A combined model was developed using contrast-enhanced CT-based radiomics features and clinical characteristics to predict liver fibrosis stages in patients with chronic liver disease (CLD). We retrospectively analyzed multiphase CT scans and biopsy-confirmed liver fibrosis. 160 CLD patients were randomly divided into 7:3 training/validation ratio. Clinical laboratory indicators associated with liver fibrosis were identified using Spearman\'s correlation and multivariate logistic regression correlation. Radiomic features were extracted after segmenting the entire liver from multiphase CT images. Feature dimensionality reduction was performed using RF-RFE, LASSO, and mRMR methods. Six radiomics-based models were developed in the training cohort of 112 patients. Internal validation was conducted on 48 randomly assigned patients. Receptor Operating Characteristic (ROC) curves and confusion matrices were constructed to evaluate model performance. The radiomics model exhibited robust performance, with AUC values of 0.810 to 1.000 for significant fibrosis, advanced fibrosis, and cirrhosis. The integrated clinical-radiomics model had superior diagnostic efficacy in the validation cohort, with AUC values of 0.836 to 0.997. Moreover, these models outperformed established biomarkers such as the aspartate aminotransferase to platelet ratio index (APRI) and the fibrosis 4 score (FIB-4), as well as the gamma glutamyl transpeptidase to platelet ratio (GPR), in predicting the fibrotic stages. The clinical-radiomics model holds considerable promise as a non-invasive diagnostic tool for the assessment and staging of liver fibrosis in the patients with CLD, potentially leading to better patient management and outcomes.

UNASSIGNED: To evaluate the performance of a clinical-radiomics model based on contrast-enhanced computed tomography (CE-CT) in assessing human epidermal growth factor receptor 2 (HER2) status in urothelial bladder carcinoma (UBC).
UNASSIGNED: From January 2022 to December 2023, 124 patients with UBC were classified into the training (n=100) and test (n=24) sets. CE-CT scans were performed on the patients. Univariate and multivariate analyses were conducted to identify independent predictors of HER2 status in patients with UBC. We employed eight machine learning algorithms to establish radiomic models. A clinical-radiomics model was developed by integrating radiomic signatures and clinical features. Receiver operating characteristic curves and decision curve analysis (DCA) were generated to evaluate and validate the predictive capabilities of the models.
UNASSIGNED: Among the eight classifiers, the random forest radiomics model based on CE-CT demonstrated the highest efficacy in predicting HER2 status, with area under the curve (AUC) values of 0.880 (95% CI: 0.813-0.946) and 0.814 (95% CI: 0.642-0.986) in the training and test sets, respectively. In the training set, the clinical-radiomics model achieved an AUC of 0.935, an accuracy of 0.870, a sensitivity of 0.881, and a specificity of 0.854. In the test set, the clinical-radiomics model achieved an AUC of 0.857, an accuracy of 0.760, a sensitivity of 0.643, and a specificity of 0.900. DCA analysis indicated that the clinical-radiomics model provided good clinical benefit.
UNASSIGNED: The radiomics nomogram demonstrates good diagnostic performance in predicting HER2 expression in patients with UBC.

Central lymph node (CLN) status is considered to be an important risk factor in patients with papillary thyroid carcinoma (PTC). The aim of the present study was to identify risk factors associated with CLN metastasis (CLNM) for patients with PTC based on preoperative clinical, ultrasound (US) and contrast-enhanced computed tomography (CT) characteristics, and establish a prediction model for treatment plans. A total of 786 patients with a confirmed pathological diagnosis of PTC between January 2021 to December 2022 were included in the present retrospective study, with 550 patients included in the training group and 236 patients enrolled in the validation group (ratio of 7:3). Based on the preoperative clinical, US and contrast-enhanced CT features, univariate and multivariate logistic regression analyses were used to determine the independent predictive factors of CLNM, and a personalized nomogram was constructed. Calibration curve, receiver operating characteristic (ROC) curve and decision curve analyses were used to assess discrimination, calibration and clinical application of the prediction model. As a result, 38.9% (306/786) of patients with PTC and CLNM(-) status before surgery had confirmed CLNM using postoperative pathology. In multivariate analysis, a young age (≤45 years), the male sex, no presence of Hashimoto thyroiditis, isthmic location, microcalcification, inhomogeneous enhancement and capsule invasion were independent predictors of CLNM in patients with PTC. The nomogram integrating these 7 factors exhibited strong discrimination in both the training group [Area under the curve (AUC)=0.826] and the validation group (AUC=0.818). Furthermore, the area under the ROC curve for predicting CLNM based on clinical, US and contrast-enhanced CT features was higher than that without contrast-enhanced CT features (AUC=0.818 and AUC=0.712, respectively). In addition, the calibration curve was appropriately fitted and decision curve analysis confirmed the clinical utility of the nomogram. In conclusion, the present study developed a novel nomogram for preoperative prediction of CLNM, which could provide a basis for prophylactic central lymph node dissection in patients with PTC.

