A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Davide Placido ¹, Bo Yuan ^2,3,4, Jessica X Hjaltelin ¹

¹Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
²Harvard Medical School, Boston, MA, USA.
³Dana-Farber Cancer Institute, Boston, MA, USA.
⁴Broad Institute of MIT and Harvard, Boston, MA, USA.
⁵VA Boston Healthcare System, Boston, MA, USA.
⁶Boston University School of Medicine, Boston, MA, USA.
⁷Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
⁸Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁹Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁰Weill Cornell Medicine, New York City, NY, USA.
¹¹Genentech, Inc., South San Francisco, CA, USA.
¹²Brigham and Women's Hospital, Boston, MA, USA.
¹³Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. soren.brunak@cpr.ku.dk.
¹⁴Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. soren.brunak@cpr.ku.dk.
¹⁵Harvard Medical School, Boston, MA, USA. chris@sanderlab.org.
¹⁶Dana-Farber Cancer Institute, Boston, MA, USA. chris@sanderlab.org.
¹⁷Broad Institute of MIT and Harvard, Boston, MA, USA. chris@sanderlab.org.

⁰Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

Nature Medicine +

|

May 8, 2023

View abstract on PubMed

Summary

Artificial intelligence accurately predicts pancreatic cancer risk using patient health records. This early detection method identifies high-risk individuals, potentially improving survival rates for this aggressive disease.

Area Of Science

Oncology
Medical Informatics
Artificial Intelligence

Background

Pancreatic cancer presents late, leading to poor patient outcomes.
Early detection is crucial for improving survival and quality of life.
Current diagnostic methods have limitations in identifying at-risk individuals proactively.

Purpose Of The Study

To develop and validate artificial intelligence (AI) models for predicting pancreatic cancer risk.
To assess the performance of AI models using large-scale clinical datasets from Denmark and the US.
To evaluate the feasibility of implementing AI-driven surveillance programs for early pancreatic cancer detection.

Main Methods

Machine learning models were trained on sequential disease codes from electronic health records.
Data from the Danish National Patient Registry (DNPR) and US Veterans Affairs (US-VA) databases were utilized.
Model performance was evaluated using the area under the receiver operating characteristic (AUROC) curve for predicting cancer occurrence within various time windows.

Main Results

The best model trained on Danish data achieved an AUROC of 0.88 for predicting cancer within 36 months.
Excluding recent disease events (3 months prior) reduced performance to AUROC 0.83.
The model identified a relative risk of 59 for the highest-risk group (over 50 years old).
Cross-application to US data showed lower AUROC (0.71), necessitating model retraining for improved performance (AUROC 0.78).

Conclusions

AI models demonstrate significant potential for early pancreatic cancer risk prediction.
These models can inform the design of targeted surveillance programs for high-risk populations.
Early detection through AI can potentially enhance patient lifespan and quality of life.

Related Concept Videos

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...