Bladder cancer (BC) in US men is costly and common; its high cost largely from its high recurrence rate (>50%), which necessitates frequent surveillance. We aim to change the paradigm around how BC surveillance is performed by validating new tools with high sensitivity and specificity for carcinoma in situ. In this talk, I discuss our innovative solutions to improve mapping the bladder for longitudinal tracking of suspicious lesions and to create miniature tools for optical detection based on machine learning, computer vision and optical coherence tomography.
SignificanceSuccessful differentiation of carcinoma in situ (CIS) from inflammation in the bladder is key to preventing unnecessary biopsies and enabling accurate therapeutic decisions. Current standard-of-care diagnostic imaging techniques lack the specificity needed to differentiate these states, leading to false positives.AimWe introduce multiparameter interferometric polarization-enhanced (MultiPIPE) imaging as a promising technology to improve the specificity of detection for better biopsy guidance and clinical outcomes.ApproachIn this ex vivo study, we extract tissue attenuation-coefficient-based and birefringence-based parameters from MultiPIPE imaging data, collected with a bench-top system, to develop a classifier for the differentiation of benign and CIS tissues. We also analyze morphological features from second harmonic generation imaging and histology slides and perform imaging-to-morphology correlation analysis.ResultsMultiPIPE enhances specificity to differentiate CIS from benign tissues by nearly 20% and reduces the false-positive rate by more than four-fold over clinical standards. We also show that the MultiPIPE measurements correlate well with changes in morphological features in histological assessments.ConclusionsThe results of our study show the promise of MultiPIPE imaging to be used for better differentiation of bladder inflammation from flat tumors, leading to a fewer number of unnecessary procedures and shorter operating room (OR) time.
Blue light cystoscopy (BLC) and white light cystoscopy (WLC) are standard of care tools to image the bladder for suspicious areas of tumor development. Having clear, high-quality frames in cystoscopy videos are crucial to sensitive, efficient detection of bladder tumors. Vessel features carry rich information but are often lost or poorly visualized in frames containing illumination artifacts or impacted by impurities in the bladder. In our study, we introduced an automatic WLC and BLC classification method for cystoscopy video analysis and proposed an image enhancement pipeline that addresses the loss of features for cystoscopy videos containing WLC and BLC frames.
Blue light cystoscopy (BLC) has been demonstrated to detect bladder tumors with better sensitivity than white light cystoscopy (WLC); however, the use of BLC is limited to the operating room. In this study, we aim to bring BLC to the clinic by transforming WLC frames into digitally-stained BLC-like frames. We collected region-matched WLC and BLC videos from TURBT procedures and generated BLC-like frames, using WLC frames as input and the matched BLC frames as target. We will discuss the staining performances with perfectly registered WLC-BLC datasets, as well as WLC and BLC video clips collected with commercial clinical systems.
Detecting early-stage glaucoma remains a challenge in current clinical practice. In this study we assessed the ability of the optical attenuation coefficient (AC) of the retinal nerve fiber layer (RNFL) to detect early-stage glaucoma, evaluated the effectiveness of the AC against the conventional RNFL thickness measurement, and introduced new depth-dependent diagnostic parameters. Our results showed statistically significant differences between ACs extracted from the RNFL of healthy eyes and early-stage glaucoma eyes, including glaucoma suspects and mild open-angle glaucoma. We also showed that depth-dependent AC analysis is an even more sensitive measure to monitor and detect early signs of glaucoma.
Significance: Tissue birefringence is an important parameter to consider when designing realistic, tissue-mimicking phantoms. Options for suitable birefringent materials that can be used to accurately represent tissue scattering are limited.
Aim: To introduce a method of fabricating birefringent tissue phantoms with a commonly used material—polydimethylsiloxane (PDMS)—for imaging with polarization-sensitive optical coherence tomography (PS-OCT).
Approach: Stretch-induced birefringence was characterized in PDMS phantoms made with varying curing ratios, and the resulting phantom birefringence values were compared with those of biological tissues.
Results: We showed that, with induced birefringence levels up to 2.1 × 10 − 4, PDMS can be used to resemble the birefringence levels in weakly birefringent tissues. We demonstrated the use of PDMS in the development of phantoms to mimic the normal and diseased bladder wall layers, which can be differentiated by their birefringence levels.
Conclusions: PDMS allows accurate control of tissue scattering and thickness, and it exhibits controllable birefringent properties. The use of PDMS as a birefringent phantom material can be extended to other birefringence imaging systems beyond PS-OCT and to mimic other organs.
In glaucoma, degeneration occurs in the retinal ganglion cells, whose cell bodies reside in the retinal nerve fiber layer (RNFL), leading to a decrease in the RNFL thickness. In clinical practice, optical coherence tomography (OCT) is used to measure the RNFL thickness for glaucoma diagnosis. Recent studies have shown that the degenerative process induces changes in optical properties of the RNFL, such as reflectivity. Because such properties can also be determined with OCT, we quantify this reduced scattering effect by determining spectroscopic attenuation coefficient (AC) in the RNFL with our proprietary autoConfoal algorithm, from two OCT B-scans of glaucomatous eyes.
The optical attenuation coefficient (AC), an important tissue parameter that measures how quickly incident light is attenuated when passing through a medium, has been shown to enable quantitative analysis of tissue properties from optical coherence tomography (OCT) signals. Successful extraction of this parameter would facilitate tissue differentiation and enhance the diagnostic value of OCT. In this review, we discuss the physical and mathematical basis of AC extraction from OCT data, including current approaches used in modeling light scattering in tissue and in AC estimation. We also report on demonstrated clinical applications of the AC, such as for atherosclerotic tissue characterization, malignant lesion detection, and brain injury visualization. With current studies showing AC analysis as a promising technique, further efforts in the development of methods to accurately extract the AC and to explore its potential use for more extensive clinical applications are desired.
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