Predicting prognosis for patients with ESCC before surgery by SVMs ranking with nomogram analyses.

OBJECTIVE: A SVM predictive model consisting of preoperative tumor markers and inflammatory factors was established to explore its significance in evaluating the prognosis of patients with ESCC. METHODS: Clinical data of 311 patients with ESCC who underwent surgery were collected and followed up until October 2019. Statistical software SPSS version 22.0, and R (version 3.6.1) were used to analyze the data. RESULTS: In the Test, Val1 and Val2 groups, the sensitivity of preoperative optimal combination (SVM5) to predict the prognosis of patients with ESCC was 88.89%, 76.92%, and 73.68%, respectively. The specificity was 92.00%, 74.42%, and 78.00%, respectively. The sensitivity and specificity were not statistically different from those of SVM9 (P > 0.05), while the sensitivity of SVM9+5 for predicting the prognosis of patients with ESCC was 91.84%, 82.26%, and 80.36%, respectively. The specificity was 97.44%, 75.93%, and 78.00%, respectively. Its sensitivity and specificity were higher than those of SVM9 (P < 0.001). CONCLUSIONS: We used a nomogram to input the indicators in the SVM5 into the artificial intelligence program for patients with ESCC who have not yet developed an individualized plan. It can predict and evaluate the postoperative outcome of patients with ESCC with a sensitivity of 79.04%, specificity of 81.82%, PPV of 83.54%, NPV of 76.97%, and accuracy of 80.32%. For patients who have undergone surgery, we can enter the indicators in SVM9+5 into the artificial intelligence program.

Age-related twin-peak prevalence profiles of H. pylori infection, gastritis, GIN and gastric cancer: Analyses of 70,534 patients with gastroscopic biopsies.

OBJECTIVES: H. pylori (Hp) infection has been indicated in the pathogenesis of gastric diseases including gastric cancer (GC). This study aimed at exploring the relationships between Hp infection and gastric diseases including GC in a large dataset of routine patients undergoing gastroscopy. METHODS: From November 2007 to December 2017, 70,534 first-time visiting patients aged 18-94 years with gastroscopic biopsies were histologically diagnosed and analyzed. Patients' data were entered twice in an Excel spreadsheet database and analyzed using the SPSS (version 22.0) software package and statistical significance was defined as P<0.05 for all analyses. RESULTS: The first interesting observation was age-related twin-peak prevalence profiles (TPPs) for Hp infection, gastritis, and advanced diseases with different time spans (TS) between the first and second occurring peaks. Hp infection and gastritis had TPPs occurring at earlier ages than TPPs of gastric introepithelial neoplasia (GIN) and GC. More patients were clustered at the second occurring TPPs. The time spans (TS) from the first occurring peak of Hp infection to the first occurring peaks of other gastric diseases varied dramatically with 0-5 years for gastritis; 5-15 years for GINs, and 5-20 years for GC, respectively. The number of males with Hp infection and gastric diseases, excluding non-atrophic gastritis (NAG), was more than that of females (P<0.001). CONCLUSIONS: We have first observed age-related twin-peak prevalence profiles for Hp infection, gastritis, GIN, and GC, respectively, among a large population of patients undergoing gastroscopy. The second prevalence peak of GC is at ages of 70-74 years indicating that many GC patients would be missed during screening because the cut-off age for screening is 69 years old in China.

Active Stat3 and Her-2 as combined survival predictors show superiority to TNM staging system for postoperative patients with gastric cancer.

