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The Hallmarks of Predictive Oncology
DescriptionPredictive oncology can be defined as the branch of precision medicine focused on improving cancer treatment outcomes by customizing therapeutic decisions for each patient based on all available information – genetic, molecular, cellular, and clinical. The rapid evolution of machine learning has led to a proliferation of sophisticated predictive oncology models. While many of these models show promise in research settings, clinical adoption has moved slowly for several reasons. One key challenge lies in generalizability; models trained on preclinical datasets often fail to translate to patient data. This limitation primarily arises from limited access to data and disparities between preclinical training and real-world contexts, compounded by the inherent heterogeneity of patient populations and the dynamic nature of disease status. Another major challenge relates to model transparency and interpretability – the ability to scrutinize the inner workings of a model and explain the biomolecular factors that underlie each of its predictions. The lack of model interpretation has been recognized as one of the most important barriers to building trustworthy AI systems in high-stakes clinical applications. In addition to challenges in model development, the successful clinical application of predictive oncology models also faces infrastructure and regulatory hurdles. The financial, computational, and regulatory resources needed to run both retrospective and prospective studies are rarely available outside major biomedical research campuses, especially in low-income regions. These challenges, among others, highlight the need for structured recommendations for model development, which clearly enumerate the methodological and clinical utility risks.
To address these fundamental challenges, we propose seven hallmarks all predictive oncology models should strive to address. These are: 1) Data Relevance and Actionability, ensuring the model's input is both pertinent and actionable; 2) Expressive Architecture, denoting the model's ability to capture complex biological interactions; 3) Standardized Benchmarking, for consistent model evaluation; 4) Demonstrated Generalizability, to ensure model performance across diverse settings; 5) Mechanistic Interpretability, for understanding the biological basis of model predictions; 6) Accessibility and Reproducibility, guaranteeing user-friendly model use; and 7) Fairness, to promote equitable model application across different patient demographics and resource-constrained communities. In addition, we consider how ethical principles apply to each of the seven hallmarks to maximize the societal benefits of therapy response models. We illustrate the systematic evaluation of a predictive oncology model via a scorecard. We also formulate a hallmarks-based checklist for model developers to succinctly enumerate the advances and risks associated with a model. We hope that the broader community – not only cancer researchers but regulators, clinicians, and lawmakers – will engage in shaping these guidelines, leading to the adoption of a concise set of standards.
To address these fundamental challenges, we propose seven hallmarks all predictive oncology models should strive to address. These are: 1) Data Relevance and Actionability, ensuring the model's input is both pertinent and actionable; 2) Expressive Architecture, denoting the model's ability to capture complex biological interactions; 3) Standardized Benchmarking, for consistent model evaluation; 4) Demonstrated Generalizability, to ensure model performance across diverse settings; 5) Mechanistic Interpretability, for understanding the biological basis of model predictions; 6) Accessibility and Reproducibility, guaranteeing user-friendly model use; and 7) Fairness, to promote equitable model application across different patient demographics and resource-constrained communities. In addition, we consider how ethical principles apply to each of the seven hallmarks to maximize the societal benefits of therapy response models. We illustrate the systematic evaluation of a predictive oncology model via a scorecard. We also formulate a hallmarks-based checklist for model developers to succinctly enumerate the advances and risks associated with a model. We hope that the broader community – not only cancer researchers but regulators, clinicians, and lawmakers – will engage in shaping these guidelines, leading to the adoption of a concise set of standards.