria for bone metastases in patients with treatment-naïve prostate cancer. Methods This single-center retrospective study included all patients with treatment-naïve unfavorable intermediate- or high-risk prostate cancer. All underwent MRI of the lumbar column (T2Dixon) and pelvis (3DT2w, DWI, and T2 Dixon). The presence and location of lymph node and bone metastases were registered according to risk groups and radiological (rad) T-stage. The risk of lymph node metastases was assessed by odds ratio (OR). Results We included 390 patients, of which 68% were high-risk and 32% were unfavorable intermediate-risk. In the high-risk group, the rate of regional- and non-regional lymph node metastases was 11% and 6%, respectively, and the rate of bone metastases was 10%. In the unfavorable intermediate-risk group, the rate of regional- and non-regional lymph node metastases was 4% and 0.8%, respectively, and the rate of bone metastases was 0.8%. Metastases occurred exclusively in the lumbar column in 0.5% of all patients, in the pelvis in 4%, and the pelvis and lumbar column in 3%. All patients with bone metastases had radT3-4, and patients with radT3-4 showed a four-fold increased risk of lymph node metastases (OR 4.48, 95% CI: 2.1–9.5). Conclusion Bone metastases were found in 10% with high-risk prostate cancer and 0.8% with unfavorable intermediate-risk. Therefore, we question the recommendation to screen the unfavorable intermediate-risk group for bone metastases. Key Points • The rate of bone metastases was 10% in high-risk patients and 0.8% in the unfavorable intermediate-risk group. • The rate of lymph-node metastases was 17% in high-risk patients and 5% in the unfavorable intermediate-risk group. • No bone metastases were seen in radiologically localized disease

Characterization of Lipids in Saliva, Tears and Minor Salivary Glands of Sjögren’s Syndrome Patients Using an HPLC/MS-Based Approach

The diagnostic work-up of primary Sjögren’s syndrome (pSS) includes quantifying saliva and tear production, evaluation of autoantibodies in serum and histopathological analysis of minor salivary glands. Thus, the potential for further utilizing these fluids and tissues in the quest to find better diagnostic and therapeutic tools should be fully explored. Ten samples of saliva and tears from female patients diagnosed with pSS and ten samples of saliva and tears from healthy females were included for lipidomic analysis of tears and whole saliva using high-performance liquid chromatography coupled to time-of-flight mass spectrometry. In addition, lipidomic analysis was performed on minor salivary gland biopsies from three pSS and three non-SS females. We found significant differences in the lipidomic profiles of saliva and tears in pSS patients compared to healthy controls. Moreover, there were differences in individual lipid species in stimulated saliva that were comparable to those of glandular biopsies, representing an intriguing avenue for further research. We believe a comprehensive elucidation of the changes in lipid composition in saliva, tears and minor salivary glands in pSS patients may be the key to detecting pSS-related dry mouth and dry eyes at an early stage. The identified differences may illuminate the path towards future innovative diagnostic methodologies and treatment modalities for alleviating pSS-related sicca symptoms

Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction

Abstract Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear film lipid layer, studying the expression of tear proteins might increase the understanding of the etiology of the condition. Machine learning is able to detect patterns in complex data. This study applied machine learning to classify levels of meibomian gland dysfunction from tear proteins. The aim was to investigate proteomic changes between groups with different severity levels of meibomian gland dysfunction, as opposed to only separating patients with and without this condition. An established feature importance method was used to identify the most important proteins for the resulting models. Moreover, a new method that can take the uncertainty of the models into account when creating explanations was proposed. By examining the identified proteins, potential biomarkers for meibomian gland dysfunction were discovered. The overall findings are largely confirmatory, indicating that the presented machine learning approaches are promising for detecting clinically relevant proteins. While this study provides valuable insights into proteomic changes associated with varying severity levels of meibomian gland dysfunction, it should be noted that it was conducted without a healthy control group. Future research could benefit from including such a comparison to further validate and extend the findings presented here