Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico [Clin Transl Oncol] 2025 Jan; Vol. 27 (1), pp. 299-308. Date of Electronic Publication: 2024 Jun 20.

Competing Interests: Declarations. Conflict of interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Ethical approval: The study was approved by the Ethics Committee of the First Hospital of Dalian Medical University. Informed consent: For this type of study, formal consent is not required.
Background: Colorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently needed.
Purpose: This study assesses the diagnostic effectiveness of Carbohydrate Antigen 19-9 (CA19-9), Carcinoembryonic Antigen (CEA), Alpha-fetoprotein (AFP), and Cancer Antigen 125 (CA125) serum tumor markers for colorectal cancer (CRC) and investigates a machine learning-based diagnostic model incorporating these markers with blood biochemical indices for improved CRC detection.
Method: Between January 2019 and December 2021, data from 800 CRC patients and 697 controls were collected; 52 patients and 63 controls attending the same hospital in 2022 were collected as an external validation set. Markers' effectiveness was analyzed individually and collectively, using metrics like ROC curve AUC and F1 score. Variables chosen through backward regression, including demographics and blood tests, were tested on six machine learning models using these metrics.
Result: In the case group, the levels of CEA, CA199, and CA125 were found to be higher than those in the control group. Combining these with a fourth serum marker significantly improved predictive efficacy over using any single marker alone, achieving an Area Under the Curve (AUC) value of 0.801. Using stepwise regression (backward), 17 variables were meticulously selected for evaluation in six machine learning models. Among these models, the Gradient Boosting Machine (GBM) emerged as the top performer in the training set, test set, and external validation set, boasting an AUC value of over 0.9, indicating its superior predictive power.
Conclusion: Machine learning models integrating tumor markers and blood indices offer superior CRC diagnostic accuracy, potentially enhancing clinical practice.
(© 2024. The Author(s), under exclusive licence to Federación de Sociedades Españolas de Oncología (FESEO).)

English

Publisher: Springer Italia Country of Publication: Italy NLM ID: 101247119 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1699-3055 (Electronic) Linking ISSN: 1699048X NLM ISO Abbreviation: Clin Transl Oncol Subsets: MEDLINE

Colorectal Neoplasms*/blood , Colorectal Neoplasms*/diagnosis , Biomarkers, Tumor*/blood , Machine Learning Algorithms*, Humans ; Male ; Female ; Antigens, Tumor-Associated, Carbohydrate ; Carcinoembryonic Antigen/blood ; alpha-Fetoproteins/analysis ; CA-125 Antigen/blood ; Membrane Proteins/blood ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Aged ; Aged, 80 and over

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