scholarly journals Prostate Cancer Histopathology Using Label-free Multispectral Deep-UV Microscopy Quantifies Phenotypes of Tumor Aggressiveness and Enables Multiple Diagnostic Virtual Stains

2021 ◽  
Author(s):  
Soheil Soltani ◽  
Ashkan Ojaghi ◽  
Hui Qiao ◽  
Nischita Kaza ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Low Cost ◽  
Histopathological Analysis ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Tissue Sections ◽  
Adversarial Network ◽  
Important Challenge ◽  
Deep Uv

Abstract Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

scholarly journals Prostate cancer histopathology with label-free multispectral deep UV microscopy quantifies phenotypes of tumor grade and aggressiveness

2021 ◽  
Author(s):  
Soheil Soltani ◽  
Ashkan Ojaghi ◽  
Hui Qiao ◽  
Nischita Kaza ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Low Cost ◽  
Tumor Grade ◽  
Histopathological Analysis ◽  
Basal Cells ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Tissue Sections ◽  
Deep Uv

Abstract Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To help address this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and inflammation. Further, we show that this phenotypical continuum can be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly, we adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images. Agreement between the virtual H&E images and the gold standard H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

scholarly journals CAPTCHA recognition based on a generative-adversarial network

2020 ◽  
pp. 40-48
Author(s):  
Anatoliy Parfenov ◽  
Peter Sychov
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Topic ◽  
Generative Adversarial Network ◽  
The Past ◽  
Adversarial Network ◽  
Competitive Network ◽  
New Research

CAPTCHA recognition is certainly not a new research topic. Over the past decade, researchers have demonstrated various ways to automatically recognize text-based CAPTCHAs. However, in such methods, the recognition setup requires a large participation of experts and carries a laborious process of collecting and marking data. This article presents a general, low-cost, but effective approach to automatically solving text-based CAPTCHAs based on deep learning. This approach is based on the architecture of a generative-competitive network, which will significantly reduce the number of real required CAPTCHAs.

scholarly journals Probing single-cell metabolism reveals prognostic value of highly metabolically active circulating stromal cells in prostate cancer

2020 ◽  
Vol 6 (40) ◽  
pp. eaaz3849
Author(s):  
Francesca Rivello ◽  
Kinga Matuła ◽  
Aigars Piruska ◽  
Minke Smits ◽  
Niven Mehra ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Single Cell ◽  
Cancer Patients ◽  
Stromal Cells ◽  
Metastatic Prostate Cancer ◽  
Epithelial To Mesenchymal Transition ◽  
Therapy Response ◽  
Sequencing Analysis ◽  
Label Free ◽  
Mesenchymal Transition

Despite their important role in metastatic disease, no general method to detect circulating stromal cells (CStCs) exists. Here, we present the Metabolic Assay-Chip (MA-Chip) as a label-free, droplet-based microfluidic approach allowing single-cell extracellular pH measurement for the detection and isolation of highly metabolically active cells (hm-cells) from the tumor microenvironment. Single-cell mRNA-sequencing analysis of the hm-cells from metastatic prostate cancer patients revealed that approximately 10% were canonical EpCAM+ hm-CTCs, 3% were EpCAM− hm-CTCs with up-regulation of prostate-related genes, and 87% were hm-CStCs with profiles characteristic for cancer-associated fibroblasts, mesenchymal stem cells, and endothelial cells. Kaplan-Meier analysis shows that metastatic prostate cancer patients with more than five hm-cells have a significantly poorer survival probability than those with zero to five hm-cells. Thus, prevalence of hm-cells is a prognosticator of poor outcome in prostate cancer, and a potentially predictive and therapy response biomarker for agents cotargeting stromal components and preventing epithelial-to-mesenchymal transition.

scholarly journals Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sara Imboden ◽  
Xuanqing Liu ◽  
Brandon S. Lee ◽  
Marie C. Payne ◽  
Cho-Jui Hsieh ◽  
...  
Keyword(s):  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
High Throughput Screening ◽  
Imaging Method ◽  
Significant Heterogeneity ◽  
Label Free ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Integrative Assessment ◽  
Conventional Cell

AbstractMesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean $$r_s$$ r s = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.

