Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence

IntroductionDual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence.MethodsWe collected a sample of positive and negative DECTs, reviewed twice—once with and once without the DL tool—with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed.ResultsWe included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL.ConclusionsThe implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model

Sonography of active rheumatoid arthritis during pregnancy: a case report and literature review

Disease activity in rheumatoid arthritis usually subsides in pregnancy, however a subset of patients have worsened symptoms with joint pain and swelling. Monitoring and mitigating disease activity in pregnancy is important for preventing deforming structural changes which can affect the ability of the patient to care for themselves and the newborn. Ultrasound is a safe and low-cost imaging modality for detecting active changes from an inflammatory arthritis, which can help guide management. We describe a case of an acute disease flare during pregnancy, readily detected with ultrasound, and present a review of sonographic evaluation of rheumatoid arthritis in pregnancy. Keywords: Rheumatoid arthritis, ultrasound, pregnancy, synoviti

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

Background: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs. Methods: DECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics. Results: Segresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively. Conclusion: In this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout

Multiethnic PDX models predict a possible immune signature associated with TNBC of African ancestry

PURPOSE: Triple-negative breast cancer (TNBC) is an aggressive subtype most prevalent among women of Western Sub-Saharan African ancestry. It accounts for 15-25% of African American (AA) breast cancers (BC) and up to 80% of Ghanaian breast cancers, thus contributing to outcome disparities in BC for black women. The aggressive biology of TNBC has been shown to be regulated partially by breast cancer stem cells (BCSC) which mediate tumor recurrence and metastasis and are more abundant in African breast tumors. METHODS: We studied the biological differences between TNBC in women with African ancestry and those of Caucasian women by comparing the gene expression of the BCSC. From low-passage patient derived xenografts (PDX) from Ghanaian (GH), AA, and Caucasian American (CA) TNBCs, we sorted for and sequenced the stem cell populations and analyzed for differential gene enrichment. RESULTS: In our cohort of TNBC tumors, we observed that the ALDH expressing stem cells display distinct ethnic specific gene expression patterns, with the largest difference existing between the GH and AA ALDH+ cells. Furthermore, the tumors from the women of African ancestry [GH/AA] had ALDH stem cell (SC) enrichment for expression of immune related genes and processes. Among the significantly upregulated genes were CD274 (PD-L1), CXCR9, CXCR10 and IFI27, which could serve as potential drug targets. CONCLUSIONS: Further exploration of the role of immune regulated genes and biological processes in BCSC may offer insight into developing novel approaches to treating TNBC to help ameliorate survival disparities in women with African ancestry