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Serum Laboratory Studies, Stool Test, Breath Test01:30

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Gastrointestinal (GI) diagnostic studies are pivotal in confirming, ruling out, diagnosing, or staging various diseases, including cancers. Following diagnosis, allocating time for discussions with the patient and providing informational resources is crucial. Diagnostic assessments of the GI tract often occur in outpatient settings like endoscopy suites or GI labs. Preparation for these tests may include dietary restrictions, fasting, liquid bowel preparations, laxatives, enemas, and the...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data.

Hui Li1, Jianmei Lin1, Yanhong Xiao1

  • 1373651Department of Clinical Laboratory, The Sixth Affiliated Hospital, 26469Sun Yat-sen University, Guangzhou, Guangdong, China.

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|November 22, 2021
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Summary

A new logistic regression model using carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL) shows promise for noninvasive colorectal cancer (CRC) screening.

Keywords:
clinical laboratory techniquescolorectal cancerdiagnosislogistic regressionmachine learning

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Area of Science:

  • Oncology
  • Biomarkers
  • Machine Learning

Background:

  • Current colorectal cancer (CRC) diagnostics like colonoscopy are invasive and complex.
  • Minimally invasive, affordable, and accurate screening methods for CRC are needed.

Purpose of the Study:

  • To develop and evaluate machine learning models for CRC identification using minimally invasive variables.
  • To establish a cost-effective and accurate diagnostic approach for colorectal cancer.

Main Methods:

  • Retrospective study of 1164 electronic medical records (582 CRC patients, 582 controls).
  • Analysis of laboratory data including tumor biomarkers, lipid profiles, and complete blood counts.
  • Application and performance evaluation of five machine learning models (logistic regression, random forest, k-NN, SVM, Naïve Bayes).

Main Results:

  • Logistic regression model demonstrated highest performance (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%).
  • Key predictors identified: carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL).
  • A diagnostic model using these four indicators achieved an AUC of 0.849 for all CRC patients and 0.905 for late-stage CRC.

Conclusions:

  • The logistic regression model utilizing CEA, HGB, Lp(a), and HDL offers a potentially powerful, noninvasive, and cost-effective method for CRC identification.
  • This model could serve as a valuable tool for early detection and screening of colorectal cancer.