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Abstract Background For chronic hepatitis B virus (HBV) infection patients, increasing evidence has demonstrated the effectiveness of expanding the indications and applicable population for antiviral therapy. However, the expanded indication of antiviral therapy for hepatocellular carcinoma (HCC) remains to be further explored. Methods 196 HBV-related HCC patients who received radical hepatectomy and nucleos(t)ide analogues (NAs) therapy at Sichuan Provincial People’s Hospital were enrolled in this study. HCC recurrence, overall survival (OS), early virological (VR) and biochemical responses (BR) of patients were compared between different NAs therapy and the use of anti-programmed cell death protein 1 (PD-1) therapy. Results NAs therapy at different timing of surgery was a strong independent risk factor for postoperative recurrence and overall mortality of HBV-related HCC patients. Furthermore, in HCC patients who received postoperative anti-PD-1 therapy, patients with HBV DNA

Abstract Background Gastric cancer (GC) is a common malignant disease worldwide. Aberrant miRNAs expression contributes to malignant cells behaviour, and in preclinical research, miRNA targeting has shown potential for improving GC therapy. Our present study demonstrated that miR-632 promotes GC progression in a trefoil factor 1 (TFF1)-dependent manner. Methods We collected GC tissues and serum samples to detect miR-632 expression using real-time PCR. A dual-luciferase reporter assay was used to identify whether miR-632 directly regulates TFF1 expression. Tube formation and endothelial cell recruitment assays were performed with or without miR-632 treatment. Western blot and in situ hybridization assays were performed to detect angiogenesis and endothelial recruitment markers that are affected by miR-632. Results Our results showed that miR-632 is highly expressed in GC tissue and serum and negatively associated with TFF1 in GC. miR-632 improves tube formation and endothelial cell recruitment by negatively regulating TFF1 in GC cells. Recombinant TFF1 reversed miR-632-mediated angiogenesis. TFF1 is a target gene of miR-632. Conclusions Our study demonstrated that miR-632 promotes GC progression by accelerating angiogenesis in a TFF1-dependent manner. Targeting of miR-632 may be a potential therapeutic approach for GC patients.

Abstract Background Gastric cancer (GC) is one of the most common malignant diseases with high morbidity and mortality, especially in Asian countries. During the GC developing progress, TIPE2, a member of TNF-alpha induced protein 8-like (TNFAIP8L) family, may play important roles. However, the molecular mechanisms of TIPE2 contributing to cell proliferation and tumor growth are poorly understood in GC. We performed flow cytometry to detect the cell cycle of TIPE2-knockdown GC cells under lipopolysaccharide (LPS) stimulation. Methods We measured TIPE2 expression in tumor samples from 46 human GC patients at mRNA level by Realtime PCR and in 68 pairs of GC tissues at protein level by immunohistochemistry. We established stable TIPE2 knockdown SGC7901 and BGC823 cell lines and performed CCK-8 and EdU proliferation assays under the stimulation of LPS. And then we analyzed AKT, IκBα and ERK phosphorylation levels, as well as cycle related proteins CDK4 and CyclinD3 in the stable TIPE2 knockdown SGC7901 and BGC823 cells. Results Our present studies indicated that the expression of TIPE2 was significantly decreased in tumor tissues compared to distant mucosa tissues in human GC patients. TIPE2 inhibited proliferation stimulated by LPS in SGC7901 and BGC823 cells. Silencing of TIPE2 significantly decreased cell G0/G1 phase ratio and increased G2/M phase. TIPE2 knockdown SGC7901 and BGC823 cells declined AKT and IκBα phosphorylation. TIPE2’s action on GC cell cycle was. Conclusions Our results demonstrated that TIPE2 is a novel tumor suppressor gene that inhibits GC growth may mediated via AKT and IκBα phosphorylated activation. We revealed that TIPE2 may effectively interdict neoplasm development, which has potential clinical application values for GC targeted therapies.

Background: Lymph node metastasis (LNM) of gastric cancer is an important prognostic factor regarding longterm survival. But several imaging techniques which are commonly used in stomach cannot satisfactorily assess the gastric cancer lymph node status. They can not achieve both high sensitivity and specificity. As a kind of machinelearning methods, Support Vector Machine has the potential to solve this complex issue. Methods: The institutional review board approved this retrospective study. 175 consecutive patients with gastric cancer who underwent MDCT before surgery were included. We evaluated the tumor and lymph node indicators on CT images including serosal invasion, tumor classification, tumor maximum diameter, number of lymph nodes, maximum lymph node size and lymph nodes station, which reflected the biological behavior of gastric cancer. Univariate analysis was used to analyze the relationship between the six image indicators with LNM. A SVM model was built with these indicators above as input index. The output index was that lymph node metastasis of the patient was positive or negative. It was confirmed by the surgery and histopathology. A standard machine-learning technique called k-fold cross-validation (5-fold in our study) was used to train and test SVM models. We evaluated the diagnostic capability of the SVM models in lymph node metastasis with the receiver operating characteristic (ROC) curves. And the radiologist classified the lymph node metastasis of patients by using maximum lymph node size on CT images as criterion. We compared the areas under ROC curves (AUC) of the radiologist and SVM models. Results: In 175 cases, the cases of lymph node metastasis were 134 and 41 cases were not. The six image indicators all had statistically significant differences between the LNM negative and positive groups. The means of the sensitivity, specificity and AUC of SVM models with 5-fold cross-validation were 88.5%, 78.5% and 0.876, respectively. While the diagnostic power of the radiologist classifying lymph node metastasis by maximum lymph node size were only 63.4%, 75.6% and 0.757. Each SVM model of the 5-fold cross-validation performed significantly better than the radiologist. Conclusions: Based on biological behavior information of gastric cancer on MDCT images, SVM model can help diagnose the lymph node metastasis preoperatively. [ABSTRACT FROM AUTHOR]

