A deep learning model trained for pneumothorax detection with a large dataset of chest radiographs generalized well to multiple external datasets that were independent of the training data source. Purpose To assess the generalizability of a deep learning pneumothorax detection model on datasets from multiple external institutions and examine patient and acquisition factors that might influen...

Diagnostic and Interventional Radiology

Mammography is extensively used for breast cancer screening but has high false-positive rates. Here, prospectively collected blood samples were used to identify circulating microRNA (miRNA) biomarkers to discriminate between malignant and benign breast lesions among women with abnormal mammograms. The Discovery cohort comprised 72 patients with breast cancer and 197 patients with benign breast lesions, while the Validation cohort had 73 and 196 cancer and benign cases, respectively. Absolute expression levels of 324 miRNAs were determined using RT-qPCR. miRNA biomarker panels were identified by: (1) determining differential expression between malignant and benign breast lesions, (2) focusing on top differentially expressed miRNAs, and (3) building panels from an unbiased search among all expressed miRNAs. Two-fold cross-validation incorporating a feature selection algorithm and logistic regression was performed. A six-miRNA biomarker panel identified by the third strategy, had an area under the curve (AUC) of 0.785 and 0.774 in the Discovery and Validation cohorts, respectively, and an AUC of 0.881 when differentiating between cases versus those with benign lesions or healthy individuals with normal mammograms. Biomarker panel scores increased with tumor size, stage and number of lymph nodes involved. Our work demonstrates that circulating miRNA signatures can potentially be used with mammography to differentiate between patients with malignant and benign breast lesions.

The Kaiser score is increasingly recognized as a valuable tool to improve breast MRI interpretation. Contrast enhancement kinetics are the second most important diagnostic criterion, thus defining the curve type plays a crucial role in Kaiser score assessment. We investigate whether the timepoint used to determine the initial enhancement (earlyor peak) for the signal-intensity time curve analysis affects the diagnostic performance of the Kaiser score. This IRB-approved, retrospective, single-center study included 70 consecutives histologically verified breast MRI cases. Two off-site breast radiologists independently read all examinations using the Kaiser score, assessing the initial enhancement using three approaches: -first (1 st), second (2nd) and peak (maximum) of either 1 st or 2nd post-contrast timepoints. The initial enhancement was then compared to the last timepoint (delayed enhancement) to determine the curve type. Visual assessment of curve types was used for this study. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis. Kaiser score reading results using the peak enhancement of either the first or second timepoint performed significantly better than the other approaches (P < 0.05, respectively) and specifically achieved higher sensitivity. Diagnostic accuracy (AUC area under the curve) ranged between 85.4 % and 91.6 %, without significant differences between the two readers (P < 0.5). Diagnostic performance of the Kaiser score is significantly influenced by how the initial enhancement timepoint is determined. Peak enhancement should be used as initial timepoint to avoid pitfalls due to timing or physiological differences.

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.