Assessing visibility and bone changes of spinal metastases in CT scans: a comprehensive analysis across diverse cancer types.

OBJECTIVES: To analyze the characteristics of spinal metastasis in CT scans across diverse cancers for effective diagnosis and treatment, using MRI as the gold standard. METHODS: A retrospective study of 309 patients from four centers, who underwent concurrent CT and spinal MRI, revealing spinal metastasis, was conducted. Data on metastasis including total number, volume, visibility on CT (visible, indeterminate, or invisible), and type of bone change were collected. Through chi-square and Mann-Whitney U tests, we characterized the metastasis across diverse cancers and investigated the variation in the intra-individual ratio representing the percentage of lesions within each category for each patient. RESULTS: Out of 3333 spinal metastases from 309 patients, 55% were visible, 21% indeterminate, and 24% invisible. Sclerotic and lytic lesions made up 47% and 43% of the visible and indeterminate categories, respectively. Renal cell carcinoma (RCC), prostate cancer, and hepatocellular carcinoma (HCC) had the highest visibility at 86%, 73%, and 67% (p < 0.0001, p < 0.0001, and p = 0.003), while pancreatic cancer was lowest at 29% (p < 0.0001). RCC and HCC had significantly high lytic metastasis ratios (interquartile range (IQR) 0.96-1.0 and 0.31-1.0, p < 0.001 and p = 0.005). Prostate cancer exhibited a high sclerotic lesion ratio (IQR 0.52-0.97, p < 0.001). About 39% of individuals had invisible or indeterminate lesions, even with a single visible lesion on CT. The intra-individual ratio for indeterminate and invisible metastases surpassed 18%, regardless of the maximal size of the visible metastasis. CONCLUSIONS: This study highlights the variability in characteristics of spinal metastasis based on the primary cancer type through unique lesion-centric analysis.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas. METHODS: In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score. RESULTS: The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance. CONCLUSIONS: The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Deep Learning-Based Automated Quantification of Coronary Artery Calcification for Contrast-Enhanced Coronary Computed Tomographic Angiography.

BACKGROUND: We evaluated the accuracy of a deep learning-based automated quantification algorithm for coronary artery calcium (CAC) based on enhanced ECG-gated coronary CT angiography (CCTA) with dedicated coronary calcium scoring CT (CSCT) as the reference. METHODS: This retrospective study included 315 patients who underwent CSCT and CCTA on the same day, with 200 in the internal and 115 in the external validation sets. The calcium volume and Agatston scores were calculated using both the automated algorithm in CCTA and the conventional method in CSCT. The time required for computing calcium scores using the automated algorithm was also evaluated. RESULTS: Our automated algorithm extracted CACs in less than five minutes on average with a failure rate of 1.3%. The volume and Agatston scores by the model showed high agreement with those from CSCT with concordance correlation coefficients of 0.90-0.97 for the internal and 0.76-0.94 for the external. The accuracy for classification was 92% with a 0.94 weighted kappa for the internal and 86% with a 0.91 weighted kappa for the external set. CONCLUSIONS: The deep learning-based and fully automated algorithm efficiently extracted CACs from CCTA and reliably assigned categorical classification for Agatston scores without additional radiation exposure.