OBJECTIVE: To investigate whether reducing the volume of intravenous iodinated contrast material injected during brain computed tomography (CT) provides reliable and accurate imaging without compromising diagnostic accuracy.
METHODS: This prospective study enrolled patients undergoing enhanced brain CT at a single tertiary hospital. Subjects who agreed to participate received a reduced dose of 60 ml contrast. The images were compared to an age and gender-matched control group who received the conventional 80 cc dose. Neuroradiologists assessed image quality and interpretation using a 5-point Likert scale with six specific domains. Based on ICC, inter-rater reliability was high at 0.873. Multiple linear regression predicted overall diagnostic accuracy based on contrast dose, age, and gender. Visual Grading Characteristics (VGC) analysis was also performed to quantify regional brain enhancement differences between the two contrast groups.
RESULTS: The study included 47 patients in the 60 cc group and 55 in the 80 cc control group. The results showed the 80 cc group had significantly higher enhancement ratings compared to 60 cc for all six structures assessed. The differences between groups ranged from -0.241 to -0.433 (p < 0.001) on the 5-point scale.The VGC analysis confirmed significantly greater brain parenchymal enhancement in the 80 cc group compared to the 60 cc group.
CONCLUSIONS: The findings indicate that reducing the intravenous iodinated contrast material volume during brain CT from 80 cc to 60 cc leads to a statistically significant reduction in image quality and diagnostic accuracy. Further research with larger cohorts is needed to confirm these findings and assess the clinical impact of these differences.

UNASSIGNED: Aortic valve calcification scoring plays an important role in predicting outcomes of transcatheter aortic valve replacement (TAVR). However, the impact of relative calcific density and its causal effect on peri-procedural complications due to sub-optimal valve expansion remains limited. This study aims to investigate the prognostic power of quantifying regional calcification in the device landing zone in the context of peri-procedural events and post-procedural complications based on pre-operative contrast computed tomography angiography (CCTA) images. Assess the effect of calcification on post-procedural device expansion and final configuration.
UNASSIGNED: We introduce a novel patient invariant topographic scheme for quantifying the location and relative density of landing zone calcification. The calcification was detected on CCTA images based on a recently developed method using automatic minimization of the false positive rate between aortic lumen and calcific segments. Multinomial logistic regression model evaluation and ROC curve analysis showed excellent classification power for predicting paravalvular leakage [area under the curve (AUC) = 0.8; P < 0.001] and balloon pre-dilation (AUC = 0.907; P < 0.001). The model exhibited an acceptable classification ability for left bundle branch block (AUC = 0.748; P < 0.001) and balloon post-dilation (AUC = 0.75; P < 0.001). Notably, all evaluated models were significantly superior to alternative models that did not include intensity-weighted regional volume scoring.
UNASSIGNED: TAVR planning based on contrast computed tomography images can benefit from detailed location, quantity, and density contribution of calcific deposits in the device landing zone. Those parameters could be employed to stratify patients who need a more personalized approach during TAVR planning, predict peri-procedural complications, and indicate patients for follow-up monitoring.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists.
METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists\' detection performance.
RESULTS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists.
CONCLUSIONS: The developed DL model can accurately detect colorectal cancer and improve radiologists\' detection performance, showing its potential as an effective computer-aided detection tool.
BACKGROUND: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

