Author:
Leiloglou Maria,Kedrzycki Martha S.,Chalau Vadzim,Chiarini Nicolas,Thiruchelvam Paul T. R.,Hadjiminas Dimitri J.,Hogben Katy R.,Rashid Faiza,Ramakrishnan Rathi,Darzi Ara W.,Leff Daniel R.,Elson Daniel S.
Abstract
AbstractRe-operation due to disease being inadvertently close to the resection margin is a major challenge in breast conserving surgery (BCS). Indocyanine green (ICG) fluorescence imaging could be used to visualize the tumor boundaries and help surgeons resect disease more efficiently. In this work, ICG fluorescence and color images were acquired with a custom-built camera system from 40 patients treated with BCS. Images were acquired from the tumor in-situ, surgical cavity post-excision, freshly excised tumor and histopathology tumour grossing. Fluorescence image intensity and texture were used as individual or combined predictors in both logistic regression (LR) and support vector machine models to predict the tumor extent. ICG fluorescence spectra in formalin-fixed histopathology grossing tumor were acquired and analyzed. Our results showed that ICG remains in the tissue after formalin fixation. Therefore, tissue imaging could be validated in freshly excised and in formalin-fixed grossing tumor. The trained LR model with combined fluorescence intensity (pixel values) and texture (slope of power spectral density curve) identified the tumor’s extent in the grossing images with pixel-level resolution and sensitivity, specificity of 0.75 ± 0.3, 0.89 ± 0.2.This model was applied on tumor in-situ and surgical cavity (post-excision) images to predict tumor presence.
Funder
National Institute for Health Research (NIHR) Imperial Biomedical Research Centre
Imperial Confidence in Concept (ICiC) Joint Translational Fund
Greek Foundation for Education and European Culture
Cancer Research UK (CRUK) Imperial Centre
Publisher
Springer Science and Business Media LLC
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