Rapid and High-resolution Imaging of Lumpectomy Margins Using Deep Ultraviolet Fluorescence Scanning Microscopy


 Background: Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (BCS or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to efficiently and accurately assess the entire surface of an excised lumpectomy specimen during surgery. To address this problem, a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) imaging system was developed and evaluated to determine whether it could accurately detect cancer cells on the surface of excised breast tissue.Methods: 37 (22 malignant and 15 normal/benign) fresh breast tissue samples of variable size were stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from one side of each sample using low magnification (4x) and fully automated scanning. The images from each sample were stitched to form a color image. Routine histopathology was performed on each sample. Three non-medical inspectors were trained to interpret and assess the fluorescence images. Patch-level nuclear-cytoplasm ratio (N/C) was calculated and ROC analysis with the Youden index was used for tissue classification. Results: DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0 minute/cm2. DUV images show excellent visual contrast in color, tissue texture, cell density and shape between invasive lobular carcinoma (ILC), invasive ductal carcinoma (IDC) and their normal counterparts. Visual interpretation of DUV images was able to distinguish invasive carcinoma from normal/benign samples with high sensitivity (95%) and specificity (94.4%). Pairwise comparison of patch-level N/C identified significant differences (p<0.001) between normal and malignant samples. Using N/C alone was able to differentiate invasive carcinoma from normal breast tissues at the patch-level, with reasonable sensitivity (81.3%) and specificity (79.2%). Conclusions: DUV-FSM is a simple device that can rapidly image large, unprocessed breast specimens with subcellular resolution and excellent contrast which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a surgical specimen. This study supports further investigation into whether this technology can be used for intraoperative assessment of surgical margins during BCS procedures.

available. For these reasons, intraoperative pathology options are not routinely available or adopted. [13,18] Lastly, the only FDA-approved device for margin analysis, the MarginProbe, is a pen-like probe that utilizes radiofrequency spectroscopy to analyze tumor margins. [19] Limitations include low sensitivity (75.2%) and specificity (46.4%) and reliance on user-guided spot scanning.
Microscopy with UV surface excitation (MUSE) is a novel, nondestructive technology that can image fresh, unfixed tissue that is stained with multiple fluorescence dyes, thus generating fluorescence images with outstanding resolution, sharpness and contrast. [42] Yoshitake et al., [43] found that the histological features of breast tissue images obtained with a high incident angle water immersion illumination MUSE system have limited correspondence with those obtained with conventional hematoxylin and eosin (H&E) histology, and suggested that further development is needed for breast surgical applications. Xie et al. developed a MUSE system with a fully automated 3-D sample translation that can image fresh tissue at a rate of 5 min/cm 2 , and an algorithm that can create a fluorescent analog of conventional H&E images. They further demonstrated that MUSE could provide microscopic visualization of breast margin surfaces at speeds relevant for intraoperative use. [44] In this study, we investigated the translational potential of MUSE as an intraoperative tool for margin assessment during BCS. Specifically, we aimed to determine the features of MUSE images that can be used to distinguish fresh, unprocessed malignant from normal/benign breast tissues; the accuracy of the technology; and the speed a lumpectomy specimen can be surveyed. We demonstrate that: 1) a low-cost deep-ultraviolet fluorescence scanning microscope (DUV-FSM) can rapidly image (1.0 cm 2 /min) fresh breast tissues at a subcellular resolution with excellent contrast; 2) visual interpretation of DUV images can achieve excellent sensitivity and specificity in detecting invasive samples; and 3) nuclear-cytoplasm ratio (N/C) may also be used for quantitative assessment of margin status.

