# Geles: A Novel Imaging Informatics System for Generalizable Lesion Identification in Neuroendocrine Tumors

> **NIH NIH R21** · UNIVERSITY OF COLORADO DENVER · 2023 · $388,467

## Abstract

PROJECT SUMMARY
Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are difficult to detect tumors which commonly
present at advanced stages, with the liver as the most common site of metastases. 68Ga and 64Cu DOTATATE
positron emission tomography-computed tomography (PET/CT) are the most sensitive methods to identify
somatostatin receptor subtype 2 positive GEP-NETs, and targeted peptide radionuclide receptor therapy with
177Lu DOTATATE is the most effective systemic therapy for many patients. Despite the clear advantage in
progression-free survival compared to prior standard of care, the vast majority of patients (99%) do not have
complete response and require additional therapies. Further development of treatments requires an accurate
assessment of the response to therapy. However, there is currently an unmet medical need for automated,
standardized quantification of 68Ga DOTATATE positive disease burden, which could have a great impact on
novel therapeutic drug regimen development. Deep learning-based approaches have recently been applied to
automated lesion detection and quantification, and have achieved state-of-the-art performance. These methods,
however, do not consider dataset/domain shifts between training and testing data. In dataset/domain shifts, data
used to build and train models might have a significantly different distribution from that used for model testing.
Therefore, models without considering domain shifts would not generalize well to unseen data, leading to poor
lesion detection performance. In this proposed research, we will develop a novel deep learning-based imaging
informatics system, termed Geles, for automated, Generalizable lesion detection for livers in GEP-NET PET/CT
imaging. This system will use list-mode data acquisition to produce a large, diverse annotated training dataset,
followed by novel adversarial learning to enhance model generalizability.
The proposed Geles system will consist of two modules, domain generalization and domain adaptation. Aim 1
will develop an adversarial domain generalization module that is generalizable to unseen domains or resources.
This module will build a deep neural network with domain-adversarial learning and extract domain-invariant
feature representations for individual lesion identification, so that the system can generalize to unseen domain
data, such as PET images from different institutions, devices, imaging protocols, and other variations. Aim 2 will
develop a target-oriented domain adaptation module that is automatically adaptable to new specific datasets of
interest (i.e., target datasets). Given a small set of unannotated images from a certain target dataset, this module
will conduct low-resource unsupervised domain adaptation to further boost the lesion detection performance.
Specifically, it will build a novel, augmented generative adversarial network for image-to-image translation in a
low-resource setting, so that Geles can take advantage of limited, unannotated ...

## Key facts

- **NIH application ID:** 10740578
- **Project number:** 1R21CA274487-01A1
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** BENNETT B CHIN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $388,467
- **Award type:** 1
- **Project period:** 2023-09-19 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10740578

## Citation

> US National Institutes of Health, RePORTER application 10740578, Geles: A Novel Imaging Informatics System for Generalizable Lesion Identification in Neuroendocrine Tumors (1R21CA274487-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10740578. Licensed CC0.

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