Abstract
BackgroundBreast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real-time in vivo tissue characterization.MethodsEvaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real-time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements.ResultsWith DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen.ConclusionsIt was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery.Trail registration NIH US National Library of Medicine–clinicaltrails.gov, NCT01730365. Registered: 10/04/2012 https://clinicaltrials.gov/ct2/show/study/NCT01730365
Highlights
Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery
In this paper, we demonstrate the feasibility that diffuse reflectance spectroscopy (DRS) measurements can be acquired real-time and that a predictive classification model can be built to classify the measurements as normal or tumor tissue
The classification model based on a selection of wavelengths discriminated normal tissue from tumor tissue with the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively
Summary
Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. Optimal surgical treatment is achieved when all tumor tissue is resected, and histopathological evaluation of the resection specimen reveals no tumor positive margins. Since tumor positive resection margins are associated with a higher recurrence rate, these patients require additional treatment with boost radiotherapy or re-excision surgery [1, 2]. Breast surgeons are in need of a robust margin assessment tool that can assist them real-time in defining the optimal resection plane to ensure clear margins, and will help them to minimize the resected specimen volume [13,14,15,16,17,18]
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