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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: 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]

Predicting gastric cancer disease-free survival (DFS) and identifying patients probably with high risk are imperative for more appropriate clinical treatment plans. Compared with CT-based radiomics researches adopting linear Cox proportional hazards models, deep neural networks can perform nonlinear transformations and investigate complex associations of image features with prognosis. Exploring shared information between post-contrast CT (with better visual enhancement) and pre-contrast CT (with few side effects and contraindications) is another challenge. In this work, a cross-phase adversarial domain adaptation (CPADA) framework is proposed to adapt a deep DFS prediction network (DDFS-Net) from arterial phase to pre-contrast phase. The DDFS-Net is designed for feature learning and trained by optimizing the average negative log function of Cox partial likelihood. The CPADA maps the feature space of pre-contrast phase (target) to arterial phase (source) in an adversarial manner by measuring Wasserstein distance. The proposed methods are evaluated on a dataset of 249 gastric cancer patients by concordance index, receiver operating characteristic curves, and Kaplan-Meier survival curves. The results demonstrate that our DDFS-Net outperforms linear survival analysis methods, and the CPADA works better than supervised learning and direct transfer schemes.Clinical Relevance-This work enables preoperative DFS prediction and risk stratification in gastric cancer. It is feasible and effective to infer a patient's risk of failure given a pre-contrast CT image by DDFS-Net adapted by CPADA.

OBJECTIVE. The purpose of this study was to analyze causes of discrepancies between restaging MRI and pathologic findings in the assessment of morphologic indicators of tumor response in patients with rectal cancer who have undergone neoadjuvant treatment. MATERIALS AND METHODS. MRI and pathologic data from 57 consecutively registered patients who underwent neoadjuvant treatment and total mesorectal excision between August 2015 and July 2018 were retrospectively analyzed. The sensitivity and specificity of restaging MRI in determining tumor regression grade, T category, N category, circumferential resection margin, and extramural vascular invasion were calculated with pathologic results as the reference standard. One-by-one comparisons between MRI and pathologic findings were conducted to identify causes of discrepancies. RESULTS. The sensitivity of MRI in determining tumor regression grades 3-5 was 77.1%; T3 and T4 category, 100.0%; node-positive disease, 75.0%; circumferential resection margin, 87.5%; and extramural vascular invasion, 91.7%. The specificity values were 72.7%, 62.5%, 70.7%, 85.7%, and 64.4%. Overstaging was mainly caused by misinterpretation of fibrotic areas as residual tumor. Inflammatory cell infiltration could appear as high signal intensity in fibrotic areas on DW images, an appearance similar to that of residual tumor. Edematous mucosa and submucosa adjacent to the tumor and muscularis propria could also be mistaken for residual tumor because of their intermediate signal intensity on T2-weighted MR images. CONCLUSION. MRI was prone to overstaging of disease. Discrepancies between MRI and pathologic findings were mainly caused by misinterpretation of fibrosis. Inflammatory cell infiltration, pure mucin, edematous mucosa and submucosa adjacent to the tumor, and muscularis propria could also be misinterpreted as residual tumor.

OBJECTIVE. This study aimed to investigate the 3-year progression-free survival (PFS) of patients with stage T4a gastric cancer with extramural vessel invasion (EMVI) detected with contrast-enhanced (CE) MDCT. In addition, we investigated the possibility that CT of EMVI could improve clinical nodal (N) staging. MATERIALS AND METHODS. This retrospective study included 143 patients with T4a gastric cancer. Clinical staging was performed with CE-MDCT. All patients underwent radical gastrectomy with extended lymphadenectomy, adjuvant chemotherapy, and conventional follow-up visits. Potential prognostic factors, including CE-MDCT-detected N status, pathologic N status, EMVI detected at CT, tumor location or growth pattern, histologic type or tumor differentiation, and tumor size, were recorded. Survival estimates for PFS were obtained using the Kaplan-Meier product limit for the following patient subgroups: EMVI positive-N positive, EMVI positive-N negative, EMVI negative-N positive, and EMVI negative-N negative. Hazard ratios for 3-year PFS were generated using a Cox proportional hazard regression analysis. RESULTS. The frequency of EMVI detected at CT was 55.9% (80/143). The 3-year PFS rates were 25.0% for the EMVI positive-N positive group, 53.1% for the EMVI positive-N negative group, 75.6% for the EMVI negative-N positive group, and 64.7% for the EMVI negative-N negative group. The EMVI positive-N positive subgroup 3-year PFS rate was significantly lower than that of the other three groups ( p < 0.05, log-rank test). Using Cox proportional hazards regression analysis, EMVI positive-N positive status was found to be an independent factor for reduced 3-year PFS, with a hazard ratio of 2.169 (95% CI, 1.300-3.618; p = 0.003). CONCLUSION. EMVI detected at CT, combined with N status detected with CE-MDCT, could be used as a valuable preoperative prognostic factor in patients with T4a gastric cancer.