Abstract :

Cancer Remains One Of The Leading Causes Of Mortality Worldwide, Creating A Persistent Need For Accurate, Early, And Accessible Detection Systems. Recent Advances In Deep Learning Have Demonstrated Substantial Potential For Automated Analysis Of Medical Images, Histopathological Slides, Genomic Profiles, And Clinical Records. However, Most Existing Cancer-detection Systems Remain Narrowly Designed Around A Single Cancer Type, Modality, Dataset, Or Prediction Task, Limiting Their Generalizability And Clinical Utility. This Review Examines The Transition From Conventional Cancerspecific Deep Learning Toward A Unified Predictive Oncology Framework Capable Of Supporting Multi-cancer Detection Through Multimodal Representation Learning. The Review Synthesizes Developments In Convolutional Neural Networks, Vision Transformers, Transfer Learning, Self-supervised Learning, Multimodal Fusion, Explainable Artificial Intelligence, Continual Learning, Federated Learning, Uncertainty Estimation, And Efficient Edge Inference. Particular Attention Is Given To The Integration Of Radiological Imaging, Histopathology, Genomics, And Clinical Information For Detecting And Characterizing Multiple Cancer Types. A Conceptual Oncology Foundation Model (OncoFM) Is Proposed As A Shared Representation Layer That Can Be Adapted To Cancer Detection, Tumor Segmentation, Subtype Classification, Staging, Prognosis, And Treatment-response Prediction. The Review Further Identifies Major Methodological Limitations, Including Patient-level Data Leakage, Dataset Bias, Modality Imbalance, Limited External Validation, Inadequate Calibration, Poor Interpretability Assessment, And Insufficient Evidence From Real-world Clinical Environments. A Fiveplane Reference Architecture Comprising Data Acquisition, Representation, Adaptation, Clinical Reasoning, And Decision-support Planes Is Presented. Finally, A Staged Research Roadmap Is Proposed Toward Multimodal, Explainable, Continually Adaptive, Privacypreserving, And Clinically Validated Predictive Oncology Systems. The Review Argues That Future Progress Depends Not Only On Increasing Model Complexity But Also On Standardized Datasets, Transparent Evaluation, External Clinical Validation, And Responsible Integration Of Artificial Intelligence Into Oncology Workflows.

Published:

21-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

303 - 309

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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