Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning

Abstract

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 makes 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 (400 µm 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 holding potential for clinical breast imaging based on label-free FLIm.

Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions

“…Data modeling and machine learning techniques have been employed to analyze and interpret FLIm parameters (including fluorescence lifetime and intensity ratios for each spectral band) beyond conventional univariate statistics. When using the Laguerre expansion based deconvolution approach [116], a set of Laguerre expansion coefficients can also be incorporated into the analysis for additional discrimination value [127,155]. Several multivariate analysis methods using these parameters have been explored, adapting to the goal of each study and the complexity of the captured data.…”

“…Nonlinear classification models such as random forests (RF) [158], k‐nearest neighbors (KNN) [159] and support vector machines (SVM) [160] have been used to recognize acute tissue conditions that vary with experimental context. SVM and RF have been investigated for interpatient discrimination of breast cancer specimens imaged ex vivo [120,155] and oral and oropharyngeal cancer [127] (both in vivo and ex vivo). SVM has also been used to identify non‐melanoma skin lesions using FLIM microscopy [161].…”

“…A KNN classifier was used to detect actinic cheilitis in the lips and distinguish between normal tissue, mild dysplasia and moderate dysplasia [162]. While not all methods have been evaluated for every application discussed, the ensemble classification approach of random forests has been shown for multiple anatomical locations [127,155] to result in improved generalization between patients when identifying cancer using fiber‐based FLIm, suggesting this method produces robust classifiers for FLIm data.…”

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

“…Preliminary results are already available for applications in surgery. [183] [184] [185] [186] Limitations and open issues. With advances in high-energy pulsed lasers, hardware cameras, image analysis methods, and computational power, many exciting applications in the medical field have been proposed in the endo/laparoscopic fields.…”

Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia

“…Recently there is work by Smith et al (2019) have utilized deep neural network with FLIM data to estimate lifetime in fit free fashion. Another group demonstrated machine learning approach using random forest classifier on FLIM data for classification of tumor FLIM image (Unger et al, 2020).…”