MRI-based R2* mapping in patients with suspected or known iron overload.

PURPOSE: R2* relaxometry is a quantitative method for assessment of iron overload. The purpose is to analyze the cross-sectional relationships between R2* in organs across patients with primary and secondary iron overload. Secondary analyses were conducted to analyze R2* according to treatment regimen. METHODS: This is a retrospective, cross-sectional, institutional review board-approved study of eighty-one adult patients with known or suspected iron overload. R2* was measured by segmenting the liver, spleen, bone marrow, pancreas, renal cortex, renal medulla, and myocardium using breath-hold multi-echo gradient-recalled echo imaging at 1.5 T. Phlebotomy, transfusion, and chelation therapy were documented. Analyses included correlation, Kruskal-Wallis, and post hoc Dunn tests. p < 0.01 was considered significant. RESULTS: Correlations between liver R2* and that of the spleen, bone marrow, pancreas, and heart were respectively 0.49, 0.33, 0.27, and 0.34. R2* differed between patients with primary and secondary overload in the liver (p < 0.001), spleen (p < 0.001), bone marrow (p < 0.01), renal cortex (p < 0.001), and renal medulla (p < 0.001). Liver, spleen, and bone marrow R2* were higher in thalassemia than in hereditary hemochromatosis (all p < 0.01). Renal cortex R2* was higher in sickle cell disease than in hereditary hemochromatosis (p < 0.001) and in thalassemia (p < 0.001). Overall, there was a trend toward lower liver R2* in patients assigned to phlebotomy and higher liver R2* in patients assigned to transfusion and chelation therapy. CONCLUSION: R2* relaxometry revealed differences in degree or distribution of iron overload between organs, underlying etiologies, and treatment.

Estimating the health-related quality of life of kidney stone patients: initial results from the Wisconsin Stone Quality of Life Machine-Learning Algorithm (WISQOL-MLA).

OBJECTIVE: To build the Wisconsin Stone Quality of Life Machine-Learning Algorithm (WISQOL-MLA) to predict urolithiasis patients' health-related quality of life (HRQoL) based on demographic, symptomatic and clinical data collected for the validation of the Wisconsin Stone Quality-of-Life (WISQOL) questionnaire, an HRQoL measurement tool designed specifically for patients with kidney stones. MATERIAL AND METHODS: We used data from 3206 stone patients from 16 centres. We used gradient-boosting and deep-learning models to predict HRQoL scores. We also stratified HRQoL scores by quintile. The dataset was split using a standard 70%/10%/20% training/validation/testing ratio. Regression performance was evaluated using Pearson's correlation. Classification was evaluated with an area under the receiver-operating characteristic curve (AUROC). RESULTS: Gradient boosting obtained a test correlation of 0.62. Deep learning obtained a correlation of 0.59. Multivariate regression achieved a correlation of 0.44. Quintile stratification of all patients in the WISQOL dataset obtained an average test AUROC of 0.70 for the five classes. The model performed best in identifying the lowest (0.79) and highest quintiles (0.83) of HRQoL. Feature importance analysis showed that the model weighs in clinically relevant factors to estimate HRQoL, such as symptomatic status, body mass index and age. CONCLUSIONS: Harnessing the power of the WISQOL questionnaire, our initial results indicate that the WISQOL-MLA can adequately predict a stone patient's HRQoL from readily available clinical information. The algorithm adequately relies on relevant clinical factors to make its HRQoL predictions. Future improvements to the model are needed for direct clinical applications.

Review of Natural Language Processing in Radiology.

Natural language processing (NLP) is an interdisciplinary field, combining linguistics, computer science, and artificial intelligence to enable machines to read and understand human language for meaningful purposes. Recent advancements in deep learning have begun to offer significant improvements in NLP task performance. These techniques have the potential to create new automated tools that could improve clinical workflows and unlock unstructured textual information contained in radiology and clinical reports for the development of radiology and clinical artificial intelligence applications. These applications will combine the appropriate application of classic linguistic and NLP preprocessing techniques, modern NLP techniques, and modern deep learning techniques.

Percutaneous Nephrolithotomy Access: A Systematic Review of Intraoperative Assistive Technologies.

Percutaneous nephrolithotomy (PCNL) is the current gold standard for the treatment of large and complex renal stone disease. It is a complex procedure that requires skill and experience. The most challenging step and key requisite of a successful PCNL is establishing optimal access to the renal collecting system with imaging modalities. To increase safety and efficacy in this crucial step, and with ongoing advancements in current technology, several aids have been developed to assist the urologist and help accurately guide the needle to the target. The goal of this systematic review was to identify and discuss these innovations.