# Artificial Intelligence-based decision support for chemotherapy-response assessment in Brain Tumors

> **NIH VA I01** · WM S. MIDDLETON MEMORIAL VETERANS HOSP · 2024 · —

## Abstract

ABSTRACT: In 2020, over 23,000 patients in the US will be diagnosed with Glioblastoma (GBM), a highly
aggressive brain tumor, with a dismal median survival of 15-18 months. Studies focusing on Gulf War Veterans
especially those exposed to nerve agents in Iraq in 1991 have shown a higher risk of brain tumors among
neurological diseases and a distinct neurological brain pattern as compared with the other Veterans. The
standard-of-care for GBM consists of surgical resection followed by radiotherapy combined with concomitant
and adjuvant chemotherapy. However, ~50% of GBM patients do not respond favorably to chemoradiation
following surgery. A priori identification of non-responders could allow for selection of these patients as potential
candidates for genomically-driven drug therapies (over 64 ongoing clinical trials in the US) over conventional
treatment. Further, chemotherapy costs >$100K/year. There is hence an unmet need to develop and validate
predictive biomarkers to identify up front which Veteran patients will not benefit from chemotherapy. Another
significant challenge in GBM management is the differentiation of suspicious lesions on post-treatment MRI, as
tumor recurrence or treatment-induced radiation effects. In the absence of reliable diagnosis, patients with a
benign treatment effect have to undergo an unnecessary surgical confirmation biopsy. The co-morbidities due
to unnecessary biopsies disproportionately impact Veteran GBM patients who tend to be older and have
increased comorbidity burden. Consequently, developing a companion diagnostic solution using clinical MRI
could represent a compelling solution in substantially improve quality-of-life years for Veteran GBM patients by
sparing them of the side-effects of surgery, while providing timely management in patients with tumor recurrence.
Recently, we have developed a new “Neuro-Image Risk Classifier” (NeuRisC), that uses artificial-intelligence
(AI)-driven computational features corresponding to the micro-architectural measurements of disorder in the local
intensity gradients (i.e. gradient entropy) on Gadolinium (Gd)-T1w MRI; the initial version of NeuRisC has been
shown to (a) be prognostic of GBM survival on n=203 studies (p<0.001), and (2) have an accuracy of 85% (a
37% improvement over expert readers) on n=58 studies in distinguishing radiation effects from tumor recurrence.
In this VA project, we propose to further improve, and validate the accuracy of NeuRisC by expanding our initial
feature set (using Gd-T1w MRI alone) by including (1) additional features from anatomical (T2w, FLAIR) and
functional MR sequences (perfusion), (2) a new class of biophysical deformation attributes from “normal” brain
parenchyma, and (3) peritumoral features from outside the lesion. In Aim 1, we will develop (NeuRisC)predict as a
predictive image-based marker of benefit to chemotherapy by combining intra- and peri-tumor gradient entropy
and biophysical deformation attributes from “normal” brain...

## Key facts

- **NIH application ID:** 10862550
- **Project number:** 5I01BX005842-02
- **Recipient organization:** WM S. MIDDLETON MEMORIAL VETERANS HOSP
- **Principal Investigator:** Pallavi Tiwari
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862550

## Citation

> US National Institutes of Health, RePORTER application 10862550, Artificial Intelligence-based decision support for chemotherapy-response assessment in Brain Tumors (5I01BX005842-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10862550. Licensed CC0.

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