# Quantitative Edge Analysis Can Differentiate Pancreatic Carcinoma from Normal Pancreatic Parenchyma

## TL;DR

This study introduces a new imaging method to distinguish pancreatic cancer from normal tissue by analyzing edge irregularities in CT scans.

## Contribution

The novel contribution is a quantitative edge analysis technique that achieves high accuracy in detecting pancreatic adenocarcinoma.

## Key findings

- Quantitative edge analysis showed significant differences between healthy and cancerous pancreatic borders.
- A threshold SD value enabled 96% specificity and sensitivity in differentiating adenocarcinoma.
- The method provides a useful diagnostic tool and potential starting point for machine learning applications.

## Abstract

This study aimed to introduce specific image feature analysis, focusing on pancreatic margins, and to provide a quantitative measure of edge irregularity, evidencing correlations with the presence/absence of pancreatic adenocarcinoma. We selected 50 patients (36 men, 14 women; mean age 63.7 years) who underwent Multi-detector computed tomography (MDCT) for the staging of pancreatic adenocarcinoma of the tail of the pancreas. Computer-assisted quantitative edge analysis was performed on the border fragments in MDCT images of neoplastic and healthy glandular parenchyma, from which we obtained the root mean square deviation SD of the actual border from the average boundary line. The SD values relative to healthy and neoplastic borders were compared using a paired t-test. A significant SD difference was observed between healthy and neoplastic borders. A threshold SD value was also found, enabling the differentiation of adenocarcinoma with 96% specificity and sensitivity. We introduced a quantitative measure of boundary irregularity, which correlates with the presence/absence of pancreatic adenocarcinoma. Quantitative edge analysis can be promptly performed on select border fragments in MDCT images, providing a useful supporting tool for diagnostics and a possible starting point for machine learning recognition based on lower-dimensional feature space.