Advanced biomedical imaging for accurate discrimination and prognostication of mediastinal masses.

BACKGROUND: To investigate the potential of radiomic features and dual-source dual-energy CT (DECT) parameters in differentiating between benign and malignant mediastinal masses and predicting patient outcomes. METHODS: In this retrospective study, we analysed data from 90 patients (38 females, mean age 51 ± 25 years) with confirmed mediastinal masses who underwent contrast-enhanced DECT. Attenuation, radiomic features and DECT-derived imaging parameters were evaluated by two experienced readers. We performed analysis of variance (ANOVA) and Chi-square statistic tests for data comparison. Receiver operating characteristic curve analysis and Cox regression tests were used to differentiate between mediastinal masses. RESULTS: Of the 90 mediastinal masses, 49 (54%) were benign, including cases of thymic hyperplasia/thymic rebound (n = 10), mediastinitis (n = 16) and thymoma (n = 23). The remaining 41 (46%) lesions were classified as malignant, consisting of lymphoma (n = 28), mediastinal tumour (n = 4) and thymic carcinoma (n = 9). Significant differences were observed between benign and malignant mediastinal masses in all DECT-derived parameters (p ≤ .001) and 38 radiomic features (p ≤ .044) obtained from contrast-enhanced DECT. The combination of these methods achieved an area under the curve of .98 (95% CI, .893-1.000; p < .001) to differentiate between benign and malignant masses, with 100% sensitivity and 91% specificity. Throughout a follow-up of 1800 days, a multiparametric model incorporating radiomic features, DECT parameters and gender showed promising prognostic power in predicting all-cause mortality (c-index = .8 [95% CI, .702-.890], p < .001). CONCLUSIONS: A multiparametric approach combining radiomic features and DECT-derived imaging biomarkers allows for accurate and noninvasive differentiation between benign and malignant masses in the anterior mediastinum.

Multiparametric Evaluation of Radiomics Features and Dual-Energy CT Iodine Maps for Discrimination and Outcome Prediction of Thymic Masses.

RATIONALE AND OBJECTIVES: To investigate the diagnostic value of radiomics features and dual-source dual-energy CT (DECT) based material decomposition in differentiating low-risk thymomas, high-risk thymomas, and thymic carcinomas. MATERIALS AND METHODS: This retrospective study included 32 patients (16 males, mean age 66 ± 14 years) with pathologically confirmed thymic masses who underwent contrast-enhanced DECT between 10/2014 and 01/2023. Two experienced readers evaluated all patients regarding conventional radiomics features, as well as DECT-based features, including attenuation (HU), iodine density (mg/mL), and fat fraction (%). Data comparisons were performed using analysis of variance and chi-square statistic tests. Receiver operating characteristic curve analysis and Cox-regression tests were used to discriminate between low-risk/high-risk thymomas and thymic carcinomas. RESULTS: Of the 32 thymic tumors, 12 (38%) were low-risk thymomas, 11 (34%) were high-risk thymomas, and 9 (28%) were thymic carcinomas. Values differed significantly between low-risk thymoma, high-risk thymoma, and thymic carcinoma regarding DECT-based features (p ≤ 0.023) and 30 radiomics features (p ≤ 0.037). The area under the curve to differentiate between low-risk/high-risk thymomas and thymic cancer was 0.998 (95% CI, 0.915-1.000; p < 0.001) for the combination of DECT imaging parameters and radiomics features, yielding a sensitivity of 100% and specificity of 96%. During a follow-up of 60 months (IQR, 35-60 months), the multiparametric approach including radiomics features, DECT parameters, and clinical parameters showed an excellent prognostic power to predict all-cause mortality (c-index = 0.978 [95% CI, 0.958-0.998], p = 0.003). CONCLUSION: A multiparametric approach including conventional radiomics features and DECT-based features facilitates accurate, non-invasive discrimination between low-risk/high-risk thymomas and thymic carcinomas.

Changes in Statin Prescription Patterns in Patients Admitted to an Australian Geriatric Subacute Unit.

BACKGROUND: Assessment of demographic and clinical factors influencing the decision of statin discontinuation in the elderly population admitted to subacute geriatric unit. The aim of this study is to assess the clinical factors impacting the decision-making process of statin discontinuation in the elderly. METHODS: We retrospectively assessed changes in statin discontinuation and prescription among patients (≥60 years old) discharged from a geriatric evaluation and management unit by reviewing hospital digital medical records at Western Health - The Williamstown Hospital over a 12-month period from 4 February 2012 until 4 February 2013 inclusive. The main outcome of the study was to determine the independent predictors of statin discontinuation using logistic regression analysis. RESULTS: Of the studied population, 46% were already prescribed statins prior to their admission. Statins were discontinued in 17.5% of patients at discharge. Predictors of statin de-prescription included octogenarian status, primary prevention indication, poor functional recovery, residential care facility discharge destination and lower cognitive function. The presence of previous cardiovascular disease history and the burden of comorbidities were not predictors of statin discontinuation. CONCLUSIONS: We observed that factors that conveyed poor prognosis such as advanced age, poor functional recovery, worse cognitive function, being discharged to a residential care facility as well as primary prevention indication for statin prescription are predictors of statin discontinuation in the geriatric unit.