Systematic review of machine learning applications in the early prediction and management of chronic lymphocytic leukaemia.

Objective

This review assesses the efficacy of machine learning (ML) models for classification and management of Chronic Lymphocytic Leukaemia (CLL).

Methods

Twenty studies published between 2014 and 2023 were reviewed, focusing on supervised ML models to predict patient outcomes or guide treatment decisions. Studies were identified through PubMed, Google Scholar, and IEEExplore, with the final search in March 2023. Inclusion criteria consisted of studies focused on ML applications in CLL. Exclusion criteria included studies lacking sufficient methodology or focused solely on experimental settings without clinical validation. Most studies used small, single-centre datasets, potentially contributing to overfitting and limited applicability to real-world settings.

Results

Despite dataset limitations, all reviewed studies reported positive outcomes, with some demonstrating improvements in clinical workflows. Our findings advocate developing ML models using larger, multimodal, and multi-institutional datasets. Improved model interpretability and NLP implementation to harness unstructured clinical data were identified as key areas for advancement. Additionally, innovations like cross-site federated learning and automated redaction could help address data integration and privacy challenges.

Conclusion

This review underscores the transformative potential of ML in CLL management. However, addressing limitations, including diverse datasets and enhanced model interpretability, is crucial for fully leveraging ML capabilities in haemato-oncology.