The CT and MRI features of benign calvarium and skull base osteoblastoma.

OBJECTIVE: We retrospectively reviewed the CT and MRI features of patients with benign osteoblastoma in the calvarium and skull base (CSBOB). METHODS: Nine cases of pathologically confirmed benign CSBOB were analysed retrospectively. The patients had undergone CT and/or MRI. Tumour location, size, and imaging features were reviewed and recorded. RESULTS: The patients included four males and five females with a mean age of 27.0 years (age 14-40 years). The tumours were located in the frontal bone in 3 patients, the occipital bone in 3 patients, and in the parietal bone, sphenoid bone, and skull base in 1 patient each. On CT, the tumours measured 5.1 ± 3.3 (1.8-8.4) cm. Seven tumours were shown to have caused expansile bony destruction with an eggshell appearance and varying degrees of calcification or matrix mineralization. Multiple septa were observed in 5 tumours. Intracranial growth was observed in 5 tumours. On MRI, 7 tumours showed heterogeneous hypo- to isointensity on T1WI. Heterogeneous high signal patterns with low signal rims and septa were observed in 6 tumours on T2WI, and 4 showed a fluid-fluid level. On contrast-enhanced imaging, 6 tumours showed peripheral and septal enhancement, and 2 showed the dural tail sign. CONCLUSIONS: Benign CSBOB is a rare tumour characterized by expansile bony destruction, septa, a sclerotic rim and calcification or matrix mineralization on CT and MRI. ADVANCES IN KNOWLEDGE: The findings from this study contribute to a better understanding of benign CSBOB and provide valuable imaging features that can aid in its diagnosis and differentiation from other tumours in the calvarium and skull base.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.

Radiologists' diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists' diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.