# Quantitative CEST MRI for GBM Early Response Prediction and Biopsy Guidance

> **NIH NIH R37** · JOHNS HOPKINS UNIVERSITY · 2022 · $367,041

## Abstract

ABSTRACT
 Despite advances in therapy, the most aggressive form of brain tumor, glioblastoma, remains almost
universally fatal. The first-line therapy for this devastating cancer is maximum feasible surgical resection,
followed by radiotherapy with concurrent temozolomide chemotherapy (CRT). It is encouraging that there are
multiple second-line therapies in clinical trials that could improve life quality or prolong survival, such as anti-
angiogenic therapy (AAT). In this scenario, the accurate determination of whether a patient is a responder or a
non-responder at an early stage following CRT has become a significant factor in clinical practice. However,
the limitations in neuroimaging complicate the clinical management of patients and impede efficient testing of
new therapeutics. Even with the improvements in advanced imaging modalities, distinguishing true progression
vs. pseudoprogression (induced by CRT), or response vs. pseudoresponse (induced by AAT) remain two of
the most formidable diagnostic dilemmas. Hence, the current gold standard for diagnosis and local therapy
planning is still based on pathologic appraisal of tissue samples. However, even this yields variable results due
to the intra-tumoral heterogeneity of treatment response. Therefore, reliable imaging tools, capable of early
prediction of the tumor response to clinical therapies, are urgently needed. Amide proton transfer-weighted
(APTw) imaging is a chemical exchange saturation transfer (CEST)-based molecular MRI technique, which
has been demonstrated to add important value to the clinical MRI assessment in neuro-oncology. However,
most currently used imaging protocols are essentially semi-quantitative, and the images obtained are often
called APTw images because of other contributions. Notably, it has been shown that quantitative CEST-MRI is
able to achieve more pure and higher APT signals in patients with brain tumors. On the other hand, deep-
learning is a state-of-the-art imaging analysis technique that provides exciting solutions with minimum human
input. In particular, the saliency maps derived act as a localizer for class-discriminative regions, and may have
great potential to guide biopsies and local treatment regimens. The goals of this proposal are to demonstrate
the potential of quantitative CEST-MRI to resolve two formidable diagnostic dilemmas for GBM patients and to
develop an automated deep-learning framework for post-treatment surveillance and biopsy guidance. This
application has three specific aims: (1) Implement and optimize the quantitative CEST-MRI technique and
quantify its accuracy in predicting early response to CRT and survival; (2) Determine the capability of
quantitative CEST-MRI to assess the response to bevacizumab; and (3) Develop a deep-learning pipeline that
includes structural and CEST images for responsiveness differentiation and stereotactic biopsy guidance. If
successful, our results—and particularly the deep-learning platform established—w...

## Key facts

- **NIH application ID:** 10319165
- **Project number:** 5R37CA248077-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Shanshan Jiang
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $367,041
- **Award type:** 5
- **Project period:** 2020-12-15 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10319165

## Citation

> US National Institutes of Health, RePORTER application 10319165, Quantitative CEST MRI for GBM Early Response Prediction and Biopsy Guidance (5R37CA248077-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10319165. Licensed CC0.

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