Aug 2026· Information· Vol 17, pp. 779· 0 citations· 84 references
TL;DR
The AI-driven cancer progression risk analytics framework (AI CanPRAF) is developed, which integrates AI taxonomies, imaging phenotypes, clinical context, progression analytics and decision support into one clinically oriented model.
Abstract
Artificial intelligence (AI) has emerged as a transformative analytical paradigm for modelling cancer progression using medical imaging and complementary clinical data. This systematic review collates and analyses AI-powered systems and techniques for cancer progression risk analytics and prediction. It provides an empirical examination of algorithmic approaches, imaging-derived feature domains, and clinical metrics that underpin progression prediction. Following the PRISMA 2020 reporting guidance, empirical English-language studies published between 2020 and the final search cutoff date of 15 May 2026 were identified from PubMed, EBSCOhost, Google Scholar and Web of Science. Forty (40) eligible studies spanning diverse cancer types were included, appraised using the MMAT and synthesised narratively. The evidence shows that both classical machine learning models and deep learning architectures are widely used to extract predictive information from radiological data. Radiomic descriptors of intratumoural heterogeneity, tumour morphology, functional imaging biomarkers and multiscale transform-based features consistently demonstrate strong associations with disease progression. Imaging-derived features were linked to clinically meaningful progression endpoints, including progression-free survival, disease-free survival, recurrence and metastasis, while clinical and molecular covariates supported risk stratification. This study further develops the AI-driven cancer progression risk analytics framework (AI CanPRAF), which integrates AI taxonomies, imaging phenotypes, clinical context, progression analytics and decision support into one clinically oriented model. The results demonstrate the growing role of multimodal AI systems in the development of clinically grounded cancer progression analytics and prediction solutions.
The importance of understanding how feature engineering contributes to breast cancer metastasis analytics is growing as artificial intelligence (AI) technologies rapidly transform the field of precision oncology. This systematic review collated, thematically synthesised, and analysed the evidence related to feature engineering processes, multimodal integration techniques and explainability mechanisms used in AI-driven breast cancer metastasis analytics. Following PRISMA 2020, studies from 2020 to 2026 were retrieved from MEDLINE, Scopus, Web of Science, Embase and IEEE Xplore. A total of 50 empirical studies investigating AI, feature engineering, radiomics and multimodal approaches for breast cancer metastasis prediction were included. The research findings reveal that contemporary AI-based breast cancer metastasis prediction is predominantly based on imaging-derived features, while clinical, pathological, biomarker and molecular variables increasingly enhance multimodal model development. Frequently applied techniques were observed across feature extraction, preprocessing, reproducibility assessment, dimensionality reduction, feature integration and explainability, all of which helped to create more interpretable, robust and clinically meaningful predictive systems. The study crafts an evidence-informed conceptual 5Fs framework comprising feature extraction or generation, feature preparation and transformation, feature selection and dimensionality reduction, feature construction and integration, and feature interpretability and explainability. The framework highlights the feature-centric nature of AI-driven breast cancer metastasis analytics and emphasises the importance of interpretable, multimodal and potentially clinically translatable predictive systems.
Wellington Kanyongo, B. Chimbo· Informatics· 0 citations
Background/Objectives: Computational and artificial intelligence (AI)-enabled imaging biomarkers are increasingly being investigated for immunotherapy-response assessment in oral squamous cell carcinoma (OSCC), but the evidence is heterogeneous. This systematic review identified, critically appraised, and synthesized imaging biomarkers used to predict or assess observed immunotherapy response in OSCC. Methods: PubMed/MEDLINE, Embase, and Scopus were searched from inception to 31 July 2026, without language restrictions. The protocol was registered in PROSPERO. Eligible studies evaluated quantitative, radiomic, machine-learning, or related medical-imaging biomarkers against pathological or radiological response. Risk of bias was assessed using design-specific tools, supplemented by a radiomics-specific methodological appraisal. A structured Synthesis Without Meta-analysis was prespecified. Results: Seven reports were included, covering pretreatment prediction, early/on-treatment dynamic assessment, and post-neoadjuvant/preoperative response assessment. Pretreatment-computed tomography and magnetic resonance imaging radiomics showed discriminatory performance, whereas baseline positron emission tomography uptake alone was not consistently associated with pathological response. Longitudinal fibroblast activation protein inhibitor positron emission tomography changes were associated with a major pathological response, and magnetic resonance imaging habitat and longitudinal habitat models showed higher within-study discrimination when combined with clinical information. No computational model had independent external validation specifically in an OSCC population, and no three independent studies were sufficiently comparable for meta-analysis. Conclusions: Computational imaging is promising across several stages of immunotherapy-response assessment. However, based on current evidence, none of the reviewed imaging biomarkers or models can yet be considered ready for routine clinical use. Prospective multicenter OSCC-specific validation and standardized imaging and analytical pipelines are needed to establish generalizability and clinical utility.
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics and provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Ujwal Havelikar, Atharva A. Shinde, Hrushikesh Mhaismale et al.· Omics· 0 citations
: Objectives: Lung cancer remains the leading cause of cancer-related mortality worldwide, and treatment response varies substantially across patients. Conventional genomic biomarkers such as EGFR, PD-L1, and tumor mutational burden (TMB) are widely used in precision oncology but have important limitations. This systematic review aimed to evaluate and compare conventional genomic biomarkers and artificial intelligence (AI)-based genomic models for predicting treatment response in lung cancer. Methods : This review was conducted according to PRISMA 2020 guidelines. PubMed, Scopus, Google Scholar, and ResearchGate were searched for studies published between January 1, 2014, and March 15, 2026. Original studies evaluating genomic biomarkers or AI-based genomic prediction models in relation to lung cancer treatment outcomes were included. Due to substantial heterogeneity across studies, findings were synthesized narratively. Results: Fifty-four studies met the inclusion criteria. Established biomarkers including EGFR, ALK, PD-L1, and TMB remained strongly associated with treatment selection and clinical outcomes, particularly in targeted therapy and immunotherapy settings. AI-based models frequently demonstrated strong predictive performance, with reported AUC values ranging from 0.70 to 0.97. However, most AI studies were retrospective and had limited prospective or external validation. Conclusion : Conventional genomic biomarkers remain central to current lung cancer treatment decisions because of their established clinical validity. AI-based genomic models show promise for improving prediction and patient stratification, but their clinical utility remains uncertain without stronger prospective validation.
Farzana Siddique, M. Shehata, M. Ghazal et al.· Oncology Research· 0 citations
Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.
