Background The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). Methods We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. Results The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. Conclusions Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.

Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models. Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures. Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

BackgroundOur aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and MaterialsOf the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis.ResultsThe average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946.ConclusionsTwo classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.

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Background Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas. Methods Clinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression. Results For the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score. Conclusions The radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.

Background: An association between inflammatory bowel disease (IBD) [which includes ulcerative colitis (UC) and Crohn’s disease (CD)] and IgA nephropathy (IgAN) has been discovered in observational studies, but the causal relationship is still unknown. The aim of this study was to clarify the causal link between IBD (which includes UC and CD) and IgAN via a two-sample Mendelian randomization (MR) analysis.Methods: Eligible single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) for analyses and were obtained from the publicly available genome-wide association study (GWAS) summary statistics. Inverse-variance weighting (IVW), Mendelian randomization–Egger (MR-Egger) regression, the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test, and the weighted median were utilized to obtain the results. The MR-PRESSO test and MR-Egger regression were also performed to detect and correct horizontal pleiotropy. The Cochran’s Q test and “leave-one-out” analysis were also conducted to assess the stability and reliability of the MR results.Results: This study found that IBD, UC, and CD all had significant positive causal effects on IgAN risk (IBD: OR = 1.58, 95% CI 1.15–2.16, p = 4.53 × 10–3; UC: OR = 1.55, 95% CI 1.14–2.11, p = 4.88 × 10–3; CD: OR = 1.57, 95% CI 1.21–2.03, p = 5.97 × 10–4). No significant horizontal pleiotropic effect was found for the causal association between IBD, UC, CD, and the risk of IgAN. Cochran’s Q test identified no evidence of heterogeneity for the IV estimates. The “leave-one-out” sensitivity analysis also revealed that the MR results were robust.Conclusion: The results of this two-sample MR analysis supported that IBD, UC, and CD were causally associated with the risk of IgAN, while there was no sufficient evidence for the causal effect of IgAN on IBD, UC, or CD. Our findings provide theoretical support and a new perspective for the diagnosis and treatment of these two diseases.

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Background: Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for implementation of personalized diagnosis and treatment plan. This study aims to build radiomics deep learning (DL) models using computed tomography (CT) data for preoperative differential diagnosis of common cystic tumors of pancreas.Methods: Clinical and CT data of 193 patients with PCN was collected for the study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 with mucinous cystadenoma (MCA) and 39 with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT scans. The radiomics model and radiomics-DL model were constructed using support vector machines (SVMs). Moreover, in combination with clinical and radiological features, the best combined feature set was obtained by Akaike information criterion (AIC) analysis, and the fused model was constructed using logistic regression.Results: For SCA differential diagnosis, the fused model performed the best and obtained average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. As for MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communicate with pancreatic duct and radiomics scores.Conclusions: The radiomics, radiomics-DL and fused model based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial to the formulation of individualized management strategies.