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.

Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.

Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training require extensive practice to improve their healthcare skills. When a trainee encounters any difficulty in the practice, they will need feedback from experts to improve their performance. Personal feedback is time-consuming and subjected to bias. In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We examined changes in eye movement behavior during the moments of navigation loss (MNL), a signature sign for task difficulty during colonoscopy, and tested whether deep learning algorithms can detect the MNL by feeding data from eye-tracking. Human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. Outputs from deep learning were compared to the expert's judgment on the MNLs based on colonoscopic videos. The best classification outcome was achieved when we fed human eye data with 1000 synthesized eye data, where accuracy (91.80%), sensitivity (90.91%), and specificity (94.12%) were optimized. This study built an important foundation for our work of developing an education system for training healthcare skills using simulation.

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.

Preoperative Body Mass Index, Blood Albumin and Triglycerides Predict Survival for Patients with Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is common and its prognosis is often poor due to difficulties in early diagnosis and optimal treatment strategies. TNM staging system is useful in predicting prognosis but only possible after surgery. Therefore, it is desirable to investigate prognostic factors/markers that may predict prognosis before surgery by which helps appropriate management decisions preoperatively. METHODS: A total of 320 GC patients were consecutively recruited from 2004 to 2013 and followed up for 127 months (10.6 years) after surgery. These patients' were examined for body mass index (BMI) and blood levels of albumin, triglyceride, total cholesterol, low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C). Kaplan-Meier method and log rank test were used to analyze long-term survival using the above potential risk markers. We first employed medians of these variables to reveal maximal potentials of the above prognostic predictors. RESULTS: Three major findings were obtained: (1) Preoperative BMI was positively correlated with albumin (r = 0.144, P<0.05) and triglyceride (r = 0.365, P<0.01), but negatively correlated with TNM staging (r = -0.265, P<0.05). Preoperative albumin levels were positively correlated with triglyceride (r = 0.173, P<0.05) but again, negatively correlated with TNM staging (r = -0.137, P<0.05); (2) Poor survival was observed in GC patients with lower levels of BMI (P = 0.028), albumin (P = 0.004), and triglyceride (P = 0.043), respectively. Receiver operating characteristic (ROC) curve analyses suggested BMI, albumin and triglyceride to have survival-predictor powers similar to TNM system; and (3) Cox multi-factorial analyses demonstrated that age (P = 0.049), BMI (P = 0.016), cell differentiation (P = 0.001), and TNM staging (P = 0.011) were independent overall survival-predictors for GC patients. CONCLUSIONS: Preoperative BMI, albumin, and triglyceride levels are capable of predicting survival for GC patients superior to postoperative TNM system in terms of timing for management. As potential survival-predictors, preoperative tests of BMI, albumin and triglyceride, combined with clinical imaging, may help personalized management for GC patients including planning surgical strategy, optimal radio-chemotherapy and appropriate follow-up intervals after surgery.

Potential clinical impact of three-dimensional visualization for fluorescent in situ hybridization image analysis.

Chromosomal translocation is strong indication of cancers. Fluorescent in situ hybridization (FISH) can effectively detect this translocation and achieve high accuracy in disease diagnosis and prognosis assessment. For this purpose, whole chromosome paint probes are utilized to image the configuration of DNA fragments. Although two-dimensional (2-D) microscopic images are typically used in FISH signal analysis, we present a case where the translocation occurs in the depth direction where two probed FISH signals are overlapped in the projected image plane. Thus, the translocation cannot be identified. However, when imaging the whole specimen with a confocal microscope at 27 focal planes with 0.5-µm step interval, the translocation can be clearly identified due to the free rotation capability by the three-dimensional (3-D) visualization. Such a translocation detection error of using 2-D images might be critical in detecting and diagnosing early or subtle disease cases where detecting a small number of abnormal cells can make diagnostic difference. Hence, the underlying implication of this report suggests that utilizing 3-D visualization may improve the overall accuracy of FISH analysis for some clinical cases. However, the clinical efficiency and cost of using 3-D versus 2-D imaging methods are also to be assessed carefully.