Skip to content
2000
Volume 25, Issue 12
  • ISSN: 1568-0096
  • E-ISSN: 1873-5576

Abstract

Background

Gastrointestinal (GI) cancers represent some of the most common and lethal malignancies globally, underscoring the urgent need for improved diagnostic strategies. Traditional diagnostic methods, while effective to some degree, are often invasive and unsuitable for regular screenings.

Objective

This review article explores integrating machine learning (ML) with liquid biopsy techniques as a revolutionary approach to enhance the detection and monitoring of GI cancers. Liquid biopsies offer a non-invasive alternative for cancer detection through the analysis of circulating tumor DNA (ctDNA) and other biomarkers, which when combined with ML, can significantly improve diagnostic accuracy and patient outcomes.

Methods

We conducted a comprehensive review of recent advancements in liquid biopsy and ML, focusing on their synergistic potential in the early detection of GI cancers. The review addresses the application of next-generation sequencing and digital droplet PCR in enhancing the sensitivity and specificity of liquid biopsies.

Results

Machine learning algorithms have demonstrated remarkable ability in navigating complex datasets and identifying diagnostically significant patterns in ctDNA and other circulating biomarkers. Innovations such as machine learning-enhanced “fragmentomics” and tomographic phase imaging flow cytometry illustrate significant strides in non-invasive cancer diagnostics, offering enhanced detection capabilities with high accuracy.

Conclusion

The integration of ML in liquid biopsy represents a transformative step in the early detection and personalized treatment of GI cancers. Future research should focus on overcoming current limitations, such as the heterogeneity of tumor-derived genetic materials and the standardization of liquid biopsy protocols, to fully realize the potential of this technology in clinical settings.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ccdt/10.2174/0115680096329098240920043326
2024-10-31
2026-02-28

