# Automated interactive definition of the clinical target volume in radiation oncology

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $329,887

## Abstract

Abstract
Identifying the appropriate clinical target volume (CTV) to capture microscopic disease is the greatest limitation
in clinical radiotherapy in efforts to offer maximally conformal treatment to minimize radiation associated
toxicity. The challenge of defining the CTV comes from inherent uncertainty in the tumor spread beyond the
visible gross tumor volume (GTV). Delineation of the CTV is a laborious manual process. Furthermore, there
exists a practical disconnect between CTV contouring and the subsequent treatment plan dose optimization.
Exploration of the real tradeoff between covering malignancy with the dose effective for tumor control and
delivering potentially toxic dose to surrounding healthy tissues is currently impossible. The broad long-term
goal of this project is to make CTV definition easier and better. We will focus on two challenging disease sites,
glioma and sarcoma. Our methods can be generalized to essentially all other disease sites. The first aim is to
automate CTV definition. This will be accomplished by machine learning of barrier structures and anatomic
domains that are known to affect the spread of tumor beyond the visible GTV. The CTV will be expanded in 3D
taking the preferred direction of spread in the different anatomic domains (such as spread along muscle fibers)
into account. The second aim is to develop a user interface that lets the user interact with the automatic CTV
definition system, to avoid a black box impression. The user can edit the auto generated contour if necessary.
Any changes will be logged and used to retrain the system. The CTV expansion will be integrated in a multi-
criteria optimization system for treatment planning, where the user can interactively explore the dosimetric
impact of CTV expansion on the dose coverage of the tumor and dose burden in normal structures. In the third
aim we will test the hypotheses that this system will lead to a more consistent definition of the CTV, better time
efficiency, and better treatment plans leading to provable improvements of the expected clinical outcome. We
will make the system available as a standalone system to academic users and hospitals.

## Key facts

- **NIH application ID:** 10342574
- **Project number:** 1R01CA266275-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** THOMAS R. BORTFELD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $329,887
- **Award type:** 1
- **Project period:** 2022-01-06 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10342574

## Citation

> US National Institutes of Health, RePORTER application 10342574, Automated interactive definition of the clinical target volume in radiation oncology (1R01CA266275-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10342574. Licensed CC0.

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