# Development of Deep Neural Networks for Automated Detection of Cancer Metastases in Staging Laparoscopy Images

> **NIH NIH R03** · LAHEY CLINIC · 2020 · $77,450

## Abstract

SUMMARY
For patients who undergo operative resections for gastrointestinal cancers, treatment selection
fundamentally relies on the result of intra-operative assessment of the extent of the underlying cancer (i.e.
staging). Specifically, the absence or presence of distant metastases dictates the role of operative treatment,
chemotherapy, and radiation. However, the accuracy of operative staging (i.e. staging laparoscopy) is limited
resulting in “under-staging” in up to 30% of these patients adversely affecting their cancer treatment. While
operative “under-staging” is thought to equally affect many other malignancies, the cause is believed to arise
from the inability of a conventional operative exam to reliably differentiate benign from metastatic lesions.
Recent results demonstrated that expert surgeons on average misidentify 36±19% of grossly visible
metastases questioning the accuracy of a human examiner.
 Our long-term goal is to significantly improve the accuracy of operative staging laparoscopy in patients with
gastrointestinal cancers by enhancing its capability to detect metastases through means of machine learning.
To achieve this goal, we will use existing videos from staging laparoscopies and abstract images of peritoneal
lesions that underwent biopsy (i.e. ground truth) as part of routine care (Aim 1). These images will then be
used for the development of an automated classification system. The first step of developing the classification
system involves training of a deep neural network with weak supervision that will allow for automated
segmentation of lesions from their surrounding background (Aim 2). The second step will extract feature
vectors from the lesions segmented in Aim 2 providing information for classification. The feature vectors will
be extracted by two parallel processes: unsupervised deep learning and extraction of expert-selected features.
The resulting feature vectors will be used to train a model allowing the classification (benign vs. metastasis) of
any peritoneal lesion (Aim 3).
 The results of this study are expected to provide material for future improvements / modifications of the
proposed deep learning classification system as well as the foundation for future development of an automated
surgical guidance system designed to help surgeons reliably identify metastases.
Relevance: This study will establish a robust, yet simple method to improve the staging accuracy of standard
laparoscopy via the detection of peritoneal metastases otherwise missed by human examiners. This will
significantly improve cancer care through better treatment allocation. Further, it is expected that the detection
of currently missed metastases will have a major impact on staging and treatment algorithms for a variety of
cancers.

## Key facts

- **NIH application ID:** 9984379
- **Project number:** 5R03EB027900-02
- **Recipient organization:** LAHEY CLINIC
- **Principal Investigator:** Liping Liu
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $77,450
- **Award type:** 5
- **Project period:** 2019-08-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984379

## Citation

> US National Institutes of Health, RePORTER application 9984379, Development of Deep Neural Networks for Automated Detection of Cancer Metastases in Staging Laparoscopy Images (5R03EB027900-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9984379. Licensed CC0.

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