GCdiscrimination: identification of gastric cancer based on a milliliter of blood

Abstract Gastric cancer (GC) continues to be one of the major causes of cancer deaths worldwide. Meanwhile, liquid biopsies have received extensive attention in the screening and detection of cancer along with better understanding and clinical practice of biomarkers. In this work, 58 routine blood biochemical indices were tentatively used as integrated markers, which further expanded the scope of liquid biopsies and a discrimination system for GC consisting of 17 top-ranked indices, elaborated by random forest method was constructed to assist in preliminary assessment prior to histological and gastroscopic diagnosis based on the test data of a total of 2951 samples. The selected indices are composed of eight routine blood indices (MO%, IG#, IG%, EO%, P-LCR, RDW-SD, HCT and RDW-CV) and nine blood biochemical indices (TP, AMY, GLO, CK, CHO, CK-MB, TG, ALB and γ-GGT). The system presented a robust classification performance, which can quickly distinguish GC from other stomach diseases, different cancers and healthy people with sensitivity, specificity, total accuracy and area under the curve of 0.9067, 0.9216, 0.9138 and 0.9720 for the cross-validation set, respectively. Besides, this system can not only provide an innovative strategy to facilitate rapid and real-time GC identification, but also reveal the remote correlation between GC and these routine blood biochemical parameters, which helped to unravel the hidden association of these parameters with GC and serve as the basis for subsequent studies of the clinical value in prevention program and surveillance management for GC. The identification system, called GC discrimination, is now available online at http://lishuyan.lzu.edu.cn/GC/.

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Effect of dietary dextrin levels on growth,activities of digestive enzyme and blood biochemical indices of juvenile obscure puffer(Takifugu obscurus)

JOURNAL OF FISHERIES OF CHINA ◽

10.3724/sp.j.1231.2013.38589 ◽

2013 ◽

Vol 37 (9) ◽

pp. 1359 ◽

Cited By ~ 2

Author(s):

Xianghe LIU ◽

Chaoxia YE ◽

Limian ZHENG ◽

Chengcheng OU ◽

Anli WANG

Keyword(s):

Digestive Enzyme ◽

Takifugu Obscurus ◽

Blood Biochemical ◽

Biochemical Indices ◽

Blood Biochemical Indices ◽

Obscure Puffer

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Impact of Supplementing Feed Restricted Ewes' Diet with Propylene Glycol, Monensin Sodium and Rumen-Protected Choline Chloride During Late Pregnancy on Blood Biochemical Indices, Body Condition Score and Body Weight

Animal Feed Science and Technology ◽

10.1016/j.anifeedsci.2020.114801 ◽

2020 ◽

pp. 114801

Author(s):

Leila Ahmadzadeh-Gavahan ◽

Ali Hosseinkhani ◽

Akbar Taghizadeh ◽

Babak Ghasemi-Panahi ◽

Gholamreza Hamidian ◽

...

Keyword(s):

Body Weight ◽

Propylene Glycol ◽

Body Condition ◽

Body Condition Score ◽

Choline Chloride ◽

Late Pregnancy ◽

Blood Biochemical ◽

Condition Score ◽

Biochemical Indices ◽

Blood Biochemical Indices

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A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study

JMIR Medical Informatics ◽

10.2196/13476 ◽

2019 ◽

Vol 7 (3) ◽

pp. e13476 ◽

Cited By ~ 3

Author(s):

Jiangpeng Wu ◽

Xiangyi Zan ◽

Liping Gao ◽

Jianhong Zhao ◽

Jing Fan ◽

...

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Cross Validation ◽

Clinical Symptoms ◽

Machine Learning Method ◽

Learning Method ◽

Liquid Biopsies ◽

Blood Indices ◽

Routine Blood ◽

Identification Model

Background Liquid biopsies based on blood samples have been widely accepted as a diagnostic and monitoring tool for cancers, but extremely high sensitivity is frequently needed due to the very low levels of the specially selected DNA, RNA, or protein biomarkers that are released into blood. However, routine blood indices tests are frequently ordered by physicians, as they are easy to perform and are cost effective. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between multiple sets of test data and diseases. Objective The aim of this study is to discover the potential association between lung cancer and routine blood indices and thereby help clinicians and patients to identify lung cancer based on these routine tests. Methods The machine learning method known as Random Forest was adopted to build an identification model between routine blood indices and lung cancer that would determine if they were potentially linked. Ten-fold cross-validation and further tests were utilized to evaluate the reliability of the identification model. Results In total, 277 patients with 49 types of routine blood indices were included in this study, including 183 patients with lung cancer and 94 patients without lung cancer. Throughout the course of the study, there was correlation found between the combination of 19 types of routine blood indices and lung cancer. Lung cancer patients could be identified from other patients, especially those with tuberculosis (which usually has similar clinical symptoms to lung cancer), with a sensitivity, specificity and total accuracy of 96.3%, 94.97% and 95.7% for the cross-validation results, respectively. This identification method is called the routine blood indices model for lung cancer, and it promises to be of help as a tool for both clinicians and patients for the identification of lung cancer based on routine blood indices. Conclusions Lung cancer can be identified based on the combination of 19 types of routine blood indices, which implies that artificial intelligence can find the connections between a disease and the fundamental indices of blood, which could reduce the necessity of costly, elaborate blood test techniques for this purpose. It may also be possible that the combination of multiple indices obtained from routine blood tests may be connected to other diseases as well.

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