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Background: Computed tomography (CT)‐detected extramural venous invasion (EMVI) has been identified as an independent factor that can be used for risk stratification and prediction of prognosis in patients with gastric cancer (GC). Overall survival (OS) is identified as the most important prognostic indicator for GC patients. However, the molecular mechanism of EMVI development and its potential relationship with OS in GC are not fully understood. In this radiogenomics‐based study, we sought to investigate the molecular mechanism underlying CT‐detected EMVI in patients with GC, and aimed to construct a genomic signature based on EMVI‐related genes with the goal of using this signature to predict the OS. Materials and Methods: Whole mRNA genome sequencing of frozen tumor samples from 13 locally advanced GC patients was performed to identify EMVI‐related genes. EMVI‐prognostic hub genes were selected based on overlapping EMVI‐related differentially expressed genes and OS‐related genes, using a training cohort of 176 GC patients who were included in The Cancer Genome Atlas database. Another 174 GC patients from this database comprised the external validation cohort. A risk stratification model using a seven‐gene signature was constructed through the use of a least absolute shrinkage and selection operator Cox regression model. Results: Patients with high risk score showed significantly reduced OS (training cohort, p = 1.143e‐04; validation cohort, p = 2.429e‐02). Risk score was an independent predictor of OS in multivariate Cox regression analyses (training cohort, HR = 2.758; 95% CI: 1.825–4.169; validation cohort, HR = 2.173; 95% CI: 1.347–3.505; p < 0.001 for both). Gene functions/pathways of the seven‐gene signature mainly included cell proliferation, cell adhesion, regulation of metal ion transport, and epithelial to mesenchymal transition. Conclusions: A CT‐detected EMVI‐related gene model could be used to predict the prognosis in GC patients, potentially providing clinicians with additional information regarding appropriate therapeutic strategy and medical decision‐making. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a preoperative risk stratification model for predicting disease-free survival (DFS) based on contrast-enhanced multidetector computed tomography (ceMDCT) images in patients with gastric cancer (GC) undergoing radical surgery. Methods: We retrospectively enrolled patients with GC who underwent ceMDCT followed by radical surgery. A preoperative risk stratification model was constructed (including risk factor selection, risk status scoring, and risk level assignment) using Cox proportional hazard regression and log-rank analyses in the training cohort; the model was tested in the validation cohort. A nomogram was used to compare the preoperative risk stratification model with a postoperative DFS prediction model. Results: A total of 462 patients (training/validation: 271/191) were included. The ceMDCT features of T category (score of 0 or 2), N category (0, 1, 2, or 3), extramural vessel invasion (0 or 2), and tumor location (0 or 1) were selected to construct the preoperative risk stratification model, with 4 risk levels defined based on risk score. There were significant differences in DFS among the risk levels in both cohorts (p < 0.001). The predictive value of the preoperative model was similar to that of the postoperative model, with concordance indices of 0.791 (95% CI, 0.743–0.837) and 0.739 (95% CI, 0.666–0.812), respectively, in the training cohort and 0.762 (95% CI, 0.696–0.828) and 0.738 (95% CI, 0.684–0.792), respectively, in the validation cohort. Conclusion: A preoperative risk stratification model based on ceMDCT images could be used to predict DFS and thus classify GC cases into various risk levels. [ABSTRACT FROM AUTHOR]

Purpose: Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector‐row computed tomography (MDCT)‐guided prognostic models to direct follow‐up strategy and improve prognosis. Methods: A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor‐node‐metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use. Results: In the two validation cohorts, the established four‐feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log‐rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2‐yr disease‐free survival (DFS) prediction was most effective (time‐dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage. Conclusions: The proposed MDCT‐guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the efficacy of MRI-based radiomics and clinical models in predicting MTM-HCC. Additionally, to investigate the ability of the radiomics model designed for MTM-HCC identification in predicting disease-free survival (DFS) in patients with HCC. Methods: A total of 336 patients who underwent oncological resection for HCC between June 2007 and March 2021 were included. 127 patients in Cohort1 were used for MTM-HCC identification, and 209 patients in Cohort2 for prognostic analyses. Radiomics analysis was performed using volumes of interest of HCC delineated on pre-operative MRI images. Radiomics and clinical models were developed using Random Forest algorithm in Cohort1 and a radiomics probability (RP) of MTM-HCC was obtained from the radiomics model. Based on the RP, patients in Cohort2 were divided into a RAD-MTM-HCC (RAD-M) group and a RAD-non-MTM-HCC (RAD-nM) group. Univariate and multivariate Cox regression analyses were employed to identify the independent predictors for DFS of patients in Cohort2. Kaplan–Meier curves were used to compare the DFS between different groups pf patients based on the predictors. Results: The radiomics model for identifying MTM-HCC showed AUCs of 0.916 (95% CI: 0.858–0.960) and 0.833 (95% CI: 0.675–0.935), and the clinical model showed AUCs of 0.760 (95% CI: 0.669–0.836) and 0.704 (95% CI: 0.532–0.843) in the respective training and validation sets. Furthermore, the radiomics biomarker RP, portal or hepatic vein tumor thrombus, irregular rim-like arterial phase hyperenhancement (IRE) and AFP were independent predictors of DFS in patients with HCC. The DFS of RAD-nM group was significantly higher than that of the RAD-M group (p <.001). Conclusion: MR-based clinical and radiomic models have the potential to accurately diagnose MTM-HCC. Moreover, the radiomics signature designed to identify MTM-HCC also can be used to predict prognosis in patients with HCC, realizing the diagnostic and prognostic aims at the same time. [ABSTRACT FROM AUTHOR]

