Abstract PO-012: AI and digital pathology based on nucleus morphology for diagnosis, prognosis, and morphological-gene isolation Li Fraumeni as a model

Li-Fraumeni syndrome is a familial cancer predisposition syndrome associated with germline mutation of the tumor suppressor gene 53, which encodes the tumor suppressor p53 protein. Affected patients are predisposed to an increased risk of cancer development, including soft-tissue sarcomas, breast cancer, brain tumors, and adrenocortical carcinoma, among other malignancies. The tumor suppressor gene TP53 plays an important, complex role in regulating the cell cycle, collaborating with transcription factors and other proteins. The disruption of appropriate cell cycle regulation by mutated TP53 is considered to be the cause of tumorigenesis in Li-Fraumeni syndrome. Appropriate surveillance, predominantly by using MR imaging, is used for early malignancy screening in an effort to improve the survival rate among individuals who are affected. Patients with Li-Fraumeni syndrome are also at increased risk for neoplasm development after radiation exposure, and, therefore, avoiding unnecessary radiation in both the diagnostic and therapeutic settings is paramount. Here, we review the epidemiology, genetics, imaging findings, and the current standard surveillance protocol for Li-Fraumeni syndrome from the National Comprehensive Cancer Network as well as potential treatment options.Learning Objective: Describe the cause of second primary malignancy among patients with Li-Fraumeni syndrome.

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Exosomes: Structure, Biogenesis, Types and Application in Diagnosis and Gene and Drug Delivery

Current Gene Therapy ◽

10.2174/1566523220999200731011702 ◽

2020 ◽

Vol 20 (3) ◽

pp. 195-206 ◽

Cited By ~ 1

Author(s):

Shriya Agarwal ◽

Vinayak Agarwal ◽

Mugdha Agarwal ◽

Manisha Singh

Keyword(s):

Drug Delivery ◽

Gene Therapy ◽

Immune Responses ◽

Biological Membrane ◽

Gene Isolation ◽

Delivery Vehicle ◽

Scientific Communities ◽

Therapeutic Regime ◽

Targeted Gene Therapy ◽

Delivery Mechanism

Abstract: In recent times, several approaches for targeted gene therapy (GT) had been studied. However, the emergence of extracellular vesicles (EVs) as a shuttle carrying genetic information between cells has gained a lot of interest in scientific communities. Owing to their higher capabilities in dealing with short sequences of nucleic acid (mRNA, miRNA), proteins, recombinant proteins, exosomes, the most popular form of EVs are viewed as reliable biological therapeutic conveyers. They have natural access through every biological membrane and can be employed for site-specific and efficient drug delivery without eliciting any immune responses hence, qualifying as an ideal delivery vehicle. Also, there are many research studies conducted in the last few decades on using exosome-mediated gene therapy into developing an effective therapy with the concept of a higher degree of precision in gene isolation, purification and delivery mechanism loading, delivery and targeting protocols. This review discusses several facets that contribute towards developing an efficient therapeutic regime for gene therapy, highlighting limitations and drawbacks associated with current GT and suggested therapeutic regimes.

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A Digital Pathology Solution to Resolve the Tissue Floater Conundrum

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2020-0034-oa ◽

2020 ◽

Author(s):

Liron Pantanowitz ◽

Pamela Michelow ◽

Scott Hazelhurst ◽

Shivam Kalra ◽

Charles Choi ◽

...

Keyword(s):

Digital Pathology ◽

Primary Diagnosis ◽

Image Search ◽

Molecular Tests ◽

Tissue Identification ◽

Digital Database ◽

Glass Slides ◽

Search Tool ◽

Hematoxylin And Eosin ◽

Whole Slide Images

Context.— Pathologists may encounter extraneous pieces of tissue (tissue floaters) on glass slides because of specimen cross-contamination. Troubleshooting this problem, including performing molecular tests for tissue identification if available, is time consuming and often does not satisfactorily resolve the problem. Objective.— To demonstrate the feasibility of using an image search tool to resolve the tissue floater conundrum. Design.— A glass slide was produced containing 2 separate hematoxylin and eosin (H&E)-stained tissue floaters. This fabricated slide was digitized along with the 2 slides containing the original tumors used to create these floaters. These slides were then embedded into a dataset of 2325 whole slide images comprising a wide variety of H&E stained diagnostic entities. Digital slides were broken up into patches and the patch features converted into barcodes for indexing and easy retrieval. A deep learning-based image search tool was employed to extract features from patches via barcodes, hence enabling image matching to each tissue floater. Results.— There was a very high likelihood of finding a correct tumor match for the queried tissue floater when searching the digital database. Search results repeatedly yielded a correct match within the top 3 retrieved images. The retrieval accuracy improved when greater proportions of the floater were selected. The time to run a search was completed within several milliseconds. Conclusions.— Using an image search tool offers pathologists an additional method to rapidly resolve the tissue floater conundrum, especially for those laboratories that have transitioned to going fully digital for primary diagnosis.

