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Background: Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose: To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods: 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results: Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion: DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.
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BACKGROUND: In clinical practice, Preoperative differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma is challenging but critical for treatment decisions. OBJECTIVE: This study investigated the discriminatory power of the stretched-exponential model and fractional-order calculus model parameters for hepatocellular carcinoma versus intrahepatic cholangiocarcinoma in orthotopic xenograft nude mice. METHODS: Prototype orthotopic xenograft models of hepatocellular carcinoma and intrahepatic cholangiocarcinoma were developed using 20 nude mice divided into two groups and separately transplanted with MHCC97H and HUCCT1 cells. Readout-segmented diffusion-weighted imaging with multiple b-values (0-2000 s/mm2) was obtained using a 3.0-T magnetic resonance imaging scanner. The apparent diffusion coefficient was calculated using the mono-exponential model. The distributed diffusion coefficient and intravoxel water molecular diffusion heterogeneity (α) were calculated using the stretched-exponential model. The diffusion coefficient (D), fractional-order derivative in space (ß), and spatial parameter (µ) were calculated using the fractional-order calculus model. The liver and tumor specimens of nude mice were immunostained after euthanasia to clarify the liver cancer type. Differences in diffusion-related parameters between the groups were evaluated using Mann-Whitney U-test and univariate logistic analysis. Receiver operating characteristic curves were used to assess the diagnostic efficacy of each parameter. P<0.05 was deemed significant. RESULTS: α, D, and ß were significant discriminators between the groups. The area under the curve for these three variables was 0.890, 0.830, and 0.870, respectively, with cutoff values of 0.491, 0.435, and 0.782, respectively. CONCLUSION: The stretched-exponential model parameters α and the fractional-order calculus model parameters D and ß showed high diagnostic efficacy in discriminating intrahepatic cholangiocarcinoma from hepatocellular carcinoma in orthotopic xenograft nude mouse models.
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PURPOSE: To compare the ability of a clinical-computed tomography (CT) model vs. 2D and 3D radiomics models for predicting occult peritoneal metastasis (PM) in patients with advanced gastric cancer (AGC). METHODS: In this retrospective study, we included 49 patients with occult PM and 49 control patients (without PM) who underwent preoperative CT and subsequent surgery between January 2016 and December 2018. Clinical information and CT semantic features were collected, and CT radiomics features were extracted. A predictive clinical-CT model was created using multivariate logistic regression. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. These models were validated with an external cohort (n = 30). Receiver operating characteristics curve with area under the curve (AUC), sensitivity, and specificity were used to evaluate predictive performance. RESULTS: Tumor size, mild ascites, and serum CA125 were independent factors predictive of occult PM. The clinical-CT model of these independent factors showed better diagnostic performance than 2D and 3D radiomics models. In the external validation cohort, the AUCs of different models were as follows-clinical-CT model: 0.853 (sensitivity, 66.7%; specificity, 93.3%); 2D radiomics model: 0.622 (sensitivity, 80.0%; specificity, 46.7%); and 3D radiomics model: 0.676 (sensitivity, 60.0%; specificity, 86.0%). The clinical-CT model nomogram showed good clinical predictive efficiency to assess occult PM. CONCLUSION: The clinical-CT model was better than the radiomics models in predicting occult PM in AGC.
