Role of Quantitative Diffusion-Weighted Imaging in Differentiating Benign and Malignant Orbital Masses

Aim To determine the role of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) values in differentiating benign and malignant orbital masses. Materials and Methods After obtaining institutional ethical board approval and informed consent from all patients, an observational study was done for a period of 24 months in the radiology department of a tertiary care hospital in South India. Conventional magnetic resonance imaging and DWI using a 3T scanner was done for all patients with suspected orbital mass lesion. ADC value and clinicohistopathological correlation were studied for every patient. Chi-square test was used to compare the signal characteristics of DWI and ADC maps between benign and malignant lesions. A comparison of mean ADC values for benign and malignant masses was performed using Student’s t-test for independent samples. The cut-off value for ADC was obtained using the receiver operating characteristic (ROC) curve. Results Of 44 patients with orbital lesions, 70% were benign and 30% were malignant. There was a significant difference in the mean ADC values of benign and malignant orbital masses. Using ROC curve analysis, an optimal ADC threshold of 1.26 × 10−3 mm2/s was calculated for the prediction of malignancy with 100% sensitivity, 80.65% specificity, and 86.36% accuracy (95% confidence interval: 0.872, 1.00, p < 0.0001). Two ADC thresholds were used to characterize the orbital masses with more than 90% confidence. Conclusion Quantitative assessment of ADC is a useful noninvasive diagnostic tool for differentiating benign and malignant orbital masses. Malignant orbital lesions demonstrate significantly lower ADC values as compared with benign lesions

Ocular hemodynamic alterations in patients of Type 2 diabetes mellitus

Purpose: To study ocular blood flow velocity in the ophthalmic artery (OA), central retinal artery (CRA), and posterior ciliary artery in patients with Type 2 diabetes. Materials and Methods: The retrobulbar circulation in 46 eyes of Type 2 diabetic patients was compared with age-matched 21 nondiabetic eyes. The diabetic subjects were further divided into diabetics with no-clinical retinopathy (n = 24) and with either preproliferative or proliferative retinopathy (n = 22). Philips HD11XE machine was used for performing Color Doppler imaging. Results: The end-diastolic velocity (EDV) in OA was 3.21 cm/s in the preproliferative/proliferative group as compared to 6.0 and 8.5 cm/s in no-retinopathy and control group, respectively. The peak systemic velocities and EDVs of CRA in the diabetic group were significantly lower than those of normal subjects regardless of the retinopathy. The resistivity index (RI) of CRA was 0.81 in diabetic group and 0.70 in control group, which was statistically significant. Conclusion: The study showed reduced blood flow velocity and increased RI in Type 2 diabetic patients as compared to normal healthy individuals. There are significant changes noted in retrobulbar flow in patients with diabetic retinopathy as compared to patients without retinopathy

Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study

Peer reviewed: TrueFunder: National Institute for Health and Care Research; FundRef: http://dx.doi.org/10.13039/501100000272Objective: To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination. Design: Prospective multi-reader diagnostic accuracy study. Setting: United Kingdom. Participants: One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months. Main outcome measures: Accuracy and pass rate of the artificial intelligence compared with radiologists across 10 mock FRCR rapid reporting examinations (each examination containing 30 radiographs, requiring 90% accuracy rate to pass). Results: When non-interpretable images were excluded from the analysis, the artificial intelligence candidate achieved an average overall accuracy of 79.5% (95% confidence interval 74.1% to 84.3%) and passed two of 10 mock FRCR examinations. The average radiologist achieved an average accuracy of 84.8% (76.1-91.9%) and passed four of 10 mock examinations. The sensitivity for the artificial intelligence was 83.6% (95% confidence interval 76.2% to 89.4%) and the specificity was 75.2% (66.7% to 82.5%), compared with summary estimates across all radiologists of 84.1% (81.0% to 87.0%) and 87.3% (85.0% to 89.3%). Across 148/300 radiographs that were correctly interpreted by >90% of radiologists, the artificial intelligence candidate was incorrect in 14/148 (9%). In 20/300 radiographs that most (>50%) radiologists interpreted incorrectly, the artificial intelligence candidate was correct in 10/20 (50%). Most imaging pitfalls related to interpretation of musculoskeletal rather than chest radiographs. Conclusions: When special dispensation for the artificial intelligence candidate was provided (that is, exclusion of non-interpretable images), the artificial intelligence candidate was able to pass two of 10 mock examinations. Potential exists for the artificial intelligence candidate to improve its radiographic interpretation skills by focusing on musculoskeletal cases and learning to interpret radiographs of the axial skeleton and abdomen that are currently considered “non-interpretable.