The Hallmarks of Predictive Oncology Article Swipe
Akshat Singhal
,
Xiaoyu Zhao
,
Patrick D. Wall
,
Emily So
,
G. Calderini
,
Alexander Partin
,
Natasha 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
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1158/2159-8290.cd-24-0760
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1158/2159-8290.cd-24-0760
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.
Related Topics
Concepts
Interpretability
Benchmarking
Generalizability theory
Computer science
Standardization
Relevance (law)
Set (abstract data type)
Precision medicine
Precision oncology
Personalized medicine
Data science
Medical physics
Artificial intelligence
Medicine
Psychology
Bioinformatics
Biology
Pathology
Operating system
Political science
Law
Developmental psychology
Programming language
Business
Marketing
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1158/2159-8290.cd-24-0760
- OA Status
- green
- Cited By
- 1
- References
- 139
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406087248
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406087248Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1158/2159-8290.cd-24-0760Digital Object Identifier
- Title
-
The Hallmarks of Predictive OncologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-06Full publication date if available
- Authors
-
Akshat Singhal, Xiaoyu Zhao, Patrick D. Wall, Emily So, G. Calderini, Alexander Partin, Natasha 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 IdekerList of authors in order
- Landing page
-
https://doi.org/10.1158/2159-8290.cd-24-0760Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11969157Direct OA link when available
- Concepts
-
Interpretability, Benchmarking, Generalizability theory, Computer science, Standardization, Relevance (law), Set (abstract data type), Precision medicine, Precision oncology, Personalized medicine, Data science, Medical physics, Artificial intelligence, Medicine, Psychology, Bioinformatics, Biology, Pathology, Operating system, Political science, Law, Developmental psychology, Programming language, Business, MarketingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
139Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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