BACKGROUND: In the present study, we investigated the value of 18F-fibroblast-activation protein inhibitor (FAPI) positron emission tomography/computed tomography (18F-FAPI-42 PET/CT) to preoperative evaluations of appendiceal neoplasms and management for patients.
METHODS: This single-center retrospective clinical study, including 16 untreated and 6 treated patients, was performed from January 2022 to May 2023 at Southern Medical University Nanfang Hospital. Histopathologic examination and imaging follow-up served as the reference standard. 18F-FAPI-42 PET/CT was compared to 18F-fluorodeoxyglucose (18F-FDG) PET/CT and contrast-enhanced CT (CE-CT) in terms of maximal standardized uptake value (SUVmax), diagnostic efficacy and impact on treatment decisions.
RESULTS: The accurate detection of primary tumors and peritoneal metastases were improved from 28.6% (4/14) and 50% (8/16) for CE-CT, and 43.8% (7/16) and 85.0% (17/20) for 18F-FDG PET/CT, to 87.5% (14/16) and 100% (20/20) for 18F-FAPI-42 PET/CT. Compared to 18F-FDG PET/CT, 18F-FAPI-42 PET/CT detected more regions infiltrated by peritoneal metastases (108 vs. 43), thus produced a higher peritoneal cancer index (PCI) score (median PCI: 12 vs. 5, P < 0.01). 18F-FAPI-42 PET/CT changed the intended treatment plans in 35.7% (5/14) of patients compared to CE-CT and 25% (4/16) of patients compared to 18F-FDG PET/CT but did not improve the management of patients with recurrent tumors.
CONCLUSIONS: The present study revealed that 18F-FAPI-42 PET/CT can supplement CE-CT and 18F-FDG PET/CT to provide a more accurate detection of appendiceal neoplasms and improved treatment decision making for patients.

This study investigated the utility of imaging features, such as rim enhancement on contrast-enhanced CT (CECT), in predicting the prognosis of pancreatic ductal adenocarcinoma (PDAC). This retrospective study included 158 patients (84 men; mean age, 68 years) with pathologically confirmed PDAC. The following imaging features were evaluated on CECT by two radiologists: tumor size, tumor attenuation, and the presence of rim enhancement. Cox proportional hazards analysis was performed to identify the imaging and clinicopathological features for predicting disease-free survival (DFS) and overall survival (OS). Pathological features were compared with the presence of rim enhancement. Among the 158 patients, 106 (67%) underwent curative surgery (surgery group) and 52 (33%) received conservative treatment (non-surgery group). Rim enhancement was observed more frequently in the non-surgery group than in the surgery group (44% vs. 20%; p < 0.001). Rim enhancement showed significant associations with shorter DFS and OS in the surgery group (hazard ratios (HRs), 3.03 and 2.99; p < 0.001 and p = 0.003, respectively), whereas tumor size showed significant associations with shorter OS (HR per 1 mm increase, 1.08; p < 0.001). PDACs with rim enhancement showed significant associations with higher histological tumor grades (p < 0.001). PDAC with rim enhancement on CECT could predict poorer prognosis and more aggressive tumor grades.

暂无摘要。

BACKGROUND: The Glypican 3 (GPC3)-positive expression in Hepatocellular Carcinoma (HCC) is associated with a worse prognosis. Moreover, GPC3 has emerged as an immunotherapeutic target in advanced unresectable HCC systemic therapy. It is significant to diagnose GPC3-positive HCCs before therapy. Regarding imaging diagnosis of HCC, dynamic contrast-enhanced CT is more common than MRI in many regions.
OBJECTIVE: The aim of this study was to construct and validate a radiomics model based on contrast-enhanced CT to predict the GPC3 expression in hepatocellular carcinoma.
METHODS: This retrospective study included 141 (training cohort: n = 100; validation cohort: n = 41) pathologically confirmed HCC patients. Radiomics features were extracted from the Artery Phase (AP) images of contrast-enhanced CT. Logistic regression with the Least Absolute Shrinkage and Selection Operator (LASSO) regularization was used to select features to construct radiomics score (Rad-score). A final combined model, including the Rad-score of the selected features and clinical risk factors, was established. Receiver Operating Characteristic (ROC) curve analysis, Delong test, and Decision Curve Analysis (DCA) were used to assess the predictive performance of the clinical and radiomics models.
RESULTS: 5 features were selected to construct the AP radiomics model of contrast-enhanced CT. The radiomics model of AP from contrast-enhanced CT was superior to the clinical model of AFP in training cohorts (P < 0.001), but not superior to the clinical model in validation cohorts (P = 0.151). The combined model (AUC = 0.867 vs. 0.895), including AP Rad-score and serum Alpha-Fetoprotein (AFP) levels, improved the predictive performance more than the AFP model (AUC = 0.651 vs. 0.718) in the training and validation cohorts. The combined model, with a higher decision curve indicating more net benefit, exhibited a better predictive performance than the AP radiomics model. DCA revealed that at a range threshold probability approximately above 60%, the combined model added more net benefit compared to the AP radiomics model of contrastenhanced CT.
CONCLUSIONS: A combined model including AP Rad-score and serum AFP levels based on contrast-enhanced CT could preoperatively predict GPC3-positive expression in HCC.