Background: To investigate the association between computed tomography (CT)-detected extramural venous invasion (EMVI)-related genes and immunotherapy resistance and immune escape in patients with gastric cancer (GC). Methods: Thirteen patients with pathologically proven locally advanced GC who had undergone preoperative abdominal contrast-enhanced CT and radical resection surgery were included in this study. Transcriptome sequencing was multidetector performed on the cancerous tissue obtained during surgery, and EMVI-related genes (P value for association < 0.001) were selected. A single-sample gene set enrichment analysis algorithm was also used to divide all GC samples (n = 377) in The Cancer Genome Atlas (TCGA) database into high and low EMVI-immune related groups based on immune-related differential genes. Cluster analysis was used to classify EMVI-immune-related genotypes, and survival among patients was validated in TCGA and Gene Expression Omnibus (GEO) cohorts. The EMVI scores were calculated using principal component analysis (PCA), and GC samples were divided into high and low EMVI score groups. Microsatellite instability (MSI) status, tumor mutation burden (TMB), response rate to immune checkpoint inhibitors (ICIs), immune escape were compared between the high and low EMVI score groups. Hub gene of the model in pan-cancer analysis was also performed. Results: There were 17 EMVI-immune-related genes used for cluster analysis. PCA identified 8 genes (PCH17, SEMA6B, GJA4, CD34, ACVRL1, SOX17, CXCL12, DYSF) that were used to calculate EMVI scores. High EMVI score groups had lower MSI, TMB and response rate of ICIs, status but higher immune escape status. Among the 8 genes used for EMVI scores, CXCL12 and SOX17 were at the core of the protein–protein interaction (PPI) network and had a higher priority in pan-cancer analysis. Immunohistochemical analysis showed that the expression of CXCL12 and SOX17 was significantly higher in CT-detected EMVI-positive samples than in EMVI-negative samples (P < 0.0001). Conclusion: A CT-detected EMVI gene signature could be a potential negative biomarker for ICIs treatment, as the signature is negatively correlated with TMB, and MSI, resulting in poorer prognosis. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1–2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. Methods: A total of 151 pathologically confirmed 1–2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. Results: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1–2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1–2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. Conclusions: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1–2-cm gGISTs. Key Points: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1–2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1–2-cm gGISTs. [ABSTRACT FROM AUTHOR]

Purpose: To assess liver necroinflammation in HCV patients undergone antiviral therapy by Gd-EOB-DTPA-enhanced MRI with histopathologic analyses as reference. Methods: HCV patients were enrolled in this prospective study before antiviral treatment between 09–2016 and 07–2017. Unenhanced MR, Gd-EOB-DTPA-enhanced MR, and liver biopsy were performed before and 24 weeks after treatment of daclatasvir with asunaprevir (DAA). DWI was obtained using a breath-hold single-shot echo planar spin-echo sequence. Twenty minutes after administration of Gd-EOB-DTPA, the relative enhancement (RE) and the contrast enhancement index (CEI) were recorded. Liver necroinflammatory activity grades (G0–18) were categorized on the Ishak Scoring systems. CEI, RE, and DWI of baseline and 24 weeks after treatment were compared by paired t test. Relationship between MR parameters and histologic scores was evaluated by Pearson's correlation. Receiver operating characteristic analysis evaluated the measurements' diagnostic performance. MRI variability between two readers was assessed using the intraclass correlation coefficient.Results Results: A decrease of liver necroinflammatory activity grade (p < 0.0001) was detected in final cohort (n = 21; mean age 44 years; 23 to 67 years; 11 F, 10 M). Statistical results of 42 person-times in 21 patients at baseline and follow-up showed CEI and ADC were significantly different (p = 0.006 and 0.036) across histologic grades of liver necroinflammation. Significant increase of CEI, RE, and ADC (p = 0.0004, 0.0032, 0.0110) 24 weeks after DAA treatment was seen. Additionally, CEI was correlated to necroinflammatory grade (r = − 0.596, p = 0.006). AUROC for CEI, ADC, and CEI combined with ADC to differentiate patients with none and mild (G0–6) from patients with moderate and severe necroinflammation (G7–18) was 0.834 (95% CI 0.712–0.956, 0.724(95% CI 0.565–0.884) and 0.837(95% CI 0.717–0.956). Conclusion: Gd-EOB-DTPA-enhanced MRI by CEI could be used as a noninvasive imaging biomarker to distinguish grades of necroinflammatory activity in patients with HCV after DAAs therapy at early stage and CEI combined with ADC could get a better diagnostic accuracy. [ABSTRACT FROM AUTHOR]