Dynamic Predictive Models for Side Effects Following Cancer or Cancer Treatment: A Systematic Review

Background: Advances in cancer treatments such as surgery, radiotherapy and chemotherapy have increased patient survival rates. However, these treatments often result in complications or late side-effects in patients. Accurate predictions of these side-effects are important to optimize post-treatment care focused on targeted interventions for prevention or management. Dynamic predictive models, which are specifically tailored to adapt to longitudinal patient data, offer an innovative approach to updating patient risk in light of new data. This systematic review aims to summarize the application of dynamic predictive models in predicting cancer treatment-related complications and to synthesize techniques and algorithms used in developing and validating these models.

Methods: This review was conducted following the PRISMA guidelines. A systematic search was performed across multiple databases including Scopus, Web of Science, PubMed, and IEEE Xplore to identify studies that have employed dynamic predictive models for cancer or cancer treatment related complications or side-effects. The keywords used included “dynamic prediction”, “predictive models”, “treatment side effects”, “cancer treatment” and “over time”. Studies were included if they have employed longitudinal or time-varying data to update predictions over time, reflecting the incorporation of new data at multiple time points.

Preliminary Results: A total of 506 studies were initially screened, resulting in the inclusion of 13 articles. Modelling techniques varied, including statistical models such as Cox
proportional hazards and machine learning-based models like Long Short-Term Memory (LSTM) employed for handling time series data. The included studies were found to cover various cancer types, with prostate and head and neck cancers being the most common. Treatment types included surgery, radiation therapy, chemotherapy and hormone therapy. Predicted complications ranged from biochemical recurrence to patient-reported outcomes such as voice impairment. Each model employed different strategies for dynamically incorporating follow-up data. Most studies were from 2020–2024, reflecting a recent focus on dynamic models.

Conclusion: Despite their versatility, dynamic models are not often used in oncology applications. This review highlights the diverse applications of dynamic models in predicting cancer or cancer treatment-related complications or side effects over time. These models showcase significant potential for improving post-treatment care by updating predictions as more data becomes available.