# Federated Learning for Optimal Decision Making in Radiotherapy Using Panomics Analytics

> **NIH NIH R01** · H. LEE MOFFITT CANCER CTR & RES INST · 2022 · $158,678

## Abstract

The complex environment of modern radiation therapy (RT) comprises data from a rich combination of patient-
specific information including: demographics, physical characteristics of high-energy dose, features subsequent
to repeated application of image-guidance (radiomics), and biological markers (genomics, proteomics, etc.),
generated before and/or over a treatment period that can span few days to several weeks. Rapid growth of these
available and untapped “pan-Omics” data, invites ample opportunities for Big data analytics to deliver on the
promise of personalized medicine in RT. This is particularly true in promising but high-risk RT procedures such
as stereotactic body RT (SBRT), which have witnessed tremendous expansion due to clinical successes in early
disease stages and socio-economic benefits of shortened high dose treatments. This has led to the desire to
exploit these treatments into more advanced stages of cancer, however, the unknown risks associated with
increased toxicities hamper its potential. Therefore, robust clinical decision support systems (CDSSs) capable
of exploring the complex pan-Omics interaction landscape with the goal of exploiting known principles of
treatment response before and during the course of fractionated RT are urgently needed. The long-term goal of
this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which
are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation
in radiotherapy from available data. To meet this need and overcome current challenges, we will develop and
quantitively evaluate: (1) federated graph-based supervised machine learning algorithms for robust prediction
outcomes before and during RT; (2) federated deep reinforcement learning to dynamically optimize treatment
adaptation; and (3) a user-centered software prototype for RT decision support using the extendable XNAT
platform, with the broader goal of building a comprehensive real-time framework for outcome modeling and
response-based adaption in RT. We hypothesize that the use of advanced federated machine learning
techniques and user-centered tools will unlock the potentials to move from current population-based approaches
limited by subjective experiences and heuristic rules into robust, patient-specific, user-friendly CDSSs. This
approach and its corresponding software tools will be tested within two clinical RT sites of lung and liver cancers,
to demonstrate its versatility and highlight pertinent human-computer factors and cancer specific issues.
Impact statement: Patient-specific big data are now available before and/or during RT courses, offering new
and untapped opportunities for personalized treatment. This study will overcome current shortcomings of
population-based approaches and data underuse in current RT practice by investigating and developing a
federated user-centered, personalized CDSS with the need for centralize...

## Key facts

- **NIH application ID:** 10417829
- **Project number:** 3R01CA233487-05S1
- **Recipient organization:** H. LEE MOFFITT CANCER CTR & RES INST
- **Principal Investigator:** Issam M. El Naqa
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $158,678
- **Award type:** 3
- **Project period:** 2019-06-06 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10417829

## Citation

> US National Institutes of Health, RePORTER application 10417829, Federated Learning for Optimal Decision Making in Radiotherapy Using Panomics Analytics (3R01CA233487-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10417829. Licensed CC0.

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