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Objective: To assess the reliability of the myeloma spine and bone damage score (MSBDS) across multiple readers with different levels of expertise and from different institutions. Methods: A reliability exercise, including 104 data sets of static images and complete CT examinations of patients affected by multiple myeloma (MM), was performed. A complementary imaging atlas provided detailed examples of the MSBDS scores, including low-risk and high-risk lesions. A total of 15 readers testing the MSBDS were evaluated. ICC estimates and their 95% confidence intervals were calculated based on mean rating (k = 15), absolute agreement, a two-way random-effects model and Cronbach's alpha. Results : Overall, the ICC correlation coefficient was 0.87 (95% confidence interval: 0.79-0.92), and the Cronbach's alpha was 0.93 (95% confidence interval: 0.94-0.97). Global inter- and intra-observer agreement among the 15 readers with scores below or equal to 6 points and scores above 6 points were 0.81 (95% C.I.: 0.72-0.86) and 0.94 (95% C.I.:0.91-0.98), respectively. Conclusion: We present a consensus-based semiquantitative scoring systems for CT in MM with a complementary CT imaging atlas including detailed examples of relevant scoring techniques. We found substantial agreement among readers with different levels of experience, thereby supporting the role of the MSBDS for possible large-scale applications. Significance and Innovations • Based on previous work and definitions of the MSBDS, we present real-life reliability data for quantitative bone damage assessment in multiple myeloma (MM) patients on CT. • In this study, reliability for the MSBDS, which was tested on 15 readers with different levels of expertise and from different institutions, was shown to be moderate to excellent. • The complementary CT imaging atlas is expected to enhance unified interpretations of the MSBDS between different professionals dealing with MM patients in their routine clinical practice.

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes., Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data., Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed., Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.