OBJECTIVES: TNM staging of gastric cancer (GC) is useful in predicting prognosis, but its definition is only possible after surgery. It is therefore desirable to develop a method that can predict prognosis and assist management options before surgery. METHODS: This study investigated 110 GC patients after radical gastrectomy and followed-up for 136 months. Patients' complete clinicopathological data were collected and gastroscopically biopsied or surgically resected tissues were examined for the expression of Her-2, nm-23, CEA and phosphorylated Stat3 (p-Stat3) using immunohistochemistry (IHC). Univariate and multivariate ROC curves, Kaplan-Meier survival curves, and SPSS Version 22.0 and R (version 3.6.1) statistical software were used to analyze the data. RESULTS: Three major findings were observed: (1) Tissue levels of p-Stat3, Her-2, CEA and nm-23 were correlated with GC patients' survival probability termed as survival prediction power (SPP). (2) Using 5-year survival as an end-point, the SPP of the p-Stat3+Her-2 combination was stronger (AUC=0.867) than that of TNM staging (AUC=0.755). (3) Using cut-off values derived from ROC curves, Kaplan-Meier analyses showed that the p-Stat3+Her-2 molecular combination could clearly predict overall survival rates between the predictive low-risk patients (69.2%) and the predictive high-risk patients (13.2%) with a discriminative difference as high as 56.0%. CONCLUSIONS: We conclude that area under the ROC curve (AUC) can be used to quantify SPP powers for biomarkers, making cross-comparisons possible among different survival predictors. This study has first established a multi-factor survival prediction model by which the p-Stat3+Her-2 combination has the best discriminative capability to differentiate low-risk patients from high-risk patients in terms of survival prognosis.

Combination of preoperative CA19-9 levels, cell differentiation, and age predicts survival for patients with gastric cancer before surgery.

ABSTRACT: Gastric cancer (GC) is very common in China, posing a threat to public health, with high morbidity and mortality ranks. Tumor-node-metastasis (TNM) staging system is routinely used to predict prognosis for patients with GC but only available after surgery. Therefore, searching for markers that can predict prognosis of GC patients before surgery is desirable to assist management decisions preoperatively. Among 322 GC patients followed-up for 128 months, the tumor markers alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 15-3 and carbohydrate antigen 72-4 of 168 patients were detected before surgery, and their impact on survival was analyzed. Four major findings were revealed: (1) Preoperative examined CA19-9 levels and cell differentiation using endoscopic biopsies were positively correlated with lymphatic metastases and TNM stages obtained after surgery. (2) Kaplan-Meier analyses demonstrated that poor survival of patients with GC was associated with higher CA19-9 levels, poor cell differentiation, and older age. (3) Cox multi-factorial regression analyses indicated that, in terms of predicting overall survival for GC patients, preoperative CA19-9 level, cell differentiation and age were independent factors, respectively, comparable to postoperative TNM staging system. (4) Using receiver operating characteristic curve analysis, we first revealed that preoperative CA19-9 levels and cell differentiation had the impact weights (IW) on survival comparable to postoperative TNM components. These findings suggest that preoperative CA19-9 levels, cell differentiation and age are useful prognostic related markers for GC patients, superior to postoperative TNM system in terms of timing for management. We propose that, assisted by clinical imaging, a comprehensive utilization of these preoperative survival-predictors may help formulate individualized medical management for GC patients such as surgical strategy, optimal chemotherapy and radiotherapy, and appropriate follow-up intervals after surgery.

Preoperative Combined Prediction Models Have Superior Capability in Predicting Survival as the Child-Pugh Grade in Patients with HCC after Interventional Embolotherapy.

