An MRI-Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors.

BACKGROUND: Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection. PURPOSE: To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors. STUDY TYPE: Retrospective. POPULATION: In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors. FIELD STRENGTH/SEQUENCES: Fast-spin-echo (FSE) T1 -weighted and fat-suppressed FSE T2 -weighted imaging on a 1.5T and 3.0T MRI. ASSESSMENT: T1 and fat-suppressed T2 -weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI). STATISTICAL TESTS: Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test. RESULTS: In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model. DATA CONCLUSION: The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Proton density fat fraction measurements of rotator cuff muscles: Accuracy, repeatability, and reproducibility across readers and scanners.

PURPOSE: To determine the accuracy, repeatability, and reproducibility of magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) measurements of rotator cuff muscles between two readers and three different scanners. METHODS: Twenty-seven volunteers underwent serial shoulder MRI examinations of both left and right sides on one 1.5-T MRI scanner and two 3.0-T MRI scanners. Two independent readers measured muscular PDFF of the supraspinatus, infraspinatus/teres minor muscle, and subscapularis. MR spectroscopy-based proton density fat fraction (MRS-PDFF) was regarded as the reference standard for assessing accuracy. A "coffee break" examination method was used to test the repeatability of each scanner. Bland-Altman plots, Pearson correlation, and linear regression analysis were used to assess bias and linearity. The Wilcoxon signed-rank test and Friedman test were applied to evaluate repeatability and reproducibility. RESULTS: MRI-PDFF measurements indicated strong linearity (R2 = 0.749) and small bias (-0.18%) in comparison with the MRS-PDFF measurements. A very strong positive Pearson correlation (r = 0.955-0.986) between the PDFF estimates of the two repeat scans indicated excellent repeatability. The PDFF measurements showed high reproducibility, with a strong positive Pearson correlation (r = 0.668-0.698) and a small mean bias (-0.04 to -0.10%) across different scanners. CONCLUSION: MRI-PDFF measurements of rotator cuff muscles were highly accurate, repeatable, and reproducible across different readers and scanners, leading us to the conclusion that PDFF can be a reliable and robust quantitative imaging biomarker for longitudinal or multi-center studies.