# Radiomics and Pathomics to predict upstaging of DCIS

> **NIH NIH R01** · H. LEE MOFFITT CANCER CTR & RES INST · 2021 · $699,147

## Abstract

Abstract
 Ductal carcinomas in situ (DCIS) of the breast are a heterogeneous group of neoplastic lesions that are usually
detected by screening mammography. Workup generally includes a percutaneous (core) Biopsy (Bx) for
histologic confirmation, followed by multiparametric MRI (mpMRI), followed by breast-conserving excision, and
adjuvant radiation. Approximately 20-25% of patients with core Bx-confirmed DCIS are upstaged to invasive
carcinoma upon pathology of resected tissue. Foreknowledge of this would dictate a more aggressive surgical
intervention, including sentinel node biopsy for axillary staging. Further, another 20-25% of patients are judged
to have low-risk disease and current thought is that such women may have better outcomes in an active
surveillance setting, and this is being tested in clinical trials. The ultimate goal and the overall impact of this
project is to use machine learning to identify biochemical (SA1) or imaging (SA2) biomarkers, as well as their
combination (SA3) to discriminate indolent from aggressive DCIS, as determined by upstaging upon excisional
biopsy.
 The major hypothesis to be tested in this work is that hypoxia and expression of hypoxia-related proteins
(HRPs) can discriminate aggressive from more indolent DCIS, and that this can be used for decision support.
Expression of HRPs is optimally characterized by immunohistochemistry (IHC), and we have deployed methods
for multiplexed IHC, as well as methods for advanced analytics using machine learning (pathomics). We have
also shown that hypoxic habitats within breast cancers can be identified from mpMRI using machine learning
(radiomics). We thus propose to use pathomics of core biopsies and radiomics of mpMRI to determine the
presence and extent of hypoxic habitats in DCIS prior to surgery to predict subsequent upstaging after surgical
resection. This work will be performed in Aim 1 for pathomics and Aim 2 for radiomics, and Aim 3 will develop
combined radio-pathomics predictors. Each aim will contain: (a) retrospective arms for training, tuning, and
testing; and (b) prospective internal and external cohorts for rigorous validation. For the retrospective studies,
we have identified 604 cases wherein women with DCIS obtained core Bx, mpMRI, and surgery with pathology
at Moffitt in the last 10 years. Internal prospective studies will accrue ~6 women/month who have consented to
the total Cancer Care® protocol and who have their complete workup at Moffitt. External validation cohorts will
be accrued at UCSF and at Advent Health.
 At the end of this work we will have developed a risk model for DCIS that can be deployed prior to surgery to
guide decisions along the spectrum from active surveillance at one end to more extensive surgical intervention
at the other. This is expected to lay a foundation for subsequent interventional trials. Additionally, the inclusion
of hypoxia as a central hypothesis has high potential to illuminate components of the natural history of...

## Key facts

- **NIH application ID:** 10120171
- **Project number:** 1R01CA249016-01A1
- **Recipient organization:** H. LEE MOFFITT CANCER CTR & RES INST
- **Principal Investigator:** Mehdi Damaghi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $699,147
- **Award type:** 1
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10120171

## Citation

> US National Institutes of Health, RePORTER application 10120171, Radiomics and Pathomics to predict upstaging of DCIS (1R01CA249016-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10120171. Licensed CC0.

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