MRI-Based Classification for Tibial Spine Fracture: Detection Efficacy, Classification Accuracy, and Reliability.

RATIONALE AND OBJECTIVES: Recently, a new MRI-based classification for evaluating tibial spine fractures (TSFs) was developed to aid in treating these injuries. Our objective was to assess the detection efficacy, classification accuracy, and reliability of this classification in detecting and grading TSFs, as well as its impact on treatment strategy, compared to the Meyers and McKeever (MM) classification. MATERIALS AND METHODS: A retrospective study included 68 patients with arthroscopically confirmed TSFs. All patients had plain radiography and conventional MRI of the affected knee before arthroscopy. Three experienced radiologists independently reviewed all plain radiographs and MRI data and graded each patient according to MM and MRI-based classifications. The detection efficacy, classification accuracy, and inter-rater agreement of both classifications were evaluated and compared, using arthroscopic findings as the gold standard. RESULTS: The final analysis included 68 affected knees. Compared to the MM classification, the MRI-based classification produced 22.0% upgrade of TSFs and 11.8% downgrade of TSFs. According to the reviewers, the fracture classification accuracy of the MRI-based classification (91.2-95.6%) was significantly higher than that of the MM classification (73.5-76.5%, p = 0.002-0.01). The fracture detection rate of MRI-based classification (94.1-98.5%) was non-significantly higher than that of the MM classification (83.8-89.7%, p = 0.07-0.4). The soft tissue injury detection accuracy for MRI-based classification was 91.2-94.1%. The inter-rater reliability for grading TSFs was substantial for both the MM classification (κ = 0.69) and MRI-based classification (κ = 0.79). CONCLUSION: MRI-based classification demonstrates greater accuracy and reliability compared to MM classification for detecting and grading TSFs and associated soft tissue injuries.

The validity and reproducibility of the thyroid imaging reporting and data system (TI-RADS) in categorization of thyroid nodules: Multicentre prospective study.

PURPOSE: To assess diagnostic validity and reproducibility of Thyroid Imaging Reporting and Data System (TI-RADS) for interpretation of thyroid nodules by thyroid ultrasonography (US). METHOD: A prospective multicentre study initially included 557 patients with clinically suspected thyroid nodules. After exclusion, a final cohort of 380 patients with 948 thyroid nodules detected by US were enrolled. Based on American College of Radiology (ACR) TI-RADS, three radiologists analysed all US examinations independently and assigned a TI-RADS category to each thyroid nodule. The final diagnosis was based on cytology which was used as reference standard for calculating diagnostic performance of TI-RADS for predicting malignant thyroid nodules. The Fleiss and weighted kappa (κ) statistics were applied to assess inter-observer agreement of morphological features and TI-RADS scoring results for thyroid nodules. Additionally, we made a simple screening among referring clinicians to assess the clinical response to application of TI-RADS. RESULTS: A total of 948 thyroid nodules were evaluated; 136 (14.3%) were malignant, and 812 (85.7%) were benign. The papillary carcinoma was the most common malignant thyroid nodules (81.6%). The best cut-off value for predicting malignant thyroid nodules was > TR3. On a lesion-based analysis, the TI-RADS had a sensitivity, specificity, and an accuracy of 98.3%, 90.9%, and 92.1%, respectively when regarding those thyroid nodules classified as > TR3 for predicting malignancy. The inter-observer agreement of the TI-RADS category was good (κ = 0.636). Ninety percent of referring clinicians accept TI-RADS. CONCLUSIONS: TI-RADS improves diagnostic performance of US for predicting malignant thyroid nodules with high validity and high reproducibility.