Label-free multi-spectral deep UV imaging of prostate cancer tissue

2021 ◽  
Author(s):  
Soheil Soltani ◽  
Ashkan Ojaghi ◽  
Adeboye O. Osunkoya ◽  
Francisco E. Robles
Keyword(s):  
Prostate Cancer ◽  
Cancer Tissue ◽  
Label Free ◽  
Uv Imaging ◽  
Prostate Cancer Tissue ◽  
Deep Uv

scholarly journals Surface-Enhanced Raman Spectroscopy to Characterize Different Fractions of Extracellular Vesicles from Control and Prostate Cancer Patients

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 580
Author(s):  
Eric Boateng Osei ◽  
Liliia Paniushkina ◽  
Konrad Wilhelm ◽  
Jürgen Popp ◽  
Irina Nazarenko ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Raman Spectroscopy ◽  
Cancer Patients ◽  
Extracellular Vesicles ◽  
Protein Secondary Structure ◽  
Surface Enhanced Raman Spectroscopy ◽  
Cancer Diagnostics ◽  
Label Free ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Extracellular vesicles (EVs) are membrane-enclosed structures ranging in size from about 60 to 800 nm that are released by the cells into the extracellular space; they have attracted interest as easily available biomarkers for cancer diagnostics. In this study, EVs from plasma of control and prostate cancer patients were fractionated by differential centrifugation at 5000× g, 12,000× g and 120,000× g. The remaining supernatants were purified by ultrafiltration to produce EV-depleted free-circulating (fc) fractions. Spontaneous Raman and surface-enhanced Raman spectroscopy (SERS) at 785 nm excitation using silver nanoparticles (AgNPs) were employed as label-free techniques to collect fingerprint spectra and identify the fractions that best discriminate between control and cancer patients. SERS spectra from 10 µL droplets showed an enhanced Raman signature of EV-enriched fractions that were much more intense for cancer patients than controls. The Raman spectra of dehydrated pellets of EV-enriched fractions without AgNPs were dominated by spectral contributions of proteins and showed variations in S-S stretch, tryptophan and protein secondary structure bands between control and cancer fractions. We conclude that the AgNPs-mediated SERS effect strongly enhances Raman bands in EV-enriched fractions, and the fractions, EV12 and EV120 provide the best separation of cancer and control patients by Raman and SERS spectra.

scholarly journals Synthesizing VERDICT maps from standard diffusion mp-MRI data using GANs

2021 ◽  
Author(s):  
Eleni Chiou ◽  
Vanya Valindria ◽  
Francesco Giganti ◽  
Shonit Punwani ◽  
Iasonas Kokkinos ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mean Squared Error ◽  
Quantitative Estimation ◽  
Ground Truth ◽  
Quantitative Information ◽  
Strongly Correlated ◽  
Generative Adversarial Network ◽  
Mean Values ◽  
The Real ◽  
Adversarial Network

AbstractPurposeVERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from DWI data from multi-parametric (mp)-MRI which is widely used in clinical practice for prostate cancer diagnosis.MethodsWe use data from 67 patients who underwent both mp-MRI and VERDICT MRI. We compute the ground truth VERDICT maps from VERDICT MRI and we propose a generative adversarial network (GAN)-based approach to synthesize VERDICT maps from mp-MRI DWI data. We use correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps compared to the real ones.ResultsQuantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps from DWI from mp-MRI acquisitions.ConclusionRealistic VERDICT maps can be generated using DWI from standard mp-MRI. The synthetic maps preserve important quantitative information enabling the exploitation of VERDICT MRI for precise prostate cancer characterization with a single mp-MRI acquisition.

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