Jakob Unger

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (Nu2009u2009=u2009u200920) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

Electrocautery effects on fluorescence lifetime measurements: An in vivo study in the oral cavity

Abstract Tumor removal typically involves electrocautery, but no studies to date have quantified the effect of electrocautery on fluorescence emission. Electrocautery was applied to N=4 locations of the oral cavity and striated leg muscle of a live Yorkshire pig. Autofluorescence of cauterized tissues and surrounding regions was measured at distinct time points up to 120 minutes following cauterization. The fluorescence lifetime was spectrally resolved in four spectral detection channels that maximized the signal emanating from endogenous fluorophores of interest. The autofluorescence emission (355u202fnm excitation) was temporally resolved using a high-speed digitizer; resulting fluorescence decay characteristics were retrieved using the Laguerre deconvolution technique. Histology was performed and co-registered with the autofluorescence data. Results show that cauterized tissue presents a distinct autofluorescence signature from surrounding regions immediately after cauterization. Differences become less evident with time. The autofluorescence-derived parameters suggest altered metabolism in peripheral regions compared to the region of maximal damage. Within the time-frame of this study, tissues investigated show variable degrees of recovery from the effects of electrocautery that can be monitored by changes in fluorescence lifetime characteristics. Our findings suggest delineation of pathologic conditions could be affected by tissue cauterization and that future studies in this area will be necessary.

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

Abstract. An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block’s outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67u2009u2009mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

Breast cancer margin delineation with fluorescence lifetime imaging (Conference Presentation)

The current standard of care for early stages of breast cancer is breast-conserving surgery (BCS). BCS involves a lumpectomy procedure, during which the tumor is removed with a rim of normal tissue-if cancer cells found in that rim of tissue, it is called a positive margin and means part of the tumor remains in the breast. Currently there is no method to determine if cancer cells exist at the margins of lumpectomy specimens aside from time-intensive histology methods that result in reoperations in up to 38% of cases. We used fluorescence lifetime imaging (FLIm) to measure time-resolved autofluorescence from N=13 ex vivo human breast cancer specimens (N=10 patients undergoing lumpectomy or mastectomy) and compared our results to histology. Tumor (both invasive and ductal carcinoma in situ), fibrous tissue, fat and fat necrosis have unique fluorescence signatures. For instance, between 500-580 nm, fluorescence lifetime of tumor was shortest (4.7 ± 0.4 ns) compared to fibrous tissue (5.5 ± 0.7 ns) and fat (7.0 ± 0.1 ns), P<0.05 (ANOVA). These differences are due to the biochemical properties of lipid, nicotineamide adenine dinucleotide (NADH) and collagen fibers in the fat, tumor and fibrous tissue, respectively. Additionally, the FLIm data is augmented to video of the breast tissue with image processing algorithms that track a blue (450 nm) aiming beam used in parallel with the 355 nm excitation beam. This allows for accurate histologic co-registration and in the future will allow for three-dimensional lumpectomy surfaces to be imaged for cancer margin delineation.

Three-dimensional online surface reconstruction of augmented fluorescence lifetime maps using photometric stereo (Conference Presentation)

Multi-Spectral Time-Resolved Fluorescence Spectroscopy (ms-TRFS) can provide label-free real-time feedback on tissue composition and pathology during surgical procedures by resolving the fluorescence decay dynamics of the tissue. Recently, an ms-TRFS system has been developed in our group, allowing for either point-spectroscopy fluorescence lifetime measurements or dynamic raster tissue scanning by merging a 450 nm aiming beam with the pulsed fluorescence excitation light in a single fiber collection. In order to facilitate an augmented real-time display of fluorescence decay parameters, the lifetime values are back projected to the white light video. The goal of this study is to develop a 3D real-time surface reconstruction aiming for a comprehensive visualization of the decay parameters and providing an enhanced navigation for the surgeon. Using a stereo camera setup, we use a combination of image feature matching and aiming beam stereo segmentation to establish a 3D surface model of the decay parameters. After camera calibration, texture-related features are extracted for both camera images and matched providing a rough estimation of the surface. During the raster scanning, the rough estimation is successively refined in real-time by tracking the aiming beam positions using an advanced segmentation algorithm. The method is evaluated for excised breast tissue specimens showing a high precision and running in real-time with approximately 20 frames per second. The proposed method shows promising potential for intraoperative navigation, i.e. tumor margin assessment. Furthermore, it provides the basis for registering the fluorescence lifetime maps to the tissue surface adapting it to possible tissue deformations.

Autofluorescence lifetime imaging during transoral robotic surgery: a clinical validation study of tumor detection (Conference Presentation)

Autofluorescence lifetime spectroscopy is a promising non-invasive label-free tool for characterization of biological tissues and shows potential to report structural and biochemical alterations in tissue owing to pathological transformations. In particular, when combined with fiber-optic based instruments, autofluorescence lifetime measurements can enhance intraoperative diagnosis and provide guidance in surgical procedures. We investigate the potential of a fiber-optic based multi-spectral time-resolved fluorescence spectroscopy instrument to characterize the autofluorescence fingerprint associated with histologic, morphologic and metabolic changes in tissue that can provide real-time contrast between healthy and tumor regions in vivo and guide clinicians during resection of diseased areas during transoral robotic surgery. To provide immediate feedback to the surgeons, we employ tracking of an aiming beam that co-registers our point measurements with the robot camera images and allows visualization of the surgical area augmented with autofluorescence lifetime data in the surgeon’s console in real-time. For each patient, autofluorescence lifetime measurements were acquired from normal, diseased and surgically altered tissue, both in vivo (pre- and post-resection) and ex vivo. Initial results indicate tumor and normal regions can be distinguished based on changes in lifetime parameters measured in vivo, when the tumor is located superficially. In particular, results show that autofluorescence lifetime of tumor is shorter than that of normal tissue (p < 0.05, n = 3). If clinical diagnostic efficacy is demonstrated throughout this on-going study, we believe that this method has the potential to become a valuable tool for real-time intraoperative diagnosis and guidance during transoral robot assisted cancer removal interventions.

A feasibility study on automated protein aggregate characterization utilizing a hybrid classification model

The characterization of cytoplasmic protein aggregates based on time-lapse fluorescence microscopy imaging data is important for research in neuro-degenerative diseases such as Parkinson. As the manual assessment is time-consuming and subject to significant variability, incentive for the development of an objective automated system is provided. We propose and evaluate a pipeline consisting of cell-segmentation, tracking and classification of neurological cells. Focus is specifically on the novel and challenging classification task which is covered by relying on feature extraction followed by a hybrid classification approach incorporating a support vector machine focusing on mainly stationary information and a hidden Markov model to incorporate temporal context. Several image representations are experimentally evaluated to identify cell properties that are important for discrimination. Relying on the proposed approach, classification accuracies up to 80 % are reached. By extensively analyzing the outcomes, we discuss about strengths and weaknesses of our method as a quantitative assessment tool.