Material
Patient cohort, consenting, and tissue collection: This study was
conducted at Memorial Sloan Kettering Skin Cancer Center, Hauppauge, New
York between April 2017 and February 2020. Patients undergoing Mohs
surgery for NMSCs consented to an institutional review board-approved
protocol (# 08-006) for the collection of fresh discarded specimens
(cut tangentially) after the completion of pathology analysis. Our
collection did not compromise Mohs procedures or patient care. We
excluded patients younger than 18 years of age. Also, large samples that
exceeded the imaging window area (20x20 mm) of the tissue holder or
tissues thinner than 1 mm were not included in the study.
Tissue preparation and FF-OCT imaging: Discarded tissues were
thawed from the frozen blocks and rinsed in normal saline. They were
then placed in the plastic cassette (provided with the device) with the
frozen section cut surface facing the glass window for imaging. Nicks
and their associated color codes were applied on the edges of the
specimens for the purpose of tissue orientation and subsequent
histopathology correlation. A drop of glycerin was applied to the glass
window before the tissue placement. The cassette was then closed with a
cover to secure the specimen in place. The tissue flattening was
achieved using the sponge cushion lining the inner surface of the cover.
A drop of mineral oil was added to the lens and the cassette was then
inserted into the imaging well of the device for scanning. The
technological details of the device have been previously
described14. Once the scanning process was completed,
the entire plane of 10 to 30 microns below the cut surface of the
specimen was visualized as one mosaic composed of multiple small fields
of view (FOV; 800 µm x 600 µm). The scanned images were stored on a
connected computer for analysis14.
Blinded analysis: The two expert readers (MJ, a pathologist; and
CSJC, a Mohs surgeon), first trained themselves by studying 10 mosaics
from BCC tumors and defined FF-OCT features for BCC and surrounding
normal structures (epidermis, hair follicles, sebaceous glands, eccrine
ducts, adipose tissue, vessels, and nerves), comparing them with their
corresponding histopathology. Later a test set was created using 113
FF-OCT mosaics (from 113 fresh tissues). The images used for training
were removed from the test set. All the FF-OCT images collected were
de-identified and were assigned a study number and provided to the
experts for reading independently. The readers were blinded to the
histopathology diagnosis. Each of the readers recorded findings
including the presence or absence of the tumor, type, and subtype of
tumor in a spreadsheet. Clinical data was also collected for the
consented patients including name, age, gender, clinical diagnosis, and
location of the lesion. Corresponding histopathology sections (created
at the time of Mohs evaluation) provided the closest mirror images of
the FF-OCT mosaics and were used as ground truth for the concordance of
FF-OCT reading.
Deep-learning algorithm: For the artificial intelligence (AI)
algorithm, images from 23 nodular BCC (nBCC) were used. The image sizes
were variable, and each image had more than 5,000 x 5,000 pixels with a
pixel separation of 1.332 μm. The images were chopped into patches with
512 x 512 pixels to accommodate the limitations of computation power and
storage capacity.
A convolutional neural network (CNN) classification model was built on
top of the U-Net with symmetric down and up samplings result for nBCC
detection17,24,25 (SupplementalFigure 1 ). During the CNN training phase, 1,253 image patches
with nBCC were used. The largest receptive field is 186 x 186
μm2. During training, the cross-entropy loss was used
as the baseline for evaluation with 5-fold cross-validation. In addition
to mitigation of imbalanced nBCC and non-nBCC classes, the focal loss
was also adopted to improve the segmentation performance. As shown in
Eq. (1), the focal loss is defined to down-weight easy examples and
focus training on hard negatives. The focal loss down weights easy
examples with a factor of (1 − pt)γ so that the model can focus on
learning the misclassified pixels24.
\(\text{FL}\left(p_{t}\right)=\ {-\alpha}_{t}\left(1-p_{t}\right)^{\gamma}log(p_{t})\)(1)
where \(\alpha_{t}\) is the weighting factor to address the class
imbalance issue, γ is the focusing parameter, and \(p_{t}\) is defined
in Eq. (2)].
\(p_{t}=\left\{\par
\begin{matrix}p\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ,if\ y=1\\
1-p\ \ \ \ \ \ \ \ \ \ \ \ \ ,otherwise\\
\end{matrix}\right.\ \) (2)
where p ∈ [0,1], is the model’s estimated probability for the class
with y=1 (nBCC pixels). In our two-class scenario, y= -1 for non-nBCC
pixel.
The focal loss was first employed to eliminate the OCT mosaic artifacts.
After the segmentation model, a classification model was used to
differentiate the BCC tumor nodules from other tissues.
To quantitatively evaluate the image segmentation performance, mean
intersection over union
(mIOU) was used to measure the overlapping between the predicted and
annotated image pixels.
In addition to image segmentation, a classification model was built on
top of the U-Net result for nBCC detection of the excised tissues. The
post-segmentation image erosion process was applied to
reduce the fragmented dusty pixels. In addition, a voting strategy was
adopted on the outputs of the U-Net patches by partitioning each of the
512x512-pixel patches to 128x128-pixel patches for both hard and soft
votings. As a result, 22,386 small patches were generated for training,
and among them, 10,193 small patches have nBCC pixels. Resnet18 was used
as the classification model. And 5-fold cross-validation was applied.