Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning
Posted on 2020-02-14 - 16:34
Tumor-free surgical margins are critical in breast conserving surgery. In up to 38%
of the cases, however, patients undergo a second surgery since malignant cells are found at the
margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure
clear margins at the time of surgery. The objective of this study was to evaluate a random forest
classifier that make use of parameters derived from point-scanning label-free fluorescence lifetime
imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection
margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen.
FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in
this study. The supervised training was based on a previously developed registration technique
between autofluorescence imaging data and cross-sectional histology slides. A pathologist’s
histology annotations provide the ground truth to distinguish between adipose, fibrous and tumor
tissue. Current results demonstrate the ability of this approach to classify the tumor with 89%
sensitivity and 93% specificity and to rapidly ( 20 frames per second) overlay the probabilistic
classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore,
we show an iterative imaging refinement that allows surgeons to switch between rapid scans
with a customized low spatial resolution to quickly cover the specimen and slower scans with
enhanced resolution (400m per point measurement) in suspicious regions where more details
are required. In summary, this technique provides high diagnostic prediction accuracy, rapid
acquisition, adaptive resolution, nondestructive probing and facile interpretation of images, thus
it hold potential for clinical breast imaging based on label-free FLIm.
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Unger, Jakob; Hebisch, Christoph; Phipps, Jennifer; Lagarto, Joao; Kim, Hanna; Darrow, Morgan; et al. (2020). Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.4708169.v1
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AUTHORS (8)
JU
Jakob Unger
CH
Christoph Hebisch
JP
Jennifer Phipps
JL
Joao Lagarto
HK
Hanna Kim
MD
Morgan Darrow
RB
Richard Bold
LM
Laura Marcu