Information entropy of quantitative chemometric endogenous fluorescence improves photonic lung cancer diagnosis.

Quantitative chemometric widefield endogenous fluorescence microscopy (CFM) maps the endogenous absolute chromophore concentration and spatial distribution in cells and tissue sections label-free from fluorescence color images under broadband excitation and detection. By quantifying the endogenous chromophores, including tryptophan, elastin, reduced nicotinamide adenine dinucleotide [NAD(P)H], and flavin adenine dinucleotide (FAD), CFM reveals the biochemical environment and subcellular structure. Here we show that the chromophore information entropy, marking its spatial distribution pattern of quantitative chemometric endogenous fluorescence at the microscopic scale, improves photonic lung cancer diagnosis with independent diagnostic power to the cellular metabolism biomarker. NAD(P)H and FAD's information entropy is found to decrease from normal to perilesional to cancerous tissue, whereas the information entropy for the redox ratios [FAD/tryptophan and FAD/NAD(P)H] is smaller for the normal tissue than both perilesional and cancerous tissue. CFM imaging of the specimen's inherent biochemical and structural properties eliminates the dependence on measurement details and facilitates robust, accurate diagnosis. The synergy of quantifying absolute chromophore concentration and information entropy achieves high accuracies for a three-class classification of lung tissue into normal, perilesional, and cancerous ones and a three-class classification of lung cancers into grade 1, grade 2, and grade 3 using a support vector machine, outperforming the chromophore concentration biomarkers.

Deep learning automated pathology in ex vivo microscopy.

Standard histopathology is currently the gold standard for assessment of margin status in Mohs surgical removal of skin cancer. Ex vivo confocal microscopy (XVM) is potentially faster, less costly and inherently 3D/digital compared to standard histopathology. Despite these advantages, XVM use is not widespread due, in part, to the need for pathologists to retrain to interpret XVM images. We developed artificial intelligence (AI)-driven XVM pathology by implementing algorithms that render intuitive XVM pathology images identical to standard histopathology and produce automated tumor positivity maps. XVM images have fluorescence labeling of cellular and nuclear biology on the background of endogenous (unstained) reflectance contrast as a grounding counter-contrast. XVM images of 26 surgical excision specimens discarded after Mohs micrographic surgery were used to develop an XVM data pipeline with 4 stages: flattening, colorizing, enhancement and automated diagnosis. The first two stages were novel, deterministic image processing algorithms, and the second two were AI algorithms. Diagnostic sensitivity and specificity were calculated for basal cell carcinoma detection as proof of principal for the XVM image processing pipeline. The resulting diagnostic readouts mimicked the appearance of histopathology and found tumor positivity that required first collapsing the confocal stack to a 2D image optimized for cellular fluorescence contrast, then a dark field-to-bright field colorizing transformation, then either an AI image transformation for visual inspection or an AI diagnostic binary image segmentation of tumor obtaining a diagnostic sensitivity and specificity of 88% and 91% respectively. These results show that video-assisted micrographic XVM pathology could feasibly aid margin status determination in micrographic surgery of skin cancer.

Budding epithelial morphogenesis driven by cell-matrix versus cell-cell adhesion.

Many embryonic organs undergo epithelial morphogenesis to form tree-like hierarchical structures. However, it remains unclear what drives the budding and branching of stratified epithelia, such as in the embryonic salivary gland and pancreas. Here, we performed live-organ imaging of mouse embryonic salivary glands at single-cell resolution to reveal that budding morphogenesis is driven by expansion and folding of a distinct epithelial surface cell sheet characterized by strong cell-matrix adhesions and weak cell-cell adhesions. Profiling of single-cell transcriptomes of this epithelium revealed spatial patterns of transcription underlying these cell adhesion differences. We then synthetically reconstituted budding morphogenesis by experimentally suppressing E-cadherin expression and inducing basement membrane formation in 3D spheroid cultures of engineered cells, which required ß1-integrin-mediated cell-matrix adhesion for successful budding. Thus, stratified epithelial budding, the key first step of branching morphogenesis, is driven by an overall combination of strong cell-matrix adhesion and weak cell-cell adhesion by peripheral epithelial cells.

Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.

SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics. AIM: We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. APPROACH: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves. RESULTS: Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs. CONCLUSIONS: From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.

A 3D biofabricated cutaneous squamous cell carcinoma tissue model with multi-channel confocal microscopy imaging biomarkers to quantify antitumor effects of chemotherapeutics in tissue.

