Value of CT to detect radiographically occult injuries of the proximal femur in elderly patients after low-energy trauma: determination of non-inferiority margins of CT in comparison with MRI.

PURPOSE: To determine the margins of non-inferiority of the sensitivity of CT and the sample size needed to test the non-inferiority of CT in comparison with MRI. MATERIALS AND METHODS: During a 2-year period, elderly patients with suspected radiographically occult post-traumatic bone injuries were investigated by CT and MRI in two institutions. Four radiologists analyzed separately the CT and MRI examinations to detect post-traumatic femoral injuries. Their sensitivities at CT (SeCT) and MRI (SeMRI) were calculated with the reference being a best valuable comparator (consensus reading of the MRI and clinical follow-up). ROC analysis followed by an exact test (Newcombe's approach) was performed to assess the 95% confidence interval (CI) for the difference SeCT-SeMRI for each reader. A sample size calculation was performed based on our observed results by using a one-sided McNemar's test. RESULTS: Twenty-nine out of 102 study participants had a post-traumatic femoral injury. SeCT ranged between 83 and 93% and SeMRI ranged between 97 and 100%. The 95% CIs for (SeCT-SeMRI) were [- 5.3%, + 0.8%], (pR1 = 0.1250), [- 4.5%; + 1.2%] (pR2 = 0.2188), [- 3.4%; + 1.1%] (pR3 = 0.2500) to [- 3.8%; + 1.6%] (pR4 = 0.3750) according to readers, with a lowest limit for 95% CIs superior to a non-inferiority margin of (- 6%) for all readers. A population of 440 patients should be analyzed to test the non-inferiority of CT in comparison with MRI. CONCLUSION: CT and MRI are sensitive for the detection of radiographically occult femoral fractures in elderly patients after low-energy trauma. The choice between both these modalities is a compromise between the most available and the most sensitive technique. KEY POINTS: • The sensitivity of four separate readers to detect radiographically occult post-traumatic femoral injuries in elderly patients after low-energy trauma ranged between 83 and 93% at CT and between 97 and 100% at MRI according to a best valuable comparator including MRI and clinical follow-up. • CT is a valuable alternative method to MRI for the detection of post-traumatic femoral injuries in elderlies after low-energy trauma if a 6% loss in sensitivity can be accepted in comparison with MRI. • The choice between CT and MRI is a compromise between the most available and the most sensitive technique.

Assessment of MRI abnormalities of the sacroiliac joints and their ability to predict axial spondyloarthritis: a retrospective pilot study on 110 patients.

OBJECTIVE: To assess sacroiliac joint (SIJ) modifications on MRI and their ability to predict axial spondyloarthritis (SpA) with the purpose of identifying parameters for future prospective studies. METHODS: Retrospective study was carried out of 110 consecutive patients referred for SIJ MRI with coronal, axial short TI inversion recovery (STIR), and axial T1 sequences over 6 months. Factors associated with SpA, including MRI SIJ modifications (fat deposition, structural abnormalities on T1-weighted images, and bone marrow edema [BME] on STIR sequences) and age were explored using multivariate logistic regression. The reference diagnosis was made 1-1.5 years later based on clinical, radiological, and biological findings, according to Assessment of SpondyloArthritis International Society (ASAS) criteria. RESULTS: Twenty-eight patients were diagnosed with SpA (female/male: 19/9, age 41 ± 13 years). Abnormal findings were found in up to 21 % of patients without SpA (including 11 % with BME), versus 64 % of SpA patients (50 % with BME). A threshold age of 42.6 years was found to discriminate SpA patients (ROC AUC: 0.71, 95 % CI: 0.59-0.81). BME location in the sacral (OR: 7.07 [1.05, 47.6], p = 0.044) and both sacral and iliac areas (OR: 36.0 [5.61, 231], p = 0.0002), as well as age (OR: 0.95 [0.92, 0.98], p = 0.0019) were found to be independent predictors of SpA. 83.6 % of patients were effectively diagnosed using BME location and patient age in a classification and regression tree (CART) algorithm (sensitivity: 61 %, specificity: 91 %, PPV: 71 %, NPV: 87 %). CONCLUSION: The BME location combined with the patient's age (threshold 42.6 years) could help predict SpA. Further studies are required before these features can be used by radiologists to boost their confidence in reporting SIJ MRI.