Improved resection margins in breast-conserving surgery using Terahertz Pulsed imaging data

TL;DR

This study introduces a Bayesian classifier applied to Terahertz Pulsed Imaging data that significantly improves intraoperative detection of tumor margins in breast-conserving surgery, potentially reducing re-operation rates.

Contribution

The paper presents a novel multivariate Bayesian classifier for TPI data that enhances accuracy in distinguishing benign from malignant breast tissue during surgery.

Findings

Sensitivity of 96% in tumor detection

Specificity of 95% in tissue classification

Potential application to other tumor types

Abstract

New statistical methods were employed to improve the ability to distinguish benign from malignant breast tissue ex vivo in a recent study. The ultimately aim was to improve the intraoperative assessment of positive tumour margins in breast-conserving surgery (BCS), potentially reducing patient re-operation rates. A multivariate Bayesian classifier was applied to the waveform samples produced by a Terahertz Pulsed Imaging (TPI) handheld probe system in order to discriminate tumour from benign breast tissue, obtaining a sensitivity of 96% and specificity of 95%. We compare these results to traditional and to state-of-the-art methods for determining resection margins. Given the general nature of the classifier, it is expected that this method can be applied to other tumour types where resection margins are also critical.