Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
Introduction The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features. Methods We retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: post- or pre-menopausal, clinical presentation, invasion depth, FIGO stage, lymphaticmetastasis. The following MRI features were evaluated: tumor dimension, cystic degeneration or necrosis, hemorrhage, signal intensity (SI) on T2-weighted images (T2WI), relative SI of lesion to myometrium on T2WI, T1WI, DWI, ADCmax, ADCmin, ADCmean (RSI-T2, RSI-T1, RSI-DWI, RSIADCmax, RSI-ADCmin, RSI-ADCmean), ADCmax, ADCmin, ADCmean, the maximum, minimum and mean relative enhancement (RE) of lesion to myometrium on the arterial and venous phases (REAmax, REAmin, REAmean, REVmax, REVmin, REVmean). Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate prediction ability. Results The mean age of UCS was higher than EAC. UCS occurred more often in the postmenopausal patients. UCS and EAC did not significantly differ in depth of myometrial invasion, FIGO stage and lymphatic metastasis. The anterior-posterior and transverse dimensions were significantly larger in UCS than EAC. Cystic degeneration or necrosis and hemorrhage were more likely occurred in UCS. The SI of tumor on T2WI was more heterogeneous in UCS. The RSI-T2, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior dimension, transverse dimension, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. The AUC was 0.997 of the combined of ADCmax, REAmax and REVmax. Our study showed that ADCmax threshold value of 789.05 (10–3mm2/s) can differentiate UCS from EAC with 100% sensitivity, 76.5% specificity, and 0.76 AUC, REAmax threshold value of 0.45 can differentiate UCS from EAC with 88.2% sensitivity, 100% specificity, and 0.88 AUC. Conclusion Multiparametric MRI features may be utilized as a biomarker to distinguish UCS from EAC....
Background: The COVID-19 pandemic seemed to mainly involve the respiratory system, but it was realized that it could affect any organ, including the CNS. The pandemic has followed a wave-like trend, with its peaks being due to the COVID-19 different variants and the introduction of the vaccine, which led to an apparent reduction in hospitalizations but also brought about perplexities related to its adverse effects. The aim of this study was to analyze the changes in the use of head CT/contrast CT and their impacts on the onset of cerebrovascular disease in our emergency department during the COVID-19 period and the vaccine rollout. Methods: Patients ≥ 18 years old admitted to our emergency department from January 2018 to September 2021 were enrolled. The patients were divided into three groups. The COVID-19 period included patients who visited our emergency department from 1 March 2020 to 31 January 2021; the vaccine period was considered to range from 1 February 2021 to 30 September 2021. The patients who visited the emergency department from 1 January 2018 to 31 January 2020 were considered the controls. Results: We found an increase in head CT/contrast CT requests during the COVID-19 period and increase in head contrast CT during the vaccine period, without an increase in the incidence of cerebrovascular disease. Conclusions: The uncertainty regarding the possible thrombotic events associated with COVID-19 and its vaccine increased the relative use of head CT/contrast CT by about 20% compared to the control period...
Background: Implant subsidence is recognized as a complication of interbody stabilization, although its relevance remains ambiguous, particularly in terms of relating the effect of the position and depth of subsidence on the clinical outcome of the procedure. This study aimed to evaluate how implant positioning and size influence the incidence and degree of subsidence and to examine their implications for clinical outcomes. Methods: An observational study of 94 patients (157 levels) who underwent ACDF was conducted. Radiological parameters (implant position, implant height, vertebral body height, segmental height and intervertebral height) were assessed. Clinical outcomes were evaluated using the Visual Analogue Scale (VAS) and Neck Disability Index (NDI). Subsidence was evaluated in groups according to its degree, and statistical analyses were performed. Results: The findings revealed that implant-to-endplate ratio and implant height were significant risk factors associated with the incidence and degree of subsidence. The incidence of subsidence varied as follows: 34 cases (41.5%) exhibited displacement of the implant into the adjacent endplate by 2–3 mm, 32 cases (39%) by 3–4 mm, 16 cases (19.5%) by ≥4 mm and 75 (47.8%) cases exhibited no subsidence. Conclusions: The findings underscore that oversized or undersized implants relative to the disc space or endplate length elevate the risk and severity of subsidence....
Background To investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC). Methods The study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing the entire tumor, a total of 750 radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted (ceT1WI) imaging. To reduce dimensionality, reproducibility analysis by two radiologists and collinearity analysis were performed. The top six features were selected from each sequence alone, as well as their combination, using the minimum-redundancy maximum-relevance algorithm. Random forest, logistic regression, and support vector machine models were used to predict TIL levels in OTSCC, and 10-fold cross-validation was employed to assess the performance of the classifiers. Results Based on the features selected from each sequence alone, the ceT1WI models outperformed the T2WI models, with a maximum area under the curve (AUC) of 0.820 versus 0.754. When combining the two sequences, the optimal features consisted of one T2WI and five ceT1WI features, all of which exhibited significant differences between patients with low and high TILs (all P < 0.05). The logistic regression model constructed using these features demonstrated the best predictive performance, with an AUC of 0.846 and an accuracy of 80.9%. Conclusions ML-based T2WI and ceT1WI radiomics can serve as valuable tools for determining the level of TILs in patients with OTSCC....
Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis....
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