  • From This Site

    /content/journals/ccdt/10.2174/0115680096329098240920043326
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. ArnoldM. AbnetC.C. NealeR.E. VignatJ. GiovannucciE.L. McGlynnK.A. BrayF. Global Burden of 5 Major Types of Gastrointestinal Cancer.Gastroenterology20201591335349.e1510.1053/j.gastro.2020.02.068 32247694
     [Google Scholar]
  2. LuL. MullinsC.S. SchafmayerC. ZeißigS. LinnebacherM. A global assessment of recent trends in gastrointestinal cancer and lifestyle‐associated risk factors.Cancer Commun. (Lond.)202141111137115110.1002/cac2.12220 34563100
     [Google Scholar]
  3. HossainM.S. KaruniawatiH. JairounA.A. UrbiZ. OoiD.J. JohnA. LimY.C. KibriaK.M.K. MohiuddinA.K.M. MingL.C. GohK.W. HadiM.A. Colorectal Cancer: A Review of Carcinogenesis, Global Epidemiology, Current Challenges, Risk Factors, Preventive and Treatment Strategies.Cancers (Basel)2022147173210.3390/cancers14071732 35406504
     [Google Scholar]
  4. PulumatiA. PulumatiA. DwarakanathB.S. VermaA. PapineniR.V.L. Technological advancements in cancer diagnostics: Improvements and limitations.Cancer Rep.202362e176410.1002/cnr2.1764 36607830
     [Google Scholar]
  5. AdhitK.K. WanjariA. MenonS. Liquid BiopsyK.S. Liquid biopsy: An evolving paradigm for non-invasive disease diagnosis and monitoring in medicine.Cureus20231512e5017610.7759/cureus.50176
     [Google Scholar]
  6. BaiY. ZhaoH. Liquid biopsy in tumors: opportunities and challenges.Ann. Transl. Med.20186S1S8910.21037/atm.2018.11.31 30613664
     [Google Scholar]
  7. SooriM. ArezooB. DastresR. Artificial intelligence, machine learning and deep learning in advanced robotics, a review.Cognitive Robotics202310.1016/j.cogr.2023.04.001.
     [Google Scholar]
  8. AdashekJ.J. JankuF. KurzrockR. Signed in blood: Circulating tumor DNA in cancer diagnosis, treatment and screening.Cancers (Basel)20211314360010.3390/cancers13143600
     [Google Scholar]
  9. KrishnanS.K. DasP. SudarshanK.L. KotianC.M. SanthappanS. VishwakarmaM.B. MathurP. Descriptive epidemiology of gastrointestinal cancers: Results from national cancer registry programme, India.Asian Pac. J. Cancer Prev.202223240941810.31557/APJCP.2022.23.2.409
     [Google Scholar]
  10. NadeemS. DineshK. TasneefZ. BhavnaS. RahulS. KiranB. Dietary risk factors in gastrointestinal cancers: A case–control study in North India.J. Cancer Res. Ther.20231951385139110.4103/jcrt.jcrt_1830_21 37787313
     [Google Scholar]
  11. Mysuru ShivannaL. UroojA. A Review on Dietary and Non-Dietary Risk Factors Associated with Gastrointestinal Cancer.J. Gastrointest. Cancer201647324725410.1007/s12029‑016‑9845‑1 27270712
     [Google Scholar]
  12. MathurP. SathishkumarK. ChaturvediM. DasP. StephenS. Cancer incidence estimates for 2022 & projection for 2025: Result from National Cancer Registry Programme, India.Indian J. Med. Res.2022156459860710.4103/ijmr.ijmr_1821_22 36510887
     [Google Scholar]
  13. ShaibW. El-SeragH.B. The epidemiology of pancreatic cancer in the United States: Changes below the surface.Aliment. Pharmacol. Ther.200726218319110.1111/j.1365‑2036.2007.03364.x 17593064
     [Google Scholar]
  14. American Cancer Society. Cancer facts & figures 2020.2020Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2020.html
  15. BrayF. FerlayJ. SoerjomataramI. SiegelR.L. TorreL.A. JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.201868639442410.3322/caac.21492 30207593
     [Google Scholar]
  16. SiegelR.L. MillerK.D. JemalA. Cancer statistics, 2019.CA Cancer J. Clin.201969173410.3322/caac.21551 30620402
     [Google Scholar]
  17. de MartelC. FerlayJ. FranceschiS. VignatJ. BrayF. FormanD. PlummerM. Global burden of cancers attributable to infections in 2008: a review and synthetic analysis.Lancet Oncol.201213660761510.1016/S1470‑2045(12)70137‑7 22575588
     [Google Scholar]
  18. RawlaP. SunkaraT. BarsoukA. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors.Prz. Gastroenterol.20191428910310.5114/pg.2018.81072 31616522
     [Google Scholar]
  19. ArnoldM. SierraM.S. LaversanneM. SoerjomataramI. JemalA. BrayF. Global patterns and trends in colorectal cancer incidence and mortality.Gut201766468369110.1136/gutjnl‑2015‑310912 26818619
