# Machine learning accelerated on-line adaptive replanning

> **NIH NIH R01** · MEDICAL COLLEGE OF WISCONSIN · 2021 · $495,299

## Abstract

Abstract. The overall goal of this proposal is to develop and test a novel machine learning (ML) accelerated
On-Line Adaptive Replanning (MOLAR) solution for magnetic resonance imaging (MRI) guided radiation
therapy (RT) (MRgRT). During the multi-fraction RT process, the location, shape and size of tumors and
normal organs vary significantly between the fractions. These interfraction variations are among the major
factors that can limit the accuracy of RT targeting. The current standard practice of image-guided RT (IGRT),
developed to address the interfraction variations based on cone-beam CT (CBCT), can only correct for
translational errors, and thus does not fully account for interfraction changes. To address this issue,
researchers recently introduced online adaptive replanning (OLAR) that generates a new plan based on the
anatomy of the day and delivers the plan for the fraction. Currently, two main obstacles affect the success of
OLAR: (1) the anatomy of the day cannot be delineated accurately based on CBCT, and (2) the time required
to perform OLAR is long enough to render it impractical. One way to improve the delineation accuracy is to use
MRI versus CT. MRI-guided OLAR is currently being introduced into the clinics to substantially improve RT
targeting. However, the bottleneck is still the impractical length of time required to segment the anatomy of the
day, which can exceed 30 minutes. Furthermore, available synthetic CT (sCT) generation methods are slow or
inaccurate for MRI-guided OLAR. There is no method available to quickly and objective determine when OLAR
is necessary. To address these issues, we plan to develop novel techniques in the MOLAR solution. We
hypothesize that the MRI-based MOLAR solution will fully account for interfraction changes, thereby
substantially improving tumor targeting during RT delivery and the effectiveness of RT. Specifically, we aim to
(1) develop practical ML-based solutions to quickly determine the necessity of OLAR and to rapidly generate
accurate synthetic CTs; (2) develop ML-based techniques to substantially accelerate segmentation for OLAR
using a progressive three-step process; and (3) verify clinical practicality and effectiveness of MOLAR by
retrospectively and prospectively applying the MOLAR on MRI sets to test its speed and effectiveness in
accounting for interfraction variations. We will develop this novel MOLAR solution by forging unique
collaborations between clinical physicists, radiation oncologists and industry developers via an established
academic-industry partnership. The successful completion of this project will enable clinicians to routinely
practice “image-plan-treat”, which is the optimal solution for MRgRT. This new paradigm will fully account for
interfraction variations, improve tumor targeting, reduce normal tissue toxicity, and ultimately encourage
clinicians to revise the current doses and/or dose fractionations to increase therapeutic gain, enhance patient
quality of lif...

## Key facts

- **NIH application ID:** 10129924
- **Project number:** 5R01CA247960-02
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** X. ALLEN LI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $495,299
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10129924

## Citation

> US National Institutes of Health, RePORTER application 10129924, Machine learning accelerated on-line adaptive replanning (5R01CA247960-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10129924. Licensed CC0.

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