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Spitzoid Melanoma in a Child with Li-Fraumeni Syndrome

Pediatric and Developmental Pathology ◽

10.2350/13-09-1380-cr.1 ◽

2014 ◽

Vol 17 (1) ◽

pp. 64-69 ◽

Cited By ~ 7

Author(s):

Ramya Kollipara ◽

Linda D. Cooley ◽

Kimberly A. Horii ◽

Maxine L. Hetherington ◽

Philip E. LeBoit ◽

...

Keyword(s):

Li Fraumeni Syndrome ◽

Li Fraumeni

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Utility of interim blood tests for cancer screening in Li-Fraumeni syndrome

Familial Cancer ◽

10.1007/s10689-021-00265-x ◽

2021 ◽

Author(s):

Leatrisse Oba ◽

Ana F. Best ◽

Phuong L. Mai ◽

Maria Isabel Achatz ◽

Paul S. Albert ◽

...

Keyword(s):

Cancer Screening ◽

Li Fraumeni Syndrome ◽

Blood Tests ◽

Li Fraumeni

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Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Scientific Reports ◽

10.1038/s41598-021-88236-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Christian Crouzet ◽

Gwangjin Jeong ◽

Rachel H. Chae ◽

Krystal T. LoPresti ◽

Cody E. Dunn ◽

...

Keyword(s):

Deep Learning ◽

Prussian Blue ◽

Processing Speed ◽

Digital Pathology ◽

Ground Truth ◽

Individual Variability ◽

Rgb Images ◽

Cerebral Microhemorrhages ◽

Phasor Analysis ◽

Better Than

AbstractCerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

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Quality control stress test for deep learning-based diagnostic model in digital pathology

Modern Pathology ◽

10.1038/s41379-021-00859-x ◽

2021 ◽

Author(s):

Birgid Schömig-Markiefka ◽

Alexey Pryalukhin ◽

Wolfgang Hulla ◽

Andrey Bychkov ◽

Junya Fukuoka ◽

...

Keyword(s):

Deep Learning ◽

Cancer Detection ◽

Stress Testing ◽

Computational Analysis ◽

Stress Test ◽

Digital Pathology ◽

Model Performance ◽

Diagnostic Model ◽

Model Accuracy ◽

Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

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Genetic Perturbation of Pyruvate Dehydrogenase Kinase 1 Modulates Growth, Angiogenesis and Metabolic Pathways in Ovarian Cancer Xenografts

Cells ◽

10.3390/cells10020325 ◽

2021 ◽

Vol 10 (2) ◽

pp. 325

Author(s):

Carolina Venturoli ◽

Ilaria Piga ◽

Matteo Curtarello ◽

Martina Verza ◽

Giovanni Esposito ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Cells ◽

Pyruvate Dehydrogenase ◽

Metabolic Pathways ◽

Negative Impact ◽

Digital Pathology ◽

Pyruvate Dehydrogenase Kinase ◽

Ovarian Cancer Cells ◽

Pyruvate Dehydrogenase Kinase 1

Pyruvate dehydrogenase kinase 1 (PDK1) blockade triggers are well characterized in vitro metabolic alterations in cancer cells, including reduced glycolysis and increased glucose oxidation. Here, by gene expression profiling and digital pathology-mediated quantification of in situ markers in tumors, we investigated effects of PDK1 silencing on growth, angiogenesis and metabolic features of tumor xenografts formed by highly glycolytic OC316 and OVCAR3 ovarian cancer cells. Notably, at variance with the moderate antiproliferative effects observed in vitro, we found a dramatic negative impact of PDK1 silencing on tumor growth. These findings were associated with reduced angiogenesis and increased necrosis in the OC316 and OVCAR3 tumor models, respectively. Analysis of viable tumor areas uncovered increased proliferation as well as increased apoptosis in PDK1-silenced OVCAR3 tumors. Moreover, RNA profiling disclosed increased glucose catabolic pathways—comprising both oxidative phosphorylation and glycolysis—in PDK1-silenced OVCAR3 tumors, in line with the high mitotic activity detected in the viable rim of these tumors. Altogether, our findings add new evidence in support of a link between tumor metabolism and angiogenesis and remark on the importance of investigating net effects of modulations of metabolic pathways in the context of the tumor microenvironment.

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