     [Google Scholar]
  20. TorreL.A. BrayF. SiegelR.L. FerlayJ. Lortet-TieulentJ. JemalA. Global cancer statistics, 2012.CA Cancer J. Clin.20156528710810.3322/caac.21262 25651787
     [Google Scholar]
  21. BertuccioP. ChatenoudL. LeviF. PraudD. FerlayJ. NegriE. MalvezziM. La VecchiaC. Recent patterns in gastric cancer: A global overview.Int. J. Cancer2009125366667310.1002/ijc.24290 19382179
     [Google Scholar]
  22. PetrickJ.L. BraunlinM. LaversanneM. ValeryP.C. BrayF. McGlynnK.A. International trends in liver cancer incidence, overall and by histologic subtype, 1978–2007.Int. J. Cancer201613971534154510.1002/ijc.30211 27244487
     [Google Scholar]
  23. HirahataT. Ul QuraishR. QuraishA.U. Ul QuraishS. NazM. RazzaqM.A. Liquid Biopsy: A distinctive approach to the diagnosis and prognosis of cancer.Cancer Inform.20222110.1177/11769351221076062
     [Google Scholar]
  24. LinB. JiangJ. ZhouX. Recent advances in design strategies of aptamer‐based liquid biopsy.J. Polym. Sci.202462132848287010.1002/pol.20230445
     [Google Scholar]
  25. SatamH. JoshiK. MangroliaU. WaghooS. ZaidiG. RawoolS. ThakareR.P. BandayS. MishraA.K. DasG. Next-generation sequencing technology: Current trends and advancements.Biology (Basel)202312799710.3390/biology12070997
     [Google Scholar]
  26. SiravegnaG. MarsoniS. SienaS. BardelliA. Integrating liquid biopsies into the management of cancer.Nat. Rev. Clin. Oncol.201714953154810.1038/nrclinonc.2017.14 28252003
     [Google Scholar]
  27. NikanjamM. KatoS. KurzrockR. Liquid biopsy: Current technology and clinical applications.J. Hematol. Oncol.202215113110.1186/s13045‑022‑01351‑y
     [Google Scholar]
  28. Alix-PanabièresC. MarchettiD. LangJ.E. Liquid biopsy: from concept to clinical application.Sci. Rep.20231312168510.1038/s41598‑023‑48501‑x 38066040
     [Google Scholar]
  29. CrosbyD. Delivering on the promise of early detection with liquid biopsies.Br. J. Cancer2022126331331510.1038/s41416‑021‑01646‑w 35013576
     [Google Scholar]
  30. Sanz-GarciaE. ZhaoE. BratmanS.V. SiuL.L. Monitoring and adapting cancer treatment using circulating tumor DNA kinetics: Current research, opportunities, and challenges.Sci. Adv.202284eabi861810.1126/sciadv.abi8618 35080978
     [Google Scholar]
  31. FebboP.G. AlloM. AlmeE.B. Cuyun CarterG. DumanoisR. EssigA. KiernanE. KublerC.B. MartinN. PopescuM.C. Recommendations for the equitable and widespread implementation of liquid biopsy for cancer care.JCO Precis. Oncol.20248e230038210.1200/PO.23.00382
     [Google Scholar]
  32. CasolinoR. BeerP.A. ChakravartyD. DavisM.B. MalapelleU. MazzarellaL. NormannoN. PauliC. SubbiahV. TurnbullC. WestphalenC.B. BiankinA.V. Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.CA Cancer J. Clin.202474326428510.3322/caac.21825 38174605
     [Google Scholar]
  33. MarusykA. PolyakK. Tumor heterogeneity: Causes and consequences.Biochim. Biophys. Acta Rev. Cancer20101805110511710.1016/j.bbcan.2009.11.002 19931353
     [Google Scholar]
  34. KavanS. KruseT.A. VogsenM. HildebrandtM.G. ThomassenM. Heterogeneity and tumor evolution reflected in liquid biopsy in metastatic breast cancer patients: a review.Cancer Metastasis Rev.202241243344610.1007/s10555‑022‑10023‑9 35286542
     [Google Scholar]
  35. WhiteT. AlgeriS. Estimating the lifetime risk of a false positive screening test result.PLoS One2023182e028115310.1371/journal.pone.0281153
     [Google Scholar]
  36. ConnorsD. AllenJ. AlvarezJ.D. BoyleJ. CristofanilliM. HillerC. KeatingS. KelloffG. LeimanL. McCormackR. MerinoD. MorganE. PantelK. RolfoC. SerranoM.J. Pia SanzoneA. SchlangeT. SigmanC. StewartM. International liquid biopsy standardization alliance white paper.Crit. Rev. Oncol. Hematol.202015610311210.1016/j.critrevonc.2020.103112 33035734
     [Google Scholar]
  37. IgnatiadisM. SledgeG.W. JeffreyS.S. Liquid biopsy enters the clinic — implementation issues and future challenges.Nat. Rev. Clin. Oncol.202118529731210.1038/s41571‑020‑00457‑x
     [Google Scholar]
  38. ClaytonE.W. EvansB.J. HazelJ.W. RothsteinM.A. The law of genetic privacy: Applications, implications, and limitations.J. Law Biosci.20196113610.1093/jlb/lsz007
