The Hallmarks of Predictive Oncology
Akshat Singhal, Xiaoyu Zhao, Patrick D. Wall, Emily So, G. Calderini, Alexander Partin, Natasha C. Koussa, Priyanka Vasanthakumari, Oleksandr Narykov, Yitan Zhu, Sara Jones, Farnoosh Abbas‐Aghababazadeh, Sisira Kadambat Nair, Jean‐Christophe Bélisle‐Pipon, Athmeya Jayaram, Barbara A. Parker, Kay T. Yeung, Jason I. Griffiths, Ryan Weil, Aritro Nath, Benjamin Haibe‐Kains, Trey Ideker
Abstract
Abstract The rapid evolution of machine learning has led to a proliferation of sophisticated models for predicting therapeutic responses in cancer. While many of these show promise in research, standards for clinical evaluation and adoption are lacking. Here, we propose seven hallmarks by which predictive oncology models can be assessed and compared. These are Data Relevance and Actionability, Expressive Architecture, Standardized Benchmarking, Generalizability, Interpretability, Accessibility and Reproducibility, and Fairness. Considerations for each hallmark are discussed along with an example model scorecard. We encourage the broader community, including researchers, clinicians, and regulators, to engage in shaping these guidelines toward a concise set of standards. Significance: As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks, we hope to establish standards and guidelines that enable the symbiotic development of artificial intelligence and precision oncology.