SignificanceOf patients with early-stage breast cancer, 60% to 75% undergo breast-conserving surgery. Of those, 20% or more need a second surgery because of an incomplete tumor resection only discovered days after surgery. An intraoperative imaging technology allowing cancer detection on the margins of breast specimens could reduce re-excision procedure rates and improve patient survival.AimWe aimed to develop an experimental protocol using hyperspectral line-scanning Raman spectroscopy to image fresh breast specimens from cancer patients. Our objective was to determine whether macroscopic specimen images could be produced to distinguish invasive breast cancer from normal tissue structures.ApproachA hyperspectral inelastic scattering imaging instrument was used to interrogate eight specimens from six patients undergoing breast cancer surgery. Machine learning models trained with a different system to distinguish cancer from normal breast structures were used to produce tissue maps with a field-of-view of 1 cm2 classifying each pixel as either cancer, adipose, or other normal tissues. The predictive model results were compared with spatially correlated histology maps of the specimens.ResultsA total of eight specimens from six patients were imaged. Four of the hyperspectral images were associated with specimens containing cancer cells that were correctly identified by the new ex vivo pathology technique. The images associated with the remaining four specimens had no histologically detectable cancer cells, and this was also correctly predicted by the instrument.ConclusionsWe showed the potential of hyperspectral Raman imaging as an intraoperative breast cancer margin assessment technique that could help surgeons improve cosmesis and reduce the number of repeat procedures in breast cancer surgery.
Using dual-modality imaging, we can detect with this system the molecular composition of tissues on a 1 cm² area. Visible light imaging is done in real-time through an RGB camera, while the Raman modality uses a line-scanning system for surface imaging and has a spatial resolution of 250 μm² as well as a spectral resolution of 8 cm-1 for measurements just under 10 seconds. This system is used for ex-vivo measurements, where in a recent study, this system differentiated invasive breast cancer from normal breast tissues based on an SVM classification model.
We present a macroscopic line scanning Raman imaging system which has been modified to be suitable for intraoperative use. A sterilizable probe muzzle was designed to flatten the biological tissue ensuring its position at the focal plane of the Raman probe optics, removing the need for probe sterilization. The system uses a flexible imaging probe with a 1cm2 field of view to record fingerprint Raman images, mounted on an articulated arm that supports the probe weight and allows gentle contact with the tissue. Validation results obtained on porcine tissues show >95% classification accuracy between different tissue types.
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