Energy spectrum computed tomography (CT) has become a promising approach for the differential diagnosis of tumor subtypes. To explore the value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma (ccRCC) and type II papillary renal cell carcinoma (pRCC). Forty-two cases of high-grade ccRCC and 28 cases of type II pRCC were retrospectively reviewed. All region of interest (ROI) measurements were maintained consistently between the two-phase contrast-enhanced examinations. The ROIs encompassed as much of the enhancing areas of the lesions as possible. Energy spectrum CT parameters of all cases, including the 70 keV (HU) value, normalized iodine concentration (NIC), and energy spectrum curve slope were recorded by two radiologists with over 10 years of experience in abdominal CT diagnosis. In the cortical phase (CP) and parenchymal phase (PP), the 70 keV (HU) value, NIC, and slope value of the energy spectrum curve of high-grade ccRCC were significantly higher than those of type II pRCC. In the CP, NIC showed the highest differential diagnosis efficiency for the two group tumors, with a sensitivity of 78.9% and a specificity of 77.0%. There was no statistical difference in tumor hemorrhage, tumor envelope, tumor morphology, tumor border, lymph node metastasis, embolism, renal pelvis invasion, or tumor calcification between the two tumor types. However, there was significant difference in the number of tumors (P = 0.019). Energy spectrum CT parameters are valuable for the differential diagnosis of high-grade ccRCC and type II pRCC.

This study aimed to employ deep learning techniques to analyze and validate an automatic prognostic biomarker for predicting outcomes following intracerebral hemorrhage (ICH). This study included patients with ICH whose onset-to-imaging time (OIT) was less than 6 h. Patients were randomly divided into training and test sets at a 7:3 ratio. Using the Resnet50 deep learning method, we extracted features from the hematoma and perihematomal edema (PHE) areas and constructed a 90-day prognosis prediction model using logistic regression. To evaluate predictive efficacy and clinical significance, we employed logistic regression to train three models: Clinical, Deep Score, and the combined Clinical-Deep Learning (Merge). Our study comprised 1098 patients (652 male, 446 female), with a mean Glasgow Coma Scale (GCS) score of 10. Univariate and multivariate analyses identified age, intraventricular hemorrhage (IVH), hematoma and PHE volume, and admission GCS score as independent prognostic factors. Additionally, 15 deep learning features were retained through LASSO regression. In the training set, the AUC values for the three models were as follows: Clinical model (0.88), Deep Score (0.91), and Merge model (0.94). In the test set, the Merge model exhibited a significantly higher AUC value than the other models. Calibration curves revealed satisfactory calibration of the Merge model nomogram in both training and test sets. Our Merge model nomogram is an objective and effective prognostic tool, offering personalized risk assessments for 90-day functional outcomes in patients with ICH.

Computed tomography (CT) image features of chromophobe renal cell carcinoma (ChRCC) and papillary renal cell carcinoma (PRCC) are, occasionally, sometimes difficult to identify. However, spectral CT might provide quantitative parameters to differentiate them. To differentiate between ChRCC and PRCC with quantitative parameters using spectral CT. Forty cases of RCC confirmed with pathological tests were analyzed retrospectively (27 cases of PRCC and 13 cases of ChRCC). All patients underwent non-enhanced CT and dual-phase contrast-enhanced CT scans. For each lesion, the CT value of monochromatic images as well as iodine and water concentrations were measured, and the slope of spectrum curve was calculated. Data were analyzed using Student’s t-test. Sensitivity and specificity of the quantitative parameters were analyzed using the receiver operating characteristic (ROC) curve. During the cortex phase (CP) and parenchyma phase (PP), the CT value and slope of spectrum curve of ChRCC were higher than those of PRCC, and significant differences were observed at low energy levels (40–70 keV). Normalized iodine concentration of ChRCC and that of PRCC was significantly different during CP and PP (P < 0.05). The water (iodine) concentrations of ChRCC and PRCC in CP and PP were not statistically different (P > 0.05). All the ROCs for parameters were above the reference line. Spectral CT may help increase the diagnostic accuracy of differentiating PRCC from ChRCC using a quantitative analysis.

Background: Anti-angiogenic drugs have become a research hotspot in recent years. However, dynamically observing their therapeutic effect at different time points during treatment is a clinical problem. Purpose: To explore the feasibility of the quantitative parameters of spectral computed tomography (CT) in evaluating the anti-angiogenic effect of bevacizumab on rat C6 glioma. Material and Methods: Twenty-six male Sprague-Dawley rats were used to establish the C6 glioma model. The rats were randomly divided into the experimental group (n = 13) and control group (n = 13). The experimental group was intraperitoneally injected with 0.2 µL/g bevacizumab every day, whereas the control group was injected with the same dose of normal saline every day for one week. Spectral CT scanning was performed on the 4th and 8th days after treatment; meanwhile, the brain tissues were collected by heart perfusion for H&E staining, and VEGF and HIF-1α immunohistochemical staining. Results: On the 4th and 8th days, significant differences in the 70-keV single-energy CT value, slope of the energy spectrum curve, and iodine concentration were found between the experimental group and the control group. Correlation analysis between immunohistochemistry and quantitative parameters of spectral CT showed that the single energy CT value of 70 keV, slope of the energy spectrum curve, and concentration of iodine were positively correlated with VEGF and HIF-1α at different time points in the experimental group and the control group. Conclusion: Spectral CT multi-parameter imaging can be employed as a new method to evaluate the anti-angiogenic effect of bevacizumab on rat C6 glioma.