# Classification of Pancreatic Cancer and Normal Tissue in 2D and 3D Optical Coherence Tomography Images Using Convolutional Neural Networks: A Comparative Study

## TL;DR

This study shows that combining optical coherence tomography with AI can help distinguish cancerous from normal pancreatic tissue, potentially aiding surgeons during operations.

## Contribution

The study introduces a comparative evaluation of 2D and 3D CNN models for OCT-based classification of pancreatic cancer and normal tissue ex vivo.

## Key findings

- The 3D DenseNet121 model achieved the highest performance with an F1-score of 0.74, sensitivity of 72%, and specificity of 81%.
- AI-assisted OCT can accurately differentiate pancreatic cancer from normal tissue ex vivo, suggesting potential for intraoperative use.
- Comparable results were observed across different CNN architectures, indicating robustness in the approach.

## Abstract

Surgeons treating pancreatic cancer need to remove all cancer tissue to give patients the best chance of recovery. This study looked at whether a special imaging method, called optical coherence tomography (OCT), combined with artificial intelligence (AI), could tell cancer tissue apart from normal pancreatic tissue, which could be used to check if the entire tumor has been removed during surgery. Researchers scanned tissue that had already been removed from 27 patients with pancreatic cancer. They then trained computer programs to recognize differences between cancer and healthy tissue in these images. The best-performing program correctly identified cancer tissue most of the time and was also good at recognizing normal tissue. The results suggest that combining OCT with AI could one day help surgeons quickly check tissue during operations, possibly reducing the need for time-consuming laboratory tests. More research is needed to see how well this works during real surgeries on living patients.

Background/Objectives: Early and complete (R0) surgical resection is essential for optimal outcomes in pancreatic cancer. Optical coherence tomography (OCT) combined with artificial intelligence (AI) may offer real-time intraoperative guidance, potentially reducing reliance on frozen sections. This ex vivo study evaluated convolutional neural networks (CNNs) for distinguishing pancreatic ductal adenocarcinoma (PDAC) from normal pancreatic tissue in OCT images obtained ex vivo. Methods: Between October 2020 and April 2021, OCT scans were obtained from resected pancreatic specimens of 27 adult patients. Tumor and adjacent normal tissue were imaged using a 1310 nm OCT system, followed by histopathological confirmation. A total of 25 PDAC and 30 non-malignant scans were preprocessed and analyzed using cross-validated CNN models (ResNet50, DenseNet121, and MobileNetV2) with both 2D and 3D inputs. Results: Using five-fold stratified cross-validation on 9040 2D and 3000 3D samples (224 px resolution), the 3D DenseNet121 model achieved the highest performance, with an F1-score of 0.74, sensitivity of 72%, and specificity of 81%. Other architectures demonstrated comparable results. Conclusions: AI-assisted OCT can accurately differentiate PDAC from normal pancreatic tissue ex vivo, supporting its potential as a rapid intraoperative diagnostic adjunct. Further studies are warranted to assess its in vivo performance and utility in evaluating resection margins.