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TumorOriginPredictor: A Clinically Validated and Cloud-Deployed AI Platform for Tissue-of-Origin Identification in
Saicharan Vellanki1, Paraic A Kenny2
1Issaquah High School, Issaquah, Washington, USA.
Summary
Cancer of Unknown Primary (CUP) diagnosis is challenging. A new AI tool, TumorOriginPredictor, uses genomic data to accurately predict tumor origins, improving cancer treatment and patient survival.
Area of Science:
- Oncology
- Genomics
- Artificial Intelligence
Background:
- Cancer of Unknown Primary (CUP) presents a significant diagnostic challenge, accounting for 3-5% of US cancer diagnoses.
- Accurate tumor origin identification is crucial for effective treatment, but histological methods often fail in metastatic cases.
- Empirical therapies for CUP can be toxic and ineffective due to the lack of a confirmed primary site.
Purpose of the Study:
- To develop and validate an AI-powered platform, TumorOriginPredictor, for accurate tumor origin prediction using somatic mutation data.
- To improve diagnostic accuracy for Cancer of Unknown Primary (CUP) cases, thereby guiding more effective and personalized cancer treatments.
Main Methods:
- Developed TumorOriginPredictor, a machine learning platform trained on 10,945 MSK-IMPACT mutation profiles.
- Implemented five predictive models generating rank-ordered probabilities for the top three predicted tumor origins.
- Validated the platform using 770 real-world clinical profiles and deployed it on the Azure cloud for real-time predictions.
Main Results:
- TumorOriginPredictor achieved over 72% top-3 accuracy across 12 cancer types and exceeded 80% accuracy for prevalent cancers.
- The platform demonstrated clinical validity and provides interpretable, rank-ordered probability predictions.
- Real-time prediction capability was established through cloud deployment.
Conclusions:
- TumorOriginPredictor offers a scalable and trustworthy AI solution for diagnosing Cancer of Unknown Primary (CUP).
- Accurate tumor origin prediction facilitates individualized treatment, reducing reliance on empirical therapies.
- The platform has the potential to decrease healthcare costs, save time, and improve patient survival rates.
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