Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging

This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for the real-time analysis of tissue morphology at the tip of the biopsy needle, prior to collecting a biopsy specimen. This is an important clinical problem as up to 40% of collected biopsy cores provide low diagnostic value due to high adipose or necrotic content. Micron-scale-resolution optical coherence tomography (OCT) images can be collected with a minimally invasive needle probe and automatically analyzed using a computer neural network (CNN)-based AI software. The results can be conveyed to the clinician in real time and used to select the biopsy location more adequately. This technology was evaluated on a rabbit model of cancer. OCT images were collected with a hand-held custom-made OCT probe. Annotated OCT images were used as ground truth for AI algorithm training. The overall performance of the AI model was very close to that of the humans performing the same classification tasks. Specifically, tissue segmentation was excellent (similar to 99% accuracy) and provided segmentation that closely mimicked the ground truth provided by the human annotations, while over 84% correlation accuracy was obtained for tumor and non-tumor classification.

Automated summary

This paper introduces a combined optical imaging/artificial intelligence (OI/AI) technique for real-time analysis of tissue morphology at the tip of the biopsy needle. The technique uses micron-scale-resolution optical coherence tomography (OCT) images collected with a minimally invasive needle probe and analyzed using AI software. The evaluation on a rabbit model of cancer shows very close performance between the AI model and human classification tasks, with excellent tissue segmentation and high accuracy for tumor and non-tumor classification.