Use of Computed Tomography-Based Texture Analysis to Differentiate Benign From Malignant Salivary Gland Lesions.

OBJECTIVE: Salivary gland lesions show overlapping morphological findings and types of time/intensity curves. This research aimed to evaluate the role of 2-phase multislice spiral computed tomography (MSCT) texture analysis in differentiating between benign and malignant salivary gland lesions. METHODS: In this prospective study, MSCT was carried out on 90 patients. Each lesion was segmented on axial computed tomography (CT) images manually, and 33 texture features and morphological CT features were assessed. Logistic regression analysis was used to confirm predictors of malignancy ( P < 0.05 was considered to be statistically significant), followed by receiver operating characteristics analysis to assess the diagnostic performance. RESULTS: Univariate logistic regression analysis revealed that morphological CT features (shape, size, and invasion of adjacent tissues) and 17 CT texture parameters had significant differences between benign and malignant lesions ( P < 0.05). Multivariate binary logistic regression demonstrated that shape, invasion of adjacent tissues, entropy, and inverse difference moment were independent factors for malignant tumors. The diagnostic accuracy values of multivariate binary logistic models based on morphological parameters, CT texture features, and a combination of both were 87.8%, 90%, and 93.3%, respectively. CONCLUSIONS: Two-phase MSCT texture analysis was conducive to differentiating between malignant and benign neoplasms in the salivary gland, especially when combined with morphological CT features.

Atypical imaging features of the primary spinal cord glioblastoma: A case report.

BACKGROUND: Primary spinal cord (PSC) glioblastoma (GB) is an extremely rare but fatal primary tumor of the central nervous system and associated with a poor prognosis. While typical tumor imaging features are generally easy to recognize, glioblastoma multiforme can have a wide range of imaging findings. Atypical GB is often misdiagnosed, which usually delays the optimal time for treatment. In this article, we discuss a clinical case of pathologically confirmed PSC GB under the guise of benign tumor imaging findings, as well as the most recent literature pertaining to PSC GB. CASE SUMMARY: A 70-year-old female complained of limb weakness lasting more than 20 d. Irregular masses were observed inside and outside the left foramina of the spinal canal at C7-T1 on medical imaging. Based on the imaging features, radiologists diagnosed the patient with schwannoma. Tumor resection was performed under general anesthesia. The final histopathological findings revealed a final diagnosis of PSC GB, world health organization Grade IV. The patient subsequently underwent a 4-wk course of radiotherapy (60 Gy in 20 fractions) combined with temozolomide chemotherapy. The patient was alive at the time of submission of this manuscript. CONCLUSION: Atypical GB presented unusual imaging findings, which led to misdiagnosis. Therefore, a complete recognition of imaging signs may facilitate early accurate diagnosis.

The diagnostic value of a non-contrast computed tomography scan-based radiomics model for acute aortic dissection.

ABSTRACT: To investigate the diagnostic value of a computed tomography (CT) scan-based radiomics model for acute aortic dissection.For the dissection group, we retrospectively selected 50 patients clinically diagnosed with acute aortic dissection between October 2018 and November 2019, for whom non-contrast CT and CT angiography images were available. Fifty individuals with available non-contrast CT and CT angiography images for other causes were selected for inclusion in the non-dissection group. Based on the aortic dissection locations on the CT angiography images, we marked the corresponding regions-of-interest on the non-contrast CT images of both groups. We collected 1203 characteristic parameters from these regions by extracting radiomics features. Subsequently, we used a random number table to include 70 individuals in the training group and 30 in the validation group. Finally, we used the Lasso regression for dimension reduction and predictive model construction. The diagnostic performance of the model was evaluated by a receiver operating characteristic (ROC) curve.Fourteen characteristic parameters with non-zero coefficients were selected after dimension reduction. The accuracy, sensitivity, specificity, and area under the ROC curve of the prediction model for the training group were 94.3% (66/70), 91.2% (31/34), 97.2% (35/36), and 0.988 (95% confidence interval [CI]: 0.970-0.998), respectively. The respective values for the validation group were 90.0% (27/30), 94.1% (16/17), 84.6% (11/13), and 0.952 (95% CI: 0.883-0.986).Our non-contrast CT scan-based radiomics model accurately facilitated acute aortic dissection diagnosis.