The rupture risk of intracranial saccular aneurysm: a case-control study based on a three-dimensional computed tomography angiography model.

Background: Assessing the risk of rupture in intracranial aneurysms is crucial. Advancements in medical imaging now allow for three-dimensional (3D) assessments of aneurysms, providing a more detailed understanding of their morphology and associated risks. This study aimed to compare the 3D morphological parameters of ruptured and unruptured intracranial saccular aneurysms (ISAs) using computed tomography angiography (CTA) and to analyze risk factors linked to ISA rupture. Methods: This retrospective case-control study included patients diagnosed with ISAs via CTA, for which data were sourced from both the Emergency Department and Inpatient Unit in The First Affiliated Hospital of Jinan University. The patients were categorized into rupture and unrupture groups. We used 3D-Slicer (version 5.2.2, Slicer Community) to construct morphological models of the ISAs and their parent arteries. These models facilitated assessments of intracranial aneurysmal volume (IAV), aneurysmal surface area (ASA), and maximum sectional area (MSA). Differences in 3D morphological parameters between ruptured and unruptured ISAs were then analyzed. For statistical analysis, we first performed single factor analysis on the data, constructed a receiver operating characteristic (ROC) curve one by one with statistically significant parameters, and screened out ROC curves that met the sample requirements. Second, we performed multiparameter logistic regression analysis to construct a ROC curve model and analyzed its predictive performance. Results: The analysis encompassed 97 patients comprising 97 ISAs diagnosed from March 2016 to March 2022. Significant differences in morphological parameters were observed between the rupture and unrupture groups (P<0.05), including IAV, ASA, MSA, IAV/diameter (IAV/D), IAV/neck width (IAV/N), MSA/diameter (MSA/D), MSA/neck width (MSA/N), ASA/neck width (ASA/N), and ASA/MSA. It was found that the IAV, ASA, and MSA values of the rupture group were larger than those of the unrupture group. Meanwhile, the IAV/D, IAV/N, MSA/D, MSA/N, and ASA/N values were larger in the rupture group, while ASA/MSA and ASA/IAV were smaller. Conclusions: This study underscores the significance of specific morphological indicators, such as ASA/N and ASA/MSA, in predicting the rupture risk of ISAs. The IAV, MSA, and ASA parameters, especially in relation to diameter and neck width, provide crucial insights into the rupture potential of ISAs.

Quantitative evaluation of meniscus injury using synthetic magnetic resonance imaging.

BACKGROUND: Magnetic resonance imaging (MRI) can diagnose meniscal lesions anatomically, while quantitative MRI can reflect the changes of meniscal histology and biochemical structure. Our study aims to explore the association between the measurement values obtained from synthetic magnetic resonance imaging (SyMRI) and Stoller grades. Additionally, we aim to assess the diagnostic accuracy of SyMRI in determining the extent of meniscus injury. This potential accuracy could contribute to minimizing unnecessary invasive examinations and providing guidance for clinical treatment. METHODS: Total of 60 (n=60) patients requiring knee arthroscopic surgery and 20 (n=20) healthy subjects were collected from July 2022 to November 2022. All subjects underwent conventional MRI and SyMRI. Manual measurements of the T1, T2 and proton density (PD) values were conducted for both normal menisci and the most severely affected position of injured menisci. These measurements corresponded to the Stoller grade of meniscus injuries observed in the conventional MRI. All patients and healthy subjects were divided into normal group, degeneration group and torn group according to the Stoller grade on conventional MRI. One-way analysis of variance (ANOVA) was employed to compare the T1, T2 and PD values of the meniscus among 3 groups. The accuracy of SyMRI in diagnosing meniscus injury was assessed by comparing the findings with arthroscopic observations. The diagnostic efficiency of meniscus degeneration and tear between conventional MRI and SyMRI were analyzed using McNemar test. Furthermore, a receiver operating characteristic curve (ROC curve) was constructed and the area under the curve (AUC) was utilized for evaluation. RESULTS: According to the measurements of SyMRI, there was no statistical difference of T1 value or PD value measured by SyMRI among the normal group, degeneration group and torn group, while the difference of T2 value was statistically significant among 3 groups (P=0.001). The arthroscopic findings showed that 11 patients were meniscal degeneration and 49 patients were meniscal tears. The arthroscopic findings were used as the gold standard, and the difference of T1 and PD values among the 3 groups was not statistically significant, while the difference of T2 values (32.81±2.51 of normal group, 44.85±3.98 of degeneration group and 54.42±3.82 of torn group) was statistically significant (P=0.001). When the threshold of T2 value was 51.67 (ms), the maximum Yoden index was 0.787 and the AUC value was 0.934. CONCLUSIONS: The measurement values derived from SyMRI could reflect the Stoller grade, illustrating that SyMRI has good consistency with conventional MRI. Moreover, the notable consistency observed between SyMRI and arthroscopy suggests a potential role for SyMRI in guiding clinical diagnoses.

Prediction of Changes in Tumor Regression during Radiotherapy for Nasopharyngeal Carcinoma by Using the Computed Tomography-Based Radiomics.

This work aimed to explore the application value of computed tomography (CT)-based radiomics in predicting changes in tumor regression during radiotherapy for nasopharyngeal carcinoma. In this work, 144 patients with nasopharyngeal carcinoma who underwent concurrent chemoradiotherapy (CCRT) in our hospital from January 2015 to December 2021 were selected. The patients were divided into a radiosensitive group (79 cases) and an insensitive group (65 cases) according to the tumor volume shrinkage during radiotherapy. The 3D Slicer 4.10.2 software was used to delineate the tumor region of interest (ROI), and a total of 1223 radiomics features were extracted using the radiomics module under the software. After between-group and within-group consistency tests, one-way ANOVA, and LASSO dimensionality reduction, three omics features were finally selected for the establishment of predictive models. At the same time, the age, gender, tumor T stage and N stage, hemoglobin, and albumin of the patients were collected to establish a clinical prediction model. The results showed that compared with logistic regression, decision tree, random forest, and AdaBoost models, the SVM model based on CT radiomics features had the best performance in predicting tumor regression changes during tumor radiotherapy (training group area under the receiver operating characteristic curve (AUC): 0.840 (95% confidence interval (CI): 0.764-0.916); validation group: AUC: 0.810 (95% CI: 0.676-0.944)). Compared with the supported vector machine (SVM) prediction model based on clinical features, the SVM model based on radiomics features had better performance in predicting the change of retraction during tumor radiotherapy (training group: omics feature SVM model AUC: 0.84, clinical feature SVM model: 0.78; validation group: omics feature SVM model AUC: 0.8, clinical feature SVM model: 0.58, P = 0.044). Based on the radiomics characteristics and clinical characteristics of patients, a nomo prediction map was established, and the calibration curve shows good consistency, which can be visualized to assist clinical judgment. In this work, the prediction model composed of CT-based radiomic features combined with clinical features can accurately predict withdrawal changes during tumor radiotherapy, ensuring the accuracy of treatment planning, and minimizing the number of CT scans during radiotherapy.