     [Google Scholar]
  39. EledkawyA. HamzaT. El-MetwallyS. Precision cancer classification using liquid biopsy and advanced machine learning techniques.Sci. Rep.2024141584110.1038/s41598‑024‑56419‑1 38462648
     [Google Scholar]
  40. LiuL. ChenX. PetinrinO.O. ZhangW. RahamanS. TangZ.R. WongK.C. Machine learning protocols in early cancer detection based on liquid biopsy: A survey.Life (Basel)202111763810.3390/life11070638
     [Google Scholar]
  41. KoJ. BaldassanoS.N. LohP.L. KordingK. LittB. IssadoreD. Machine learning to detect signatures of disease in liquid biopsies – a user’s guide.Lab Chip201818339540510.1039/C7LC00955K 29192299
     [Google Scholar]
  42. DasS. DeyM.K. DevireddyR. GartiaM.R. Biomarkers in cancer detection, diagnosis, and prognosis.Sensors (Basel)20232413710.3390/s24010037
     [Google Scholar]
  43. KhandezaminZ. NaderanM. RashtiM.J. Detection and classification of breast cancer using logistic regression feature selection and GMDH classifier.J. Biomed. Inform.202011110359110.1016/j.jbi.2020.103591 33039588
     [Google Scholar]
  44. WangY. AdamM.L. ZhaoY. ZhengW. GaoL. YinZ. ZhaoH. Machine Learning-Enhanced Flexible Mechanical Sensing.Nano-Micro Lett.20231515510.1007/s40820‑023‑01013‑9 36800133
     [Google Scholar]
  45. MennaG. Piaser GuerratoG. BilginL. CeccarelliG.M. OliviA. Della PepaG.M. Is there a role for machine learning in liquid biopsy for brain tumors? A systematic review.Int J Mol Sci20232411972310.3390/ijms24119723
     [Google Scholar]
  46. WanN. WeinbergD. LiuT.Y. NiehausK. AriaziE.A. DelubacD. KannanA. WhiteB. BaileyM. BertinM. Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.BMC Cancer201919183210.1186/s12885‑019‑6003‑8
     [Google Scholar]
  47. PironeD. MontellaA. SiricoD.G. MugnanoM. VilloneM.M. BiancoV. MiccioL. PorcelliA.M. KurelacI. CapassoM. IolasconA. MaffettoneP.L. MemmoloP. FerraroP. Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry.Sci. Rep.2023131604210.1038/s41598‑023‑32110‑9 37055398
     [Google Scholar]
  48. HsuY.C. HuangS.M. ChangL.C. ChenY.M. ChangY.H. LinJ.W. LinC.C. ChenC.W. ChenH.Y. ChiuH.M. YuS.L. Screening of early-staged colorectal neoplasia by clonal hematopoiesis-based liquid biopsy and machine-learning.Am. J. Cancer Res.202212310881101 35411222
     [Google Scholar]
  49. BieF. WangZ. LiY. GuoW. HongY. HanT. LvF. YangS. LiS. LiX. NieP. XuS. ZangR. ZhangM. SongP. FengF. DuanJ. BaiG. LiY. HuaiQ. ZhouB. HuangY.S. ChenW. TanF. GaoS. Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.Nat. Commun.2023141604210.1038/s41467‑023‑41774‑w 37758728
     [Google Scholar]
  50. ChenY. WangB. ZhaoY. ShaoX. WangM. MaF. YangL. NieM. JinP. YaoK. SongH. LouS. WangH. YangT. TianY. HanP. HuZ. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.Nat. Commun.2024151165710.1038/s41467‑024‑46043‑y 38395893
     [Google Scholar]
  51. ZhangB. ShiH. WangH. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach.J. Multidiscip. Healthc.2023161779179110.2147/JMDH.S410301 37398894
     [Google Scholar]
  52. AhsanM.M. LunaS.A. SiddiqueZ. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.Healthcare (Basel)202210354110.3390/healthcare10030541 35327018
     [Google Scholar]
  53. LoneS.N. NisarS. MasoodiT. SinghM. RizwanA. HashemS. El-RifaiW. BedognettiD. BatraS.K. HarisM. BhatA.A. MachaM.A. Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments.Mol. Cancer20222117910.1186/s12943‑022‑01543‑7 35303879
     [Google Scholar]
  54. Effectiveness of shrinkage and variable selection methods for the prediction of complex human traits using data from distantly related individuals.Ann. Hum. Genet.201882212710.1111/ahg.12243 29405268
     [Google Scholar]
  55. GodlewskiA. CzajkowskiM. MojsakP. PienkowskiT. GoskW. LysonT. MariakZ. ReszecJ. KondraciukM. KaminskiK. KretowskiM. MoniuszkoM. KretowskiA. CiborowskiM. A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors.Sci. Rep.20231311104410.1038/s41598‑023‑38243‑1 37422554
     [Google Scholar]
  56. ContiC.B. AgnesiS. ScaravaglioM. MasseriaP. DinelliM.E. OldaniM. UggeriF. Early gastric cancer: Update on prevention, diagnosis and treatment.Int J Environ Res Public Health2023203214910.3390/ijerph20032149