Imaging system
We have converted an inverted microscope to a DUV-FSM to image the surfaces of fresh tissues from breast surgical specimens. A schematic of the DUV-FSM system is shown in Figure 1. A 285 nm LED (M285L4, Thorlabs, Newton, NJ) is mounted on the right side of an inverted fluorescence microscope (EXI-310, Accu-scope, Commack, NY) for oblique back-illumination. A 325 nm short-pass filter (XUV0325, Asahi Spectra, Torrance, CA) is placed in front of the LED to block emission spectrum tails in the visible range, avoiding possible overlap with fluorescence signals. A fused silica ball lens (model #67-388, Edmund Optics, Barrington, NJ) is used as a condenser to converge the LED radiation for a smaller illumination field and improved power density. The LED, short pass filter and ball lens are mounted inside a lens tube. A 3D printed arm holds the lens tube and is mounted on an optical post to allow for easy adjustment of the LED height and illumination angle so that the illumination area is slightly larger than the field-of-view (FOV) of the 4x microscope objective. Once the position of the LED was optimized, the entire system was fixed on an optical breadboard. To image a lumpectomy specimen, the specimen is placed on one of its six margins in a 70 mm diameter quartz dish to minimize autofluorescence of the glass. The quartz dish is mounted on a robotic, stepper-motor controlled XY stage custom designed for fast mosaic imaging (ABĒMIS LLC, Cleveland, OH). The excitation/emission filter block of the microscope is switched to the empty position so that the fluorescent signals of multiple fluorophores can be captured by a color camera without having to switch emission filters during the imaging process. A cooled, USB3.0 camera (MTR3CCD06000KPA, Hangzhou ToupTek Photonics Co., Ltd, Hangzhou, China) was selected for its large image sensor and pixel size, very low dark noise, and high image transfer speed, which are very important for fast image acquisition in intraoperative margin assessment. The camera has 2748 × 2200 pixels, pixel size of 4.54 µm and active area of 14.6 × 12.8 mm 2 . A 4x apochromatic long working distance objective lens with a numerical aperture of 0.13 was selected as a compromise between good lateral resolution (2~3 µm) and a large effective imaging area of 3.48 x 2.78 mm. The FOV of the objective lens is slightly larger than the imaging area of the camera to avoid distortions at the edge of the FOV.
The microscope is housed inside a dark enclosure to prevent personnel exposure to DUV light and to eliminate background from room light.  nm. PI has a fluorescence emission in the yellow-red spectral range and EY has an emission in the green-yellow spectral range. For staining, PI and EY were dissolved in PBS (pH 7.2) to a concentration of 100 µg/ml and 1.0 mg/ml, respectively. Each specimen was stained in the PI solution for 1 minute, then in the EY solution for 20 seconds, and finally rinsed in PBS for 10 seconds. Once staining was completed, the specimen was placed onto the quartz plate of the specimen holder. A wide pallet knife was used to gently flatten the tissue against the quartz plate to remove air bubbles between the tissue and plate. Once the tissue was in the correct position, excess liquid was removed from the edges using a Kimwipe.

Imaging protocol
The specimen holder loaded with a tissue specimen was immobilized on the motorized XY stage after (fiji.sc/) was used to process the tissue images. A Fiji plugin named BaSiC [45] was applied to the saturation and lightness channels to correct for background and shadings caused by uneven and tilted illumination. The color space transform is necessary to preserve the original color information during illumination correction. After transforming back to red-green-blue (RGB) color space, image stitching was performed using a Fiji plugin developed by Preibisch et al. [46] Lastly, histogram equalization was applied to the R and G color channels of the stitched image to enhance the visual contrast.

Histopathology evaluation
Routine histopathology was used for final diagnosis of the tissue samples. Fereidouni et al. has previously shown PI and EY staining does not interfere with downstream histopathology processes. [42] Following DUV-FSM imaging, tissue specimens were returned to MCW Tissue Bank for formalin-fixed paraffin-embedded (FFPE) tissue processing. In order to obtain full face sections for histologic evaluation, an average cut depth of ~200 µm into the embedded tissue block was used during microtomy. The tissue sections were transferred to glass slides and stained with H&E. All slides were digitalized by a Panoramic 250 Flash II slide scanner (3DHistech Ltd., Budapest, Hungary). An unblinded qualitative side by side comparison of the H&E and DUV-FSM images was performed by an experienced breast pathologist (JMJ).

Visual inspection of DUV images
Visual inspection of DUV images was performed by three trained non-pathologists to evaluate the accuracy of non-pathologists to differentiate cancer from non-cancer tissue. The 37 breast tissue samples were divided into two groups: a training and a test group. The training group included 3 invasive carcinomas (2 IDC and 1 ILC) and 2 normal tissues (1 fibrotic and 1 adipose-rich breast sample), while the test set included 3 ILC, 16 IDC, 2 adipose-rich and 11 non-adipose-rich normal samples. Three non-medical inspectors (TGS, DHY and AE) who were blinded to pathological diagnosis were trained by the pathologist (JMJ) and imaging engineer (TL) during a one-hour session to visually identify the diagnostically useful features (such as adipose, ducts, cell density, infiltration, etc.) in the training DUV images using the associated H&E images. After training, each inspector was provided DUV images of samples in the test group without access to correlative H&E images. The inspectors interpreted DUV images and provided a diagnosis (invasive carcinoma vs. normal) for each of the test samples.

