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ONCOLOGY, NUCLEAR MEDICINE AND TRANSPLANTOLOGY
Review Article
Oct 06, 2026 5 2

Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance

Richard Afriyie Osei 1 * ,
Danmegoro Suleman Taiye 2,
Godwin Kenechukwu Nwajiugo 3,
Oluwabukunmi Oluwaniran 4,
Ismaila Muhammed 5
1 Department of Nanoscience, University of North Carolina at Greensboro, Greensboro, NC, USA2 ER Department, Ar-Rafiah General Hospital, Ministry of Health, Dammam, Saudi Arabia 3 Department of Radiotherapy and Clinical Oncology, National Hospital Abuja, Abuja, Nigeria 4 Department of Anatomic Pathology, Federal Medical Center, Abeokuta, Nigeria5 Department of Mathematics, Khalifa University, Abu Dhabi, United Arab Emirates* Corresponding Author
Oncology, Nuclear Medicine and Transplantology, 2(4), 2026, onmt029, https://doi.org/10.63946/onmt/19502
Publication date: Oct 06, 2026
Full Text (PDF)

ABSTRACT

Cancer behaves as a dynamic evolutionary system, whereas conventional tissue biopsy and imaging provide incomplete temporal information about how a tumour is changing. This review critically examines whether longitudinal liquid-biopsy measurement, combined with artificial intelligence (AI), can move oncology from tumour monitoring toward genuine prediction of cancer evolution and treatment resistance. We synthesise mechanistic, clinical, and computational evidence on circulating tumour DNA, circulating tumour cells, extracellular vesicles, and multimodal liquid-biopsy platforms, together with AI approaches applied to longitudinal cancer data, drawing on landmark and recent studies across colorectal, lung, breast, and ovarian cancer. Current evidence distinguishes three increasingly demanding tasks: early detection, longitudinal monitoring, and genuine forecasting. Longitudinal circulating tumour DNA analysis can detect molecular progression and emerging resistance mutations months before radiographic progression, and machine-learning models can stratify survival using serial biomarker metrics; however, most published "AI prediction" studies demonstrate earlier detection or prognostic association rather than prospectively validated forecasting of a future, patient-specific event. Biological variability in tumour shedding, analytical noise, small and irregularly sampled longitudinal datasets, overfitting, data leakage, weak external validation, and limited interpretability constrain the transition from detection to actionable prediction. AI-enabled liquid biopsy has genuine potential to forecast tumour trajectories, but current evidence is considerably stronger for molecular monitoring and early detection than for prospectively validated, clinically actionable prediction. Realising this potential will require standardised assays, prospective longitudinal cohorts with predefined prediction horizons, rigorous external and temporal validation, calibrated and interpretable models, and interventional trials linking predictions to treatment decisions before AI-enabled liquid biopsy can be considered ready to guide clinical care.

KEYWORDS

Liquid Biopsy Circulating Tumour DNA Artificial Intelligence Machine Learning Tumour Evolution Treatment Resistance Longitudinal Monitoring Precision Oncology

CITATION (Vancouver)

Osei RA, Taiye DS, Nwajiugo GK, Oluwaniran O, Muhammed I. Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance. Oncology, Nuclear Medicine and Transplantology. 2026;2(4):onmt029. https://doi.org/10.63946/onmt/19502
APA
Osei, R. A., Taiye, D. S., Nwajiugo, G. K., Oluwaniran, O., & Muhammed, I. (2026). Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance. Oncology, Nuclear Medicine and Transplantology, 2(4), onmt029. https://doi.org/10.63946/onmt/19502
Harvard
Osei, R. A., Taiye, D. S., Nwajiugo, G. K., Oluwaniran, O., and Muhammed, I. (2026). Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance. Oncology, Nuclear Medicine and Transplantology, 2(4), onmt029. https://doi.org/10.63946/onmt/19502
AMA
Osei RA, Taiye DS, Nwajiugo GK, Oluwaniran O, Muhammed I. Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance. Oncology, Nuclear Medicine and Transplantology. 2026;2(4), onmt029. https://doi.org/10.63946/onmt/19502
Chicago
Osei, Richard Afriyie, Danmegoro Suleman Taiye, Godwin Kenechukwu Nwajiugo, Oluwabukunmi Oluwaniran, and Ismaila Muhammed. "Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance". Oncology, Nuclear Medicine and Transplantology 2026 2 no. 4 (2026): onmt029. https://doi.org/10.63946/onmt/19502
MLA
Osei, Richard Afriyie et al. "Can Liquid Biopsy Predict the Future of a Tumour? A Critical Review of AI-Enabled Longitudinal Modelling for Cancer Evolution and Treatment Resistance". Oncology, Nuclear Medicine and Transplantology, vol. 2, no. 4, 2026, onmt029. https://doi.org/10.63946/onmt/19502

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