     [Google Scholar]
  57. SharmaA. LysenkoA. JiaS. BoroevichK.A. TsunodaT. Advances in AI and machine learning for predictive medicine.J Hum Genet10.1038/s10038‑024‑01231‑y
     [Google Scholar]
  58. ZhaoB. Kim NguyenVy. XuM. ColacinoJ.A. Olivier Jolliet. Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.Sci. Rep.2024141
     [Google Scholar]
  59. LiL.W. LiuX. ShenM.L. ZhaoM.J. LiuH. Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort.Am. J. Cancer Res.20241441609162110.62347/OJTY4008 38726282
     [Google Scholar]
  60. PapandrianosN. PapageorgiouE. AnagnostisA. PapageorgiouK. Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.PLoS One2020158e023721310.1371/journal.pone.0237213
     [Google Scholar]
  61. SchnabelJ. Ultrasensitive liquid biopsy tech spots cancer earlier than standard methods.Cornell Chronicle2024
     [Google Scholar]
  62. CrunkhornS. Improving liquid biopsy for cancer detection.Nat. Rev. Drug Discov.2024233174174 38326470
     [Google Scholar]
  63. WuC. ZhangJ. LiH. XuW. ZhangX. The potential of liquid biopsies in gastrointestinal cancer.Clin. Biochem.20208411210.1016/j.clinbiochem.2020.06.007 32540214
     [Google Scholar]
  64. ZhengY. LiuY. YangJ. YuY. DongL. ZhangR. TianS. RenL. HouW. ZhuF. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.Nat. Biotechnol.20244271133114910.1038/s41587‑023‑01934‑1
     [Google Scholar]
  65. ShaoQ. LundgrenM. LynchJ. JiangM. MirM. BischofJ. NelsonM. Tumor therapeutic response monitored by telemetric temperature sensing, a preclinical study on immunotherapy and chemotherapy.Sci. Rep.2023131772710.1038/s41598‑023‑34919‑w 37173516
     [Google Scholar]
  66. PenederP. StützA.M. SurdezD. KrumbholzM. SemperS. ChicardM. SheffieldN.C. PierronG. LapoubleE. TötzlM. ErgünerB. BarrecaD. RendeiroA.F. AgaimyA. BoztugH. EngstlerG. DworzakM. BernkopfM. Taschner-MandlS. AmbrosI.M. MyklebostO. Marec-BérardP. BurchillS.A. BrennanB. StraussS.J. WhelanJ. SchleiermacherG. SchaeferC. DirksenU. HutterC. BoyeK. AmbrosP.F. DelattreO. MetzlerM. BockC. TomazouE.M. Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden.Nat. Commun.2021121323010.1038/s41467‑021‑23445‑w 34050156
     [Google Scholar]
  67. Cisneros-VillanuevaM. Hidalgo-PérezL. Rios-RomeroM. Cedro-TandaA. Ruiz-VillavicencioC.A. PageK. HastingsR. Fernandez-GarciaD. AllsoppR. Fonseca-MontañoM.A. Jimenez-MoralesS. Padilla-PalmaV. ShawJ.A. Hidalgo-MirandaA. Cell-free DNA analysis in current cancer clinical trials: a review.Br. J. Cancer2022126339140010.1038/s41416‑021‑01696‑0 35027672
     [Google Scholar]
  68. ZhangB. ShiH. WangH. Learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach.J. Multidiscip. Healthc.2023161779179110.2147/JMDH.S410301 37398894
     [Google Scholar]
  69. AziziS. CulpL. FreybergJ. MustafaB. BaurS. KornblithS. ChenT. TomasevN. MitrovićJ. StrachanP. MahdaviS.S. WulczynE. BabenkoB. WalkerM. LohA. ChenP.H.C. LiuY. BavishiP. McKinneyS.M. WinkensJ. RoyA.G. BeaverZ. RyanF. KrogueJ. EtemadiM. TelangU. LiuY. PengL. CorradoG.S. WebsterD.R. FleetD. HintonG. HoulsbyN. KarthikesalingamA. NorouziM. NatarajanV. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.Nat. Biomed. Eng.20237675677910.1038/s41551‑023‑01049‑7 37291435
     [Google Scholar]
  70. VesperH.W MyersG.L. MillerW.G. Current practices and challenges in the standardization and harmonization of clinical laboratory tests.Am J Clin Nutr2016104907S912S10.3945/ajcn.115.110387
     [Google Scholar]
/content/journals/ccdt/10.2174/0115680096329098240920043326
Loading
Enhanced Detection of Gastrointestinal Malignancies using Machine Learning-Optimized Liquid Biopsy: A Mini Review
Curr. Cancer Drug Targets< 25, 1482 (2025); https://doi.org/10.2174/0115680096329098240920043326
/content/journals/ccdt/10.2174/0115680096329098240920043326
/content/journals/ccdt/10.2174/0115680096329098240920043326
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): circulating tumor DNA; Gastrointestinal cancers; liquid biopsy; machine learning; non-invasive diagnostics; precision medicine

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test