BACKGROUND: It is of important clinical significance for hepatocellular carcinoma (HCC) patients to evaluate prognosis before interventional embolotherapy. METHODS: A total of 106 patients with HCC after interventional embolotherapy who had complete data with follow-up information until September 2019 were included in this study. These data were analyzed using SPSS Version 22.0 and R (version 3.6.1) statistical software. RESULTS: 1) The diameter of the tumor, ascites, FIT, AFP, ALT, AST, GGT, and Child-Pugh score had the ability to predict the prognosis and survival of patients with HCC. Among these molecules, the predictive effectiveness (or the area under the receiver operating characteristic [ROC] curve) of GGT was the highest, although it was slightly lower than the predictive effectiveness of the Child-Pugh score, which is the gold standard for survival analysis. 2) Among survival analyses combining five molecular indicators, the predictive postoperative viability for combination 1 was the strongest with an area under the ROC curve (AUC) of 0.856 (0.779, 0.932), similar to the all-molecular combination (combination 16) with an AUC of 0.872 (0.798, 0.945), but much higher than that of the Child-Pugh score of 0.720 (0.616, 0.823) for HCC patients (all p<0.05). 3) Kaplan-Meier analyses showed that the 3-year cumulative survival rates were 55.3% for low-risk patients and 2.6% for high-risk patients. CONCLUSION: A combined prediction model can determine the optimal combination of preoperative routine detection indices in patients with HCC intervention, and ROC curve analysis can quantify the efficacy of these indices in the survival and prognosis of HCC. Interestingly, combination 1 showed stronger predictive capability than the Child-Pugh score in predicting death risks for postoperative patients with HCC. When combination 1 has several missing clinical data, these combination prediction models (12, 3, 7, 13, 16) are also a replaceable choice. These findings may have important clinical significance in the formulation of individualized medical programs.

A Nomogram Based on Preoperative Clinical Bio-Indicators to Predict 5-year Survivals for Patients with Gastric Cancer After Radical Gastrectomy.

PURPOSE: This study aimed to improve the prediction of postoperative survival outcomes for patients with gastric cancer (GC) using a nomogram based on preoperative bio-indicators. PATIENTS AND METHODS: This retrospective study included 303 GC patients who had undergone radical gastrectomy from 2004 to 2013 at the First Affiliated Hospital, Shihezi University. The patients were followed up for 175 months after surgery and then divided into short-term (n=201) or long-term (n=102) survival groups. We used an expectation-maximization method to fill any missing data from the reviewed patient files. We then employed the Cox proportional hazard regression to identify biochemical markers that could predict 5-year overall survival (OS) as an endpoint among GC patients. Based on the results from the biochemical analysis, we developed a nomogram and assessed its performance and reliability. RESULTS: The variables significantly associated with OS in a multivariate analysis were age, body mass index (BMI), cell differentiation, high-density lipoprotein cholesterol (HDL-C), as well as serum potassium or serum magnesium. Combining all these predictors allowed us to establish a nomogram (C-index=0.701) whose accuracy of predicting survival was higher than the TNM staging system established by the 8th American Joint Committee on Cancer (C-index=0.666; p=0.016). Furthermore, decision curve of this nomogram was shown to have an ideal net clinical benefit rate. CONCLUSION: We have developed an algorithm using preoperative bio-indicators and clinical features to predict prognosis for GC patients. This tool may help clinicians to strategize appropriate treatment options for GC patients prior to surgery.

Light-Induced Caspase-3-Responsive Chimeric Peptide for Effective PDT/Chemo Combination Therapy with Good Compatibility.

Activated doxorubicin (DOX) often has severe systemic toxicity and side effects due to its inability to distinguish tumor cells from normal cells, which seriously affects the prognosis of patients. Here, we synthesized an inactivated a DOX prodrug that could be selectively activated by a light-induced caspase-3 enzyme in the tumor site. In the absence of light, this uniformly dispersed nanoparticle avoided the unnecessary toxicity under physiological conditions. Upon the laser irradiating to the tumor area of interest, the nanoparticles can produce a large amount of reactive oxygen species (ROS) to induce cell apoptosis and activate caspase-3 enzyme to release DOX selectively. Meanwhile, the produced ROS can also combine with activated DOX to cause more potent tumor damage. The experiments demonstrated that the light can effectively activate DOX drug through a series of cascade events and the subsequent synergistic therapy both in vitro and in vivo. This strategy achieved excellent therapeutic outcomes and minimal adverse effects, which should significantly improve the dilemma of traditional chemotherapy.