Quantitative image analysis
Quantitative analysis was applied to DUV tissue images to extract diagnostically useful parameters that may be useful for detecting positive tumor margins of lumpectomy specimens during BCS.
Previous studies have shown that breast cancer cells have irregular cell size and shape, enlarged nuclei, and increased N/C. [47,48] In this study, we investigated the feasibility of using N/C as a biomarker to differentiate invasive carcinoma from normal breast parenchyma at the surface of the tissue samples. Tumor region(s) on the stitched DUV image was outlined based on the corresponding H&E image. Since PI-stained cell nuclei primarily emit lights in the yellow-red wavelength range, only the R-channel of the stitched images in RGB color space was extracted and used to calculate the N/C.
The process for N/C calculation is illustrated in Figure 2. First, the color image (A&E) was converted to a R-channel image (not shown). Segmentation of the R channel image was implemented by combining edge detection and intensity thresholding.  The large patches were manually classified into adipose-rich, non-adipose-rich normal, ILC and IDC in accordance with H&E images. The number of large patches per tissue sample and total number of patches for each tissue subtype are presented in Table 1. This resulted in a total of 1,629 large patches of N/C images, including 358 patches from 18 IDC, 90 patches from 4 ILC, 176 patches from 3 adipose-rich and 1,005 patches from 12 non-adipose-rich normal tissues. All patches were used for the following comparison and classification studies. The mean N/C was compared between the 4 tissue subtypes (IDC, ILC, adipose-rich, non-adipose-rich normal groups) and between invasive (IDC, ILC) and normal tissue using Generalized Estimating Equations (GEE) to account for repeated observations per sample. Tukey's adjustment was used for multiple pairwise comparisons. [50] For classifications, ROC curves were constructed using patch-level N/C to predict invasive versus normal tissue, IDC versus ILC among invasive samples, and adipose-rich versus non-adipose-rich tissue among normal samples. The Youden Index, which weighs false positive and false negative errors equally, was used to determine the cutoff point for the calculation of patch-level sensitivity and specificity in differentiating invasive and normal tissue. The analysis was done using SAS 9.4 (SAS Institute, Cary, NC).

Breast tissue images
The study resulted in stitched DUV images and FFPE H&E images obtained from 37 breast specimens.

Performance of visual interpretation of DUV images
Results from visual interpretation of the DUV images of the 32 tissue samples in the test group by the three inspectors are summarized in Table 2

