# Core 1: Data Management and Computational Support Core

> **NIH NIH P01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $278,971

## Abstract

CORE 1: COMPUTATIONAL - PROJECT SUMMARY
Core 1 aims to address the important task of developing clinical and dosimetry data storage at the time of
planning, delivery and follow up of patient treatments. Traditionally, data is typically not recorded in a well-
structured and standardized way. Examples include the use of inconsistent structure names; multiple treatment
plans and lack of clarity as to which plan(s) were actually used to treat vs. used simply during the plan
optimization; and incomplete data, for instance missing contours of an organ at risk to be studied. For reliable
correlative studies, it is essential that a curation process removes such deficiencies. Such tasks are often
attempted manually. We propose to supplement manual processes with automated techniques to identify
specific problems and rectify them automatically where possible or flag them for manual intervention.
While large sets of treatment planning, clinical outcomes and research related data sets have already been
produced, access to the data is often obstructed by technical problems mentioned above and also by problems
arising from legal issues of inter-institutional data sharing and general data safety issues. In addition, data
captured at different institutions may have different elements and formats that need to be reconciled. Many
software and infrastructure solutions are already available to overcome such hurdles, but significant effort will
be necessary to implement and integrate them into an infrastructure and to maintain these to ensure efficient
and safe data management. Novel tools will have to be developed, tested and implemented, for inter-
institutional data exchange and management.
In addition to the curation of already accumulated data, accurate dose and LET distributions are necessary for
the biological studies proposed in the projects. These experiments may be prospectively designed using Monte
Carlo simulations in order to actively explore the dependence of biological and immunomodulatory effects on
both dose and LET.

## Key facts

- **NIH application ID:** 10491871
- **Project number:** 5P01CA261669-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Uwe Titt
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $278,971
- **Award type:** 5
- **Project period:** 2021-09-21 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10491871

## Citation

> US National Institutes of Health, RePORTER application 10491871, Core 1: Data Management and Computational Support Core (5P01CA261669-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10491871. Licensed CC0.

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