showing 6 related works from this author
Research on Application of Data Mining Methods to Diagnosing Gastric Cancer
2012
Constantly evolving technologies bring new possibilities for supporting decision making in different areas - finance, marketing, production, social area, healthcare and others. Decision support systems are widely used in medicine in developed countries and show positive results. This research reveals several possibilities of application of data mining methods to diagnosing gastric cancer, which is the fourth leading cancer type in incidence after the breast, lung and colorectal cancers. A simple decision support system model was introduced and tested using gastric cancer inquiry form statistical data. The obtained results reveal both the benefits and potential of application of DSS aimed to…
Multicentric randomised study ofHelicobacter pylorieradication and pepsinogen testing for prevention of gastric cancer mortality: the GISTAR study
2017
IntroductionPopulation-based eradication ofHelicobacter pylorihas been suggested to be cost-effective and is recommended by international guidelines. However, the potential adverse effects of widespread antibiotic use that this would entail have not been sufficiently studied. An alternative way to decrease gastric cancer mortality is by non-invasive search for precancerous lesions, in particular gastric atrophy; pepsinogen tests are the best currently available alternative. The primary objective of GISTAR is to determine whetherH pylorieradication combined with pepsinogen testing reduces mortality from gastric cancer among 40–64-year-old individuals. The secondary objectives include evaluat…
Clinicopathological characteristics of Epstein-Barr virus-positive gastric cancer in Latvia
2019
Objective Epstein-Barr virus (EBV)-associated gastric cancer has been proposed to be a distinct gastric cancer molecular subtype. The prognostic significance of EBV infection in gastric cancer remains unclear and needs further investigation. Our study aimed to analyze EBV-positive and EBV-negative gastric cancer patients regarding their personal and tumor-related characteristics, and compare their overall survival. Methods Gastric cancer patients consecutively treated at the Riga East University Hospital during 2009-2016 were identified retrospectively. Tumor EBV status was determined by in-situ hybridization for EBV-encoded RNA (EBER). Information about clinicopathological characteristics …
Detection of gastric atrophy by circulating pepsinogens: A comparison of three assays.
2017
Background Circulating levels of pepsinogens have been used in high gastric cancer-risk Asian and European populations to triage endoscopic evaluation for more severe pathology. There are different analytic methods with uncertain correlations. We therefore compared diagnostic performance of three commonly used pepsinogen assays to detect histologically confirmed gastric atrophy. Methods We tested plasma samples from adult patients with (n=50) and without (n=755) moderate or severe gastric corpus atrophy, as determined histologically by consensus of three expert pathologists. A single laboratory measured pepsinogens I (PgI) and II (PgII) using commercially available assays: two ELISA assays …
Breath testing as potential colorectal cancer screening tool
2015
Although colorectal cancer (CRC) screening is included in organized programs of many countries worldwide, there is still a place for better screening tools. In this study, 418 breath samples were collected from 65 patients with CRC, 22 with advanced or nonadvanced adenomas, and 122 control cases. All patients, including the controls, had undergone colonoscopy. The samples were analysed with two different techniques. The first technique relied on gas chromatography coupled with mass spectrometry (GC-MS) for identification and quantification of volatile organic compounds (VOCs). The T-test was used to identify significant VOCs (p values < 0.017). The second technique relied on sensor analysis…
Entropy-Based Classifier Enhancement to Handle Imbalanced Class Problem
2017
The paper presents a possible enhancement of entropy-based classifiers to handle problems, caused by the class imbalance in the original dataset. The proposed method was tested on synthetic data in order to analyse its robustness in the controlled environment with different class proportions. As also the proposed method was tested on the real medical data with imbalanced classes and compared to the original classification algorithm results. The medical field was chosen for testing due to frequent situations with uneven class ratios.