# Development and Dissemination of KiNet: A Novel Imaging Informatics Tool for Gastrointestinal and Pancreatic Neuroendocrine Tumors

> **NIH NIH R21** · UNIVERSITY OF COLORADO DENVER · 2020 · $202,928

## Abstract

PROJECT SUMMARY
Neuroendocrine tumors (NETs) are one heterogeneous type of cancer affecting most organ systems. NETs must
be correctly graded to ensure proper treatment and patient management. The proliferation index, as measured
by Ki67 nuclear staining, is required for gastrointestinal (GI) and pancreatic NET grading per the criteria of the
World Health Organization (WHO). Measuring the Ki67 labeling index (Ki67 LI) from pathology images requires
accurate quantification of immunopositive tumor, immunonegative tumor and non-tumor cells. This process is an
essential procedure in basic, translational and clinical research and in routine clinical practice. However, current
Ki67 image analysis tools have a number of drawbacks: 1) Ki67 LI assessment is still mainly achieved with
manual or semi-automated methods, leading to increased labor costs, awkward workflows, low-throughput
image analysis and significant potential inter- and intra-observer variability; 2) computer-aided Ki67 counting is
error-prone due to the multi-stage image processing design, where each stage itself is a very challenging task;
3) current algorithm design does not take into consideration the characteristics of Ki67 images such that it has
technical difficulty in classifying different types of cells in Ki67 stained images. In this proposed research, we
seek to develop and disseminate a novel deep learning-based imaging informatics system, KiNeT, specifically
for better automated Ki67 LI measurement in GI and pancreatic NETs. KiNet will take advantage of cutting-edge
machine learning algorithms, deep fully convolutional networks (FCNs), to develop an end-to-end, pixel-to-pixel
model for single-stage Ki67 LI assessment. To this end, we will first formulate Ki67 counting as a cell identification
problem and solve it using class-aware structured regression modeling within a novel FCN network. This network
will simultaneously detect and classify immunopositive tumor, immunonegative tumor and non-tumor cells. Next,
we will further enhance cell identification with another related task, extraction of regions of interest (ROIs), which
will differentiate tumor from non-tumor regions by taking Ki67 image characteristics into consideration. These
two tasks will be unified into one single neural network and jointly learned to benefit both cell identification and
region classification. KiNet will provide a novel computational method for accurate Ki67 LI assessment, thereby
enabling early detection and targeted treatments of the diseases. Compared to manual counting and current
Ki67 image analysis methods, it will significantly improve the objectivity, consistency, reliability, reproducibility
and efficiency. Additionally, the proposed single-stage Ki67 counting strategy, which is completely different from
current multi-stage Ki67 image analysis pipelines, will provide a new perspective for Ki67 image quantification.

## Key facts

- **NIH application ID:** 9905506
- **Project number:** 5R21CA237493-02
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Toby Charles Cornish
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $202,928
- **Award type:** 5
- **Project period:** 2019-04-02 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9905506

## Citation

> US National Institutes of Health, RePORTER application 9905506, Development and Dissemination of KiNet: A Novel Imaging Informatics Tool for Gastrointestinal and Pancreatic Neuroendocrine Tumors (5R21CA237493-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9905506. Licensed CC0.

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