Cutaneous squamous cell carcinoma (cSCC) causes approximately 10,000 deaths annually in the U. S. Current therapies are largely ineffective against metastatic and locally advanced cSCC. There is a need to identify novel, effective, and less toxic small molecule cSCC therapeutics. We developed a 3-dimensional bioprinted skin (3DBPS) model of cSCC tumors together with a microscopy assay to test chemotherapeutic effects in tissue. The full thickness SCC tissue model was validated using hematoxylin and eosin (H&E) and immunohistochemical histological staining, confocal microscopy, and cDNA microarray analysis. A nondestructive, 3D fluorescence confocal imaging assay with tdTomato-labeled A431 SCC and ZsGreen-labeled keratinocytes was developed to test efficacy and general toxicity of chemotherapeutics. Fluorescence-derived imaging biomarkers indicated that 50% of cancer cells were killed in the tissue after 1µM 5-Fluorouracil 48-hour treatment, compared to a baseline of 12% for untreated controls. The imaging biomarkers also showed that normal keratinocytes were less affected by treatment (11% killed) than the untreated tissue, which had no significant killing effect. Data showed that 5-Fluorouracil selectively killed cSCC cells more than keratinocytes. Our 3DBPS assay platform provides cellular-level measurement of cell viability and can be adapted to achieve nondestructive high-throughput screening (HTS) in bio-fabricated tissues.

Hyperspectral imaging in automated digital dermoscopy screening for melanoma.

OBJECTIVES: Early melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy. Artificial intelligence can generate screening algorithms that transform a set of imaging biomarkers into a risk score that can be used to classify a lesion as a melanoma or a nevus by comparing the score to a classification threshold. Melanoma imaging biomarkers have been shown to be spectrally dependent in Red, Green, Blue (RGB) color channels, and hyperspectral imaging may further enhance diagnostic power. The purpose of this study was to use the same melanoma imaging biomarkers previously described, but over a wider range of wavelengths to determine if, in combination with machine learning algorithms, this could result in enhanced melanoma detection. METHODS: We used the melanoma advanced imaging dermatoscope (mAID) to image pigmented lesions assessed by dermatologists as requiring a biopsy. The mAID is a 21-wavelength imaging device in the 350-950 nm range. We then generated imaging biomarkers from these hyperspectral dermoscopy images, and, with the help of artificial intelligence algorithms, generated a melanoma Q-score for each lesion (0 = nevus, 1 = melanoma). The Q-score was then compared to the histopathologic diagnosis. RESULTS: The overall sensitivity and specificity of hyperspectral dermoscopy in detecting melanoma when evaluated in a set of lesions selected by dermatologists as requiring biopsy was 100% and 36%, respectively. CONCLUSION: With widespread application, and if validated in larger clinical trials, this non-invasive methodology could decrease unnecessary biopsies and potentially increase life-saving early detection events. Lasers Surg. Med. 51:214-222, 2019. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Line scanning, stage scanning confocal microscope (LSSSCM).

For rapid pathological assessment of large surgical tissue excisions with cellular resolution, we present a line scanning, stage scanning confocal microscope (LSSSCM). LSSSCM uses no scanning mirrors. Laser light is focused with a single cylindrical lens to a line of diffraction-limited width directly into the (Z) sample focal plane, which is parallel to and near the flattened specimen surface. Semi-confocal optical sections are derived from the linear array distribution (Y) and a single mechanical drive that moves the sample parallel to the focal plane and perpendicular to the focused line (X). LSSSCM demonstrates cellular resolution in the conditions of high nuclear density within micronodular basal cell carcinoma.

The tryptophan metabolism enzyme L-kynureninase is a novel inflammatory factor in psoriasis and other inflammatory diseases.

BACKGROUND: Many human diseases arise from or have pathogenic contributions from a dysregulated immune response. One pathway with immunomodulatory ability is the tryptophan metabolism pathway, which promotes immune suppression through the enzyme indoleamine 2,3-dioxygenase (IDO) and subsequent production of kynurenine. However, in patients with chronic inflammatory skin disease, such as psoriasis and atopic dermatitis (AD), another tryptophan metabolism enzyme downstream of IDO, L-kynureninase (KYNU), is heavily upregulated. The role of KYNU has not been explored in patients with these skin diseases or in general human immunology. OBJECTIVE: We sought to explore the expression and potential immunologic function of the tryptophan metabolism enzyme KYNU in inflammatory skin disease and its potential contribution to general human immunology. METHODS: Psoriatic skin biopsy specimens, as well as normal human skin, blood, and primary cells, were used to investigate the immunologic role of KYNU and tryptophan metabolites. RESULTS: Here we show that KYNU(+) cells, predominantly of myeloid origin, infiltrate psoriatic lesional skin. KYNU expression positively correlates with disease severity and inflammation and is reduced on successful treatment of psoriasis or AD. Tryptophan metabolites downstream of KYNU upregulate several cytokines, chemokines, and cell adhesions. By mining data on several human diseases, we found that in patients with cancer, IDO is preferentially upregulated compared with KYNU, whereas in patients with inflammatory diseases, such as AD, KYNU is preferentially upregulated compared with IDO. CONCLUSION: Our results suggest that tryptophan metabolism might dichotomously modulate immune responses, with KYNU as a switch between immunosuppressive versus inflammatory outcomes. Although tryptophan metabolism is increased in many human diseases, how tryptophan metabolism is proceeding might qualitatively affect the immune response in patients with that disease.