Prognostic Analysis of Prophylactic Hyperthermic Intraperitoneal Chemotherapy for Advanced Gastric Cancer: a Propensity Score-Matched Analysis.

PURPOSE: To investigate the efficacy of prophylactic hyperthermic intraperitoneal chemotherapy (HIPEC) for advanced gastric cancer (AGC). METHODS: We included 198 patients treated from December 2016 to January 2019; of these patients, the 132 who had clinical T4 gastric cancer were divided into a hyperthermic intraperitoneal chemotherapy group (HIPEC group) and a radical gastrectomy and D2 lymph node dissection group (control group). Because this study was retrospective, we used propensity score matching (PSM) to reduce selectivity bias; we then assessed risk factors for recurrence and compared prognosis in terms of survival in the gastrectomy and prophylactic HIPEC groups. RESULTS: Prophylactic HIPEC reduced the risk of postoperative peritoneal metastasis (PM: 27.5% vs. 10.5%, P = 0.015) and did not increase the risk of postoperative complications, but there was no significant difference in the effect on hepatic metastases or other distant metastases. Risk factors for recurrence included pT4 staging and positive lymph node metastases. Both disease-free survival (DFS: HR 0.592; 95% CI 0.354-0.990; P = 0.042) and peritoneal recurrence-free survival (PFS: HR 0.314; 95% CI 0.127-0.774; P = 0.008) were better in the prophylactic HIPEC group than in the gastrectomy-only group. In addition, there was no difference in the prognosis of patients between the two groups of raltitrexed (RT) and paclitaxel (PTX) for perfusion dosing. CONCLUSION: Our study showed that prophylactic HIPEC could prevent postoperative PM in patients with AGC and did not increase the incidence of postoperative complications. However, it was not found to be effective in the prevention of other metastases, such as hepatic metastases.

A web-based prediction model for long-term cancer-specific survival of middle-aged patients with early-stage gastric cancer: a multi-institutional retrospective study.

BACKGROUND: This study constructed and validated a prognostic model to evaluate long-term cancer-specific survival (CSS) in middle-aged patients with early gastric cancer (EGC). METHODS: We extracted clinicopathological data from relevant patients between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided the patients into a training group (N = 688) and a validation group (N = 292). In addition, 102 Chinese patients were enrolled for external validation. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict CSS. We used the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the predictive performance of the model. RESULTS: Univariate and multivariate COX regression analyses showed that tumor location, differentiation grade, N stage, chemotherapy, and number of regional nodes examined were independent risk factors for prognosis, and these factors were used to construct the nomogram. The C-index of the model in the training cohort, internal validation cohort, and external validation cohort was 0.749 (95% CI 0.699-0.798), 0.744 (95% CI 0.671-0.818), and 0.807 (95% CI 0.721-0.893), respectively. The calibration curve showed that the model had an excellent fit. The DCA curve showed that the model had good predictive performance and practical clinical value. CONCLUSION: This study developed and validated a new nomogram to predict CSS in middle-aged patients with EGC. The prediction model has unique and practical value and can help doctors carry out individualized treatment and judge prognosis.

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics.

OBJECTIVES: This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms. METHODS: A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA). RESULTS: The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822-0.945] and 0.802 [95% CI: 0.666-0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively. CONCLUSIONS: Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients.

The Predictive Value of a New Inflammatory-Nutritional Score for Quality of Life after Laparoscopic Distal Gastrectomy for Gastric Cancer.

We explored the predictive value of various inflammatory-nutritional indicators for postoperative quality of life (QoL) in gastric cancer (GC) patients undergoing laparoscopic distal gastrectomy (LDG) and developed a novel inflammatory-nutritional score (INS). In this study, 156 GC patients who underwent LDG were included. We used multiple linear regression to analyze the correlation between postoperative QoL and inflammatory-nutritional indicators. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct INS. Hemoglobin was positively correlated with physical functioning (ß =8.5; p = 0.003) and cognitive functioning (ß = 3.5; p = 0.038) 3 mo, after surgery. Prognostic nutritional index (PNI) was positively associated with global health status (ß =5.8; p = 0.043). Albumin-alkaline phosphatase ratio (AAPR) was negatively correlated with emotional functioning 12 mo, after surgery (ß = -5.7; p = 0.024). Neutrophil-lymphocyte ratio (NLR), Lymphocyte- monocyte ratio (LMR), AAPR, hemoglobin and PNI were selected using LASSO regression analysis to construct INS. The C-index values of the model in the training group and the validation group were 0.806 (95% CI, 0.719-0.893) and 0.758 (95% CI: 0.591-0.925), respectively. INS had particular predictive value for postoperative QoL in patients undergoing LDG and provided a reference for risk stratification and clinical practice.

Quantitative Radiological Features and Deep Learning for the Non-Invasive Evaluation of Programmed Death Ligand 1 Expression Levels in Gastric Cancer Patients: A Digital Biopsy Study.

RATIONALE AND OBJECTIVES: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological features and clinical factors to predict the PD-L1 expression level in GC. MATERIALS AND METHODS: 217 patients who received gastrectomy for GC were consecutively enrolled in this study, with 157 patients from center 1 as the training cohort and 60 patients from center 2 as the external validation cohort. 1205 quantitative radiomics features were extracted from preprocessed pre-operative contrast-enhanced CT images of enrolled patients. A radiological signature was computed using a regression random forest model and was integrated with clinical factors in a multilayer perceptron. The performance of the digital biopsy system was evaluated by the receiver operating characteristic (ROC) curve and calibration curve in both the training and validation cohort. RESULTS: 15 features were selected for the construction of radiological signature, which was significantly associated with expression levels of PD-L1 in both the training cohort (p<0.0001) and the external validation cohort (p<0.01). The hybrid deep learning model integrating the radiological signature and clinical factor could accurately distinguish GCs with high PD-L1 expression levels in both the training cohort (AUC = 0.806, 95%CI: 0.736-0.875) and the validation cohort (AUC = 0.784, 95%CI: 0.668-0.901). CONCLUSIONS: Our results indicate that the combination of deep learning and quantitative radiological features are potential approaches for the non-invasive evaluation of PD-L1 expression levels in GC. The digital biopsy system could provide valuable suggestive information for clinical decision-making of immunotherapy in GC.