scholarly journals The role of exosomes in liquid biopsy for cancer diagnosis and prognosis prediction