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Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

Foamy urine is widely regarded as a sign of proteinuria. However, there is no objective definition of foamy urine and there are no reports on the proportion of involved patients who have overt proteinuria or microalbuminuria. We performed this study to investigate this proportion and to identify possible risk factors for these two conditions. We reviewed all new outpatients from 1 November 2011 to 30 April 2012 and identified patients complaining of foamy urine. Their demographic data and medical records were examined. In particular, we tabulated the patients' spot urinary protein to creatinine ratio, spot urinary microalbumin to creatinine ratio (ACR), blood urea nitrogen (BUN), and serum levels of creatinine (Cr), uric acid, calcium, phosphate, and glucose. In addition, we calculated estimated glomerular filtration rates (eGFRs) by using the CKD-EPI equation. We also performed risk factor analysis with the Chi-squared test and by logistic regression. Seventy-two patients (6.3% of total new outpatients) complained of foamy urine; of these, there were 59 males with a median age of 65.5 years (range, 36-87 years). Of the 72 patients, 16 (22.2%) had overt proteinuria. We found that diabetes, poor renal function (high Cr, BUN, low eGFR), increased serum phosphate, and increased serum glucose were associated with overt proteinuria. Multiple logistic regression analysis showed that serum Cr and serum phosphate were associated with overt proteinuria. The ACR was available for 38 patients, and in this subgroup, 12 (31.6%) showed microalbuminuria or overt proteinuria. In this subgroup, a high serum Cr was the only statistically significant risk factor. Among patients who complained of foamy urine, approximately 20% had overt proteinuria, and increased serum Cr and phosphate were statistically significant risk factors.
Blood Urea Nitrogen ; Calcium ; Creatinine ; Glomerular Filtration Rate ; Glucose ; Humans ; Logistic Models ; Male ; Medical Records ; Outpatients ; Phosphates ; Proteinuria ; Risk Factors ; Uric Acid