Quantitative analysis by N/C values
The 1,629 large patches of N/C images from 37 breast tissue samples were manually classified into adipose-rich, non-adipose-rich normal, ILC and IDC tissues in accordance with the H&E images. In the ROC curve in Figure 6(C), the area under the curve (AUC) was 0.8622 and the patch-level sensitivity and specificity in differentiating invasive carcinoma from normal tissues using N/C alone were determined to be 81.3% and specificity 79.2%, respectively. typically involve small microscopic foci of cancer, [51] a device with both large margin coverage and microscopic resolution is highly desirable to rapidly evaluate lumpectomy margin status intraoperatively with both low false-negative and false-positive rates. However, many technologies currently under investigation are either a point device (e.g., optical spectroscopy, i-Knife, MarginProbe) or a high resolution device with very small field of view (e.g., OCT, confocal) that requires excessive time to manually scan a surgical margin, or a wide-field imaging device with very low spatial resolution (e.g., fluorescence imaging, SFDI). Compared to near-infrared light and visible light, using DUV light to detect tumor at the surface should result in much higher spatial resolution (2 images in the X and Y directions in the user interface of the DUV-FSM software. This feature is clinically critical because it allows tumor margins of a wide range dimensions to be imaged rapidly during BCS.
The DUV-FSM system described here, like other MUSE devices, [42][43][44] directly images fresh specimens without the need for complex intraoperative radiography and/or pathology processing. This method is extremely simple, easy to use, relatively low in cost, and does not require radiology, pathology or cytology expertise, thereby making it attractive to community hospitals and surgery centers where most BCS procedures are performed. Using DUV-FSM for intraoperative margin imaging is nondestructive to the specimen and does not negatively impact postoperative pathology, which remains the gold standard for margin assessment. The sensitivity and specificity for rapid visual inspection of DUV images were high, even amongst non-expert inspectors with minimal training, and thus should not pose significant barriers to surgeon adoption or operating room workflow as most surgeons already interpret specimen radiograph results. To our knowledge, this study is the first to report the sensitivity and specificity of MUSE technology for breast margin detection.
Previous MUSE studies focused on creating H&E mimicking images by color mapping for histopathological assessment which requires a pathologist to interpret the images. While creating H&E mimicking images from MUSE images is necessary to understand if MUSE can provide the same or equivalent diagnostically useful information as one can get from routine pathological analysis, it is also important to investigate how this technology may be used in a clinical setting where large and variable margin coverage, high speed and simplicity are key factors. This DUV-FSM study emphasizes the importance of providing surgeons diagnostically interpretable information about margin status within minutes. We aim to generate high contrast images that allows a surgeon to visually diagnose or a computer algorithm (e.g., statistical or machine learning model) to determine if a margin is positive and re-excision is necessary to achieve a negative margin during the primary surgery. Best efforts are focused on achieving a balance between high resolution to obtain more cellular and subcellular details about the tissue surface and fast scan speed that allows specimen processing, imaging and interpretation of all six margins of a lumpectomy specimen. Using a low 4x magnification objective with a FOV of 4.48 mm in diameter, simultaneous excitation of two fluorescence dyes with distinct emission color (PI and EY), and a USB 3.0 color camera to detect the fluorescence signals significantly reduces the scan time for a large specimen to 1 cm 2 /min. The DUV-FSM method requires 80 seconds for sample staining and ~ 1 minute for image processing. In contrast, the Xie system requires 5 minutes for staining, 5 minutes to scan a 1.0 cm 2 margin, and 5 minutes for image processing to create fluorescent analog of H&E staining. [44] In addition to providing surgeons high contrast images for visual inspection, N/C from DUV images showed significant difference between invasive carcinoma and normal breast tissues and thus it may be used as a complementary biomarker for rapid margin detection during BCS. N/C has not been extensively used as a parameter for intraoperative assessment of breast margins.
This study has some limitations. First, pure DCIS samples were not included in the study. We are aware that the Society of Surgical Oncology (SSO) and American Society for Radiation Oncology (ASTRO) released consensus guidelines on margins in 2016 for BCS with whole breast radiation in the setting of DCIS (with or without microinvasive component), which accounts for about 25% of all breast cancers. The recommendation is to achieve a 2 mm margin to minimize the risk of tumor recurrence in the same breast and emphasizes the importance of clinical judgment in determining selective reexcision for patients with negative margins less than 2 mm. [52] We acknowledge that DUV-FSM, as well as other MUSE devices, will not be able to assess margins to 2 mm and close margin status (tumor cells present within 2 mm of the surface but not at the surface) will need to be determined on final routine pathological assessment. However, our preliminary work demonstrates the possibility of using DUV-FSM to detect positive margins for DCIS (see Fig. 4D-F), which should translate into decreased re-excision rates in positive-margin cases. Recent data from two large studies showed that women with DCIS who underwent BCS and post-lumpectomy whole breast radiation treatment with a close margin did not have a higher rate of local recurrence compared to those with a wider margin width. [53,54] Clasier et al. also concluded that a 2 mm margin may not be necessary if comprehensive surface imaging is achieved. [38] Therefore, the clinical priority to intraoperatively detect close but negative margins for DCIS cases may decrease in the future.
Second, it was challenging to obtain accurate co-registration between DUV and corresponding H&E images. While DUV fluorescence images were taken from the top 20 µm of samples due to the shallow penetration depth of DUV light, routine H&E slide cutting techniques produces H&E images up to 200 µm deeper into the FFPE tissue block. The optical sectioning thickness of ~ 20 µm is also thicker than a typical paraffin-embedded thickness of ~ 4 µm, which can cause subtle differences between DUV and H&E images. For instance, tubular-shape structures like ducts and blood vessels usually have optically clear lumina in H&E images but this is not the case in fluorescence images (Fig. 3). In Fig. 4A and 4B, one focus of DCIS appears in the H&E image but is not easily identifiable in the fluorescence image. In Fig. 5A, a topology of tissue compression and surface folding, which may be minimum is in whole lumpectomy specimens, is visible in the fluorescence image but was not observed in the H&E images.
Ideally, segmentation is expected to accurately segment all nuclei in the background, but this is difficult to achieve with simple edge detection and intensity thresholding because of the complicated textures and nuclei stacking. Fluorescence signals from cells slightly below the specimen surface can cause blurring and reduced intensity in nuclei images due to scattering, thus contributing to error in N/C calculation. A more advanced nuclei segmentation algorithm is needed for more accurate identification of cell nuclei in future studies. Benign structures like adenosis, blood vessels and ducts may also include regions with higher N/C. Given the small size of the vessels and ducts, N/C of these tissue types can be averaged down by using a large patch size. In this study, 2 × 2 mm patch size was selected for the calculation of N/C to be compatible with standard histopathology which samplings at a step of 2 mm. An optimal image patch size may be determined by comparing the ROC curves obtained at different patches with more samples.
Finally, the number of breast samples that have been imaged is relatively small. More samples are needed to calculate sensitivity and specificity for detection of positive margins more accurately. It also takes the current DUV-FSM about 1 minute to image a 1 cm 2 breast specimen. Improvements in the motorized XY stage and scanning algorithm, coupled with faster image transfer rate should reduce the scan time by a factor of at least 10. In future studies, DUV images will be used to train a deep learning model for tissue classification which should also further decrease scan time.

Conclusions
In conclusion, a standard inverted microscope has been converted to a DUV-FSM as a potentially

Ethics approval and consent to participate
The study was exempt from the Medical College of Wisconsin / Froedtert Hospital Institutional Review Board #5