Abstract Purpose Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. Methods A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. Results At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680–0.720), 17.73 mm (95% CI: 16.08–19.39), and 3.11 mm (95% CI: 2.67–3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). Conclusions The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Abstract Objective To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). Methods A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. Results The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640–0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. Conclusions A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

Abstract Background The aim was to investigate the prognostic value of MR apparent diffusion coefficients (ADC) using histogram analysis (HA) in predicting disease-free survival (DFS) of cervical cancer after chemo-radiation therapy. Methods We retrospectively analyzed 103 women with pathologically proven squamous cell uterine cancer who received chemo-radiation therapy between 2009 and 2013. All patients were followed up for more than 2 years. Pre-treatment MR images were retrieved and imported for HA using an in-house developed software program based on 3D Slicer. Regions of interest of whole tumors were drawn manually on DWI with reference to T2WI. HA features (mean, max, min, 50, 10, 90%, kurtosis, and skewness) were extracted from apparent diffusion coefficient (ADC) maps and compared between the recurrence and non-recurrence groups after the 2-year follow-up. Univariate and multivariate Cox regression analysis was used to correlate ADC HA features and relevant clinical variables (age, grade, maximal diameter of tumor, FIGO stage, SCC-Ag) with DFS. Results One hundred three patients with stage IB-IV cervical cancers were followed up for 2.0–94.6 months (median 48.9 months). Twenty patients developed recurrence within 2 years. In the recurrence group, the min (P = 0.001) and 10% (P = 0.048) ADC values were significantly lower than those of the non-recurrence group. Univariate and multivariate Cox regression analysis revealed that ADCmin (P = 0.006, HR = 0.110) was significantly correlated with DFS. Conclusion Pre-treatment volumetric ADCmin in histogram analysis is an independent factor that is correlated with DFS in cervical cancer patients treated with chemo-radiation therapy.

Abstract Background To investigate the performance of quantitative indicators of MRI in early prediction of the response of gastrointestinal stromal tumor (GIST) to targeted therapy in a patient-based study. Methods MRI examinations were performed on 62 patients with GIST using 1.5 T scanners before and at two and 12 weeks after treatment with targeted agents. The longest diameter (LD) and contrast-to-noise ratio (CNR) of the tumors were measured by T2-weighted imaging (T2WI), and the apparent diffusion coefficient (ADC) was determined using diffusion-weighted imaging (DWI). The pre-therapy and early percentage changes (%Δ) of the three parameters were compared with regard to their abilities to differentiate responder and non- responder patients, using ROC curves. Results There were 42 patients in responder and 20 in non-responder group. After two weeks of therapy, the percentage changes in the ADC and LD were significantly different between the two groups (ADC: responder 30% vs. non- responder 1%, Z = − 4.819, P

Abstract Background Lymph node metastasis (LNM) of gastric cancer is an important prognostic factor regarding long-term survival. But several imaging techniques which are commonly used in stomach cannot satisfactorily assess the gastric cancer lymph node status. They can not achieve both high sensitivity and specificity. As a kind of machine-learning methods, Support Vector Machine has the potential to solve this complex issue. Methods The institutional review board approved this retrospective study. 175 consecutive patients with gastric cancer who underwent MDCT before surgery were included. We evaluated the tumor and lymph node indicators on CT images including serosal invasion, tumor classification, tumor maximum diameter, number of lymph nodes, maximum lymph node size and lymph nodes station, which reflected the biological behavior of gastric cancer. Univariate analysis was used to analyze the relationship between the six image indicators with LNM. A SVM model was built with these indicators above as input index. The output index was that lymph node metastasis of the patient was positive or negative. It was confirmed by the surgery and histopathology. A standard machine-learning technique called k-fold cross-validation (5-fold in our study) was used to train and test SVM models. We evaluated the diagnostic capability of the SVM models in lymph node metastasis with the receiver operating characteristic (ROC) curves. And the radiologist classified the lymph node metastasis of patients by using maximum lymph node size on CT images as criterion. We compared the areas under ROC curves (AUC) of the radiologist and SVM models. Results In 175 cases, the cases of lymph node metastasis were 134 and 41 cases were not. The six image indicators all had statistically significant differences between the LNM negative and positive groups. The means of the sensitivity, specificity and AUC of SVM models with 5-fold cross-validation were 88.5%, 78.5% and 0.876, respectively. While the diagnostic power of the radiologist classifying lymph node metastasis by maximum lymph node size were only 63.4%, 75.6% and 0.757. Each SVM model of the 5-fold cross-validation performed significantly better than the radiologist. Conclusions Based on biological behavior information of gastric cancer on MDCT images, SVM model can help diagnose the lymph node metastasis preoperatively.