# QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCER

> **NIH NIH R00** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $249,000

## Abstract

ABSTRACT
Breast cancer is a heterogeneous disease. Around 20% to 30% of women diagnosed with invasive breast cancer
will have a recurrence and may eventually die of their disease. Currently, there are no reliable methods to identify
which cancers will recur on an individual basis. Because of this, adjuvant therapies are given to nearly all patients
with breast cancer, but benefit only a small proportion. A similar dilemma exists for neoadjuvant treatment, many
patients fail to pathologically response to chemotherapy, and yet suffer from the associated toxicity. The
conventional one-size-fits-all approach causes overtreatment, leading to morbidities and mortalities. To avoid
these side effects, biomarkers that stratify patients with clinical relevance are critically needed for precision
medicine in breast cancer. Molecular profiling is currently used to stratify breast cancer, but is limited by the
requirement for invasive biopsy and confounded by intra-tumor genetic heterogeneity. Conversely, imaging
provides a unique opportunity for the noninvasive interrogation of the tumor, its microenvironment, and invasion
to surrounding normal tissues. We hypothesize that imaging characteristics reflect underlying tumor biology, and
quantitative imaging features can provide independent valuable information, which are synergistic to known
clinical, histologic, and genetic predictors. Accordingly, we have planned three specific aims to develop new
quantitative imaging biomarkers for breast cancer, as well as clinically and biologically validate them. In Aim 1
we plan to develop automated computational tools to robustly quantify whole tumor, intratumor subregions, and
parenchyma phenotypes from multimodal MRI. The curated breast cancer cohort (n=504) from our preliminary
study will be analyzed, with available MRI scans and manually-delineated contours of tumor and parenchyma
by board-certified radiologists. In Aim 2 we will build imaging feature-based models to predict recurrence-free
survival and treatment response separately. By integrating with clinicopathologic and genomic predictors, the
comprehensive models can predict clinical outcomes more accurately. The internal cohort (n=450) will be used
for discovery, and the multi-center prospective cohort from I-SPY (n=186) will be used for validation. In Aim 3
we will elucidate the biological underpinnings behind our newly identified prognostic and predictive imaging
biomarkers, by correlating them with biospecimen-derived phenotypes from the same tumor. In particular, we
will investigate multi-omics molecular data as well as tumor morphology from H&E stained pathology slides.
Three cohorts will be analyzed, including our internal cohort (n=450), the I-SPY cohort (n=186), and the TCGA
cohort (n=1095). For three proposed aims, we have carried preliminary studies to prove the feasibility. By
leveraging the richness of available well-annotated data and advanced artificial intelligence algorithms, it will
increase ...

## Key facts

- **NIH application ID:** 10454417
- **Project number:** 5R00CA218667-05
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Jia Wu
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $249,000
- **Award type:** 5
- **Project period:** 2020-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10454417

## Citation

> US National Institutes of Health, RePORTER application 10454417, QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCER (5R00CA218667-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10454417. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*