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Jahanseir, N.

Publications and source records attributed to Jahanseir, N..

2 recordsLinked to original sources

Rapid, high-resolution, non-destructive assessments of metabolic and morphological homogeneity uniquely identify high-grade cervical precancerous lesions

PurposeTwo-photon microscopy (2PM) is an emerging clinical imaging modality with the potential to non-invasively assess tissue metabolism and morphology in high-resolution. This study aimed to assess the translational potential of 2PM for improved detection of high-grade cervical precancerous lesions. Experimental Design2P images attributed to reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and oxidized flavoproteins (FP) were acquired from the full epithelial thickness of freshly excised human cervical tissue biopsies (N = 62). Fifteen biopsies harbored high-grade squamous intraepithelial lesions (HSILs), 14 biopsies harbored low-grade SILs (LSILs), and 33 biopsies were benign. Quadratic discriminant analysis (QDA) leveraged morphological and metabolic functional metrics extracted from these images to predict the presence of HSILs. We performed gene set enrichment analysis (GSEA) using datasets available on the Gene Expression Omnibus (GEO) to validate the presence of metabolic reprogramming in HSILs. ResultsIntegrating metabolic and morphological 2P-derived metrics from finely sampled, full-thickness epithelia achieved a high 90.8 {+/-} 6.1% sensitivity and 72.3 {+/-} 11.3% specificity of HSIL detection. Notably, sensitivity (91.4 {+/-} 12.0%) and specificity (77.5 {+/-} 12.6%) were maintained when utilizing metrics from only two images at 12- and 72-{micro}m from the tissue surface. Upregulation of glycolysis, fatty acid metabolism, and oxidative phosphorylation in HSIL tissues validated the metabolic reprogramming captured by 2P biomarkers. ConclusionLabel-free 2P images from as few as two epithelial depths enable rapid and robust HSIL detection through the quantitative characterization of metabolic and morphological reprogramming, underscoring the potential of this tool for clinical evaluation of cervical precancers. Translational Relevance StatementThe colposcopy and biopsy paradigm for cervical pre-cancer detection leads to an excessive number of unnecessary biopsies, with significant economic and psychological costs. This study highlights the potential of label-free, high-resolution two photon imaging to improve this paradigm by introducing real-time morphofunctional tissue assessments. In an extensive dataset comprising freshly excised high-grade and low-grade cervical intraepithelial lesions, along with benign metaplastic and inflamed human cervical tissue biopsies, we successfully characterize a loss of morphofunctional heterogeneity indicative of high-grade precancerous changes. Leveraging a combination of two-photon imaging-derived quantitative morphofunctional metrics, our findings showcase a substantial improvement in both sensitivity and specificity of high-grade lesion detection compared to the current gold standard of colposcopy followed by a single biopsy. The demonstrated enhancement in sensitivity and specificity highlights the prospect of integrating non-invasive, label-free metabolic imaging into clinical practice, offering a more effective and efficient approach to identify and manage cervical precancerous lesions.

bioengineering↗

Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Recently, deep learning methods have been developed to facilitate the extraction of quantitative information from such images. Here, we employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-SNR, two-photon images. Two-photon excited fluorescence (TPEF) images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used. We assess the impact of the specific denoising model, loss function, data transformation, and training dataset on established metrics of image restoration when comparing denoised single frame images with corresponding six frame averages, considered as the ground truth. We further assess the restoration accuracy of six metrics of metabolic function from the denoised images relative to ground truth images. Using a novel algorithm based on deep denoising in the wavelet transform domain, we demonstrate optimal recovery of metabolic function metrics. Our results highlight the promise of denoising algorithms to recover diagnostically useful information from low SNR label-free two-photon images and their potential importance in the clinical translation of such imaging.

bioengineering↗