Fig 1.
Peptide cleavage and fluorescence release.
Fig 2.
Contrast agent structure and fluorescence release full protease cleavage.
Fig 3.
Contrast agent activation kinetics.
Fig 4.
Simplified schematic of the multimodal optical imaging microscope.
Fig 5.
Photograph of the multimodal microscope.
A. General view; B- Inside view.
Fig 6.
Main steps for data acquisition and processing.
A: Bright field image of the specimen; B: False-color fluorescence imaging taken after specimen incubation with the contrast agent; C and C’: RCM images and zoomed in region shown in red box, respectively., D: OCT image of one B-scan; E automated segmentation of the OCT data through full volumetric image (one cross section shown here); F- Overlay of the positive margins on the specimen picture.
Fig 7.
Simplified representation of the automated algorithm for tissue differentiation.
A. Representative en-face and cross-sectional (b-scan) training images for adipose tissue and tumor tissue (benign representative images not shown for brevity). B. Principal component analysis of 9 spatial texture features shows some clustering in the 2 strongest components, but also considerable overlap. C. Singular value decomposition cumulative stair function showing that 5 or fewer parameters should enable strong classification capability.
Table 1.
Summary of imaging and histopathology findings.
Fig 8.
Multimodal optical imaging findings on a small surgical resection specimen (~14 mm x 8mm).
A. Camera image of the specimen on the microscope stage. B. Fluorescence imaging, where the green box shows the selected imaging ROI in the software, and the yellow-dotted line shows the area where there is higher fluorescence from the contrast agent. C. The histology slide, where red indicates invasive ductal carcinoma. D. Segmented enface OCT image showing cancer margins with corresponding X and Y cross-sectional images. Small red and yellow boxes show region of RCM images in panels E&F. Colored overlays shows algorithmic segmentation of tissues: tumor (red), adipose (green), and benign (blue). The cross-sectional images have segmented tumor overlaid in red. G. Projection of the cancer areas from the multiple OCT slices over a depth of approximately 1 mm. H. Overlay of the positive margins on the surgical specimen.
Fig 9.
Example of positive margins identification on a small lumpectomy specimen.
A. Camera image of the specimen on the microscope stage. B. Fluorescence imaging, where the red box shows the selected imaging ROI in the software, and the yellow-dotted line shows the area where there is higher fluorescence from the contrast agent. C. The histology slide, where red indicates invasive ductal carcinoma. D. Segmented enface OCT image with corresponding X and Y cross-sectional images. Dotted-yellow line shows the plane of the cross-sectional images. Colored overlays show algorithmic segmentation of tissues: tumor (red), adipose (green), and benign (blue), Colored boxes show region of RCM images in panels E-F. The z-depth shown in F corresponds to the imaging depth for all RCM panels G) Projection of the cancer areas from the multiple OCT slices over a depth of approximately 1 mm shows larger predicted tumor areas at depth than in panel D. H. Overlay of the positive margins on the surgical specimen.
Fig 10.
Example of negative margin identification on a small lumpectomy specimen.
A) Camera photo of the specimen on the microscope stage. B) Fluorescence imaging seems to indicate positive margin presence (red asterisk). Green square indicates the imaging ROI selected in the software. C) Annotated histology indicating a benign breast lobule (yellow) and normal duct (green). D) Enface OCT image with corresponding X and Y cross-sectional images. Dotted-yellow line shows the plane of the cross-sectional images. Red and Yellow boxes show region of RCM images in E and F panels. Colored overlays show algorithmic segmentation of tissues: tumor (red), adipose (green), and benign (blue). E) RCM image of lower part of the normal duct area. F) RCM image of adipose tissue. z-depth of RCM image is consistent at 100 μm.
Fig 11.
Example of negative margin identification and false positives on a heterogeneous benign specimen.
A) Camera photo of the specimen on the microscope stage. B) Fluorescence imaging seems to indicate an area of tumor in the top-right corner (yellow). C) Annotated histology indicating benign breast lobules (blue). D) Enface OCT image with corresponding X and Y cross-sectional images. Dotted-yellow line shows the plane of the cross-sectional images. Red and Yellow arrowheads show the region of RCM images in E and F panels. Colored overlays show algorithmic segmentation of tissues: tumor (red), adipose (green), and benign (blue). Colored boxes show region of RCM images in panels E & F. E) RCM image of lower part of the normal duct area. F) RCM image of adipose tissue. z-depth of RCM image is consistent at 100 μm. G) Enface OCT image with only the false positive algorithmic tumor predictions shown.
Fig 12.
Example of positive margin identification and potentially false negatives indicated by histopathology on a heterogeneous specimen.
A) Camera photo of the specimen on the microscope stage. B) Fluorescence imaging seems to indicate an area of tumor in the top-right corner (yellow). C) Annotated histology indicating cancer areas (red annotations). D) Enface segmented OCT image with corresponding X and Y cross-sectional images. Dotted-yellow line shows the plane of the cross-sectional images. E) En face OCT image. Red and Blue boxes on the OCT image show the regions of RCM images in the F and G panels. H) Overlay of the positive margins on the surgical specimen.