Multiparametric MRI manifestations of the spontaneous intratumoral coagulative necrosis in HCC.

PURPOSE: To investigate the MRI manifestations of the spontaneous intratumoral coagulative necrosis (iCN) in patients with hepatocellular carcinoma (HCC) and its value in predicting the postoperative early recurrence (≤ 2 years). METHODS: Patients with HCC who underwent preoperative multiparametric MRI between January 2015 and February 2019 were enrolled in this retrospective study. The MRI manifestations of iCNs on TIWI, T2WI, and ADC were recorded. The sensitivity and specificity of MRI for the detection of iCNs were also evaluated. A multivariable Cox proportional hazards model and the Kaplan-Meier method were used to verify the value of histologically-confirmed and MRI-identified iCNs, respectively, in predicting early recurrence. RESULTS: A total of 163 patients (median age, 56 years; interquartile range, 49-64 years; 139 men) with HCCs were evaluated, of whom 27(16.6%) had histologically-confirmed iCNs. MRI identified 92.6% (25 of 27; 95% confidence interval [CI] 74.2%, 98.7%) of iCNs (sensitivity), with a specificity of 79.4% (78 of 136; 95% CI 71.4%, 85.7%), based on non-enhancement on post-contrast MRI. And the MRI-identified iCNs were characterized by a similar appearance to surrounding tumour tissue shown on pre-contrast MRI but not enhanced on post-contrast MRI. The multivariable Cox proportional hazards model revealed that only the presence of histologically-confirmed iCN was independently associated with early HCC recurrence (hazard ratio = 2.73; 95% CI 1.20, 6.21; P = 0.017). The Kaplan-Meier curve showed that the presence of MRI-identified iCN was also associated with early recurrence (P < 0.001). CONCLUSION: Multiparametric MRI identified iCNs with high sensitivity and modest specificity. The presence of iCNs is associated with early HCC recurrence.

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses.

OBJECTIVES: To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses. MATERIALS AND METHODS: A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model's performance. RESULTS: Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity. CONCLUSION: The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

Sex-Specific Computed Tomography Abdominal Fat and Skeletal Muscle Characteristics in Type 2 Diabetic Retinopathy Patients With/Without Comorbid Diabetic Kidney Disease.

RATIONALE AND OBJECTIVES: To investigate differences in sex-specific computed tomography abdominal fat and skeletal muscle (SM) characteristics between type 2 diabetic retinopathy (DR) patients with and without diabetic kidney disease (DKD). MATERIALS AND METHODS: This retrospective study included type 2 diabetes mellitus DR patients with/without DKD between January 2019 and July 2021. Visceral adipose tissue (VAT), subcutaneous adipose tissue, perirenal adipose tissue (PAT), intramuscular adipose tissue, and SM areas were measured. Univariate and multivariate logistic regression analyses were used to analyze risk factors for DKD. Correlation and multiple linear regression analyses were used to clarify the association between computed tomography abdominal fat, SM characteristics, and cystatin C. RESULTS: Two hundred and forty-one patients were enrolled and divided into DR with DKD group (n = 142) and DR without DKD group (n = 99). In men, hypertension (OR: 5.21; 95%CI: 1.93-14.05; p = 0.001), diastolic pressure (OR: 1.07; 95%CI: 1.01-1.12; p = 0.011), hemoglobin (OR: 0.94; 95%CI: 0.92-0.97; p < 0.001) and PAT attenuation value (OR: 1.09; 95%CI: 1.01-1.17; p = 0.026) were independent risk factors for DKD progression in DR patients, while the VAT index (VATI) (OR: 1.03; 95%CI: 1.01-1.05; p = 0.014) was an independent risk factor for female patients. Multiple linear regression analysis revealed significant correlations between hypertension (ß = 0.22, p = 0.002) and hemoglobin (ß = -0.53, p < 0.001) with cystatin C in men, and a significant correlation between VATI and cystatin C (ß = 0.35, p = 0.037) in women after adjustment for confounders. CONCLUSION: Female DR patients with elevated VAT level may suffer from a higher risk of DKD than that in male patients.