Rapid Detection of Liver Metastasis Risk in Colorectal Cancer Patients Through Blood Test Indicators

Overview

Journal Front Oncol

Specialty Oncology

Date 2024 Sep 26

PMID 39324006

Authors

Zhou Yu

Gang Li

Wanxiu Xu

Affiliations

Soon will be listed here.

Abstract

Introduction: Colorectal cancer (CRC) is one of the most common malignancies, with liver metastasis being its most common form of metastasis. The diagnosis of colorectal cancer liver metastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniques, but there is no simple and quick early diagnosisof CRCLM.

Methods: This study aims to develop a method for rapidly detecting the risk of liver metastasis in CRC patients through blood test indicators based on machine learning (ML) techniques, thereby improving treatment outcomes. To achieve this, blood test indicators from 246 CRC patients and 256 CRCLM patients were collected and analyzed, including routine blood tests, liver function tests, electrolyte tests, renal function tests, glucose determination, cardiac enzyme profiles, blood lipids, and tumor markers. Six commonly used ML models were used for CRC and CRCLM classification and optimized by using a feature selection strategy.

Results: The results showed that AdaBoost algorithm can achieve the highest accuracy of 89.3% among the six models, which improved to 91.1% after feature selection strategy, resulting with 20 key markers.

Conclusions: The results demonstrate that the combination of machine learning techniques with blood markers is feasible and effective for the rapid diagnosis of CRCLM, significantly im-proving diagnostic ac-curacy and patient prognosis.

References

Al Reshan M, Amin S, Zeb M, Sulaiman A, Alshahrani H, Azar A . Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques. Life (Basel). 2023; 13(10). PMC: 10608611. DOI: 10.3390/life13102093. View

Tan C, Chen H, Xia C . Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm. J Pharm Biomed Anal. 2009; 49(3):746-52. DOI: 10.1016/j.jpba.2008.12.010. View

Wang Y, Ma L, Yin X, Gao B . Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis. Front Oncol. 2022; 11:689509. PMC: 8776634. DOI: 10.3389/fonc.2021.689509. View

Huang G, Zhang Y, Chen L, Li Y, Huang T, Cai Y . Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data. Life (Basel). 2021; 11(9). PMC: 8467493. DOI: 10.3390/life11090940. View

Ge H, Gao X, Lin J, Zhao X, Wu X, Zhang H . Label-free SERS detection of prostate cancer based on multi-layer perceptron surrogate model method. Spectrochim Acta A Mol Biomol Spectrosc. 2023; 304:123407. DOI: 10.1016/j.saa.2023.123407. View

Zhou C, Peng D, Liao B, Jia R, Wu F . ACP_MS: prediction of anticancer peptides based on feature extraction. Brief Bioinform. 2022; 23(6). DOI: 10.1093/bib/bbac462. View

Zhang J, Zhu H, Wang J, Chen Y, Li Y, Chen X . Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis. Front Oncol. 2023; 13:1082423. PMC: 10072228. DOI: 10.3389/fonc.2023.1082423. View

Petersen M, Kleif J, Liggett J, Rasmussen M, Jorgensen L, Vilandt J . Development of an algorithm combining blood-based biomarkers, fecal immunochemical test, and age for population-based colorectal cancer screening. Gastrointest Endosc. 2024; 100(6):1061-1069.e3. DOI: 10.1016/j.gie.2024.06.015. View

Pei G, Sun K, Yang Y, Wang S, Li M, Ma X . Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data. Front Oncol. 2024; 14:1388575. PMC: 11100425. DOI: 10.3389/fonc.2024.1388575. View

10.

Zhou H, Liu Z, Wang Y, Wen X, Amador E, Yuan L . Colorectal liver metastasis: molecular mechanism and interventional therapy. Signal Transduct Target Ther. 2022; 7(1):70. PMC: 8897452. DOI: 10.1038/s41392-022-00922-2. View

11.

Kildusiene I, Dulskas A, Smailyte G . Value of combined serum CEA, CA72-4, and CA19-9 marker detection in diagnosis of colorectal cancer. Tech Coloproctol. 2024; 28(1):33. DOI: 10.1007/s10151-023-02873-4. View

12.

Lv Y, Zhang H . Effect of Non-alcoholic Fatty Liver Disease on the Risk of Synchronous Liver Metastasis: Analysis of 451 Consecutive Patients of Newly Diagnosed Colorectal Cancer. Front Oncol. 2020; 10:251. PMC: 7059642. DOI: 10.3389/fonc.2020.00251. View

13.

Liu D, Zhang X, Zheng T, Shi Q, Cui Y, Wang Y . Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images. Arch Gynecol Obstet. 2021; 303(3):811-820. PMC: 7960581. DOI: 10.1007/s00404-020-05908-5. View

14.

Lu H, Gao H, Ye M, Wang X . A Hybrid Ensemble Algorithm Combining AdaBoost and Genetic Algorithm for Cancer Classification with Gene Expression Data. IEEE/ACM Trans Comput Biol Bioinform. 2019; 18(3):863-870. DOI: 10.1109/TCBB.2019.2952102. View

15.

Guo G, Xie Y, Zhang P, Jiang T . Blood index panel for gastric cancer detection. World J Gastrointest Surg. 2022; 14(9):1026-1036. PMC: 9521474. DOI: 10.4240/wjgs.v14.i9.1026. View

16.

Delzell D, Magnuson S, Peter T, Smith M, Smith B . Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data. Front Oncol. 2020; 9:1393. PMC: 6917601. DOI: 10.3389/fonc.2019.01393. View

17.

Ros J, Balconi F, Baraibar I, Saoudi Gonzalez N, Salva F, Tabernero J . Advances in immune checkpoint inhibitor combination strategies for microsatellite stable colorectal cancer. Front Oncol. 2023; 13:1112276. PMC: 9932591. DOI: 10.3389/fonc.2023.1112276. View

18.

Ding Q, Sun Y, Zhang J, Yao Y, Huang D, Jiang Y . Utility and specificity of plasma heat shock protein 90 alpha, CEA, and CA199 as the diagnostic test in colorectal cancer liver metastasis. J Gastrointest Oncol. 2022; 13(5):2497-2504. PMC: 9660089. DOI: 10.21037/jgo-22-797. View

19.

Wang C, Chang C, Chu T, Liang Y . Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women. Life (Basel). 2023; 13(12). PMC: 10744461. DOI: 10.3390/life13122257. View

20.

Zhao Z, Tan X, Liu R . [Status and progress of simultaneous liver metastasis in colorectal cancer]. Zhonghua Wei Chang Wai Ke Za Zhi. 2020; 23(6):619-622. DOI: 10.3760/cma.j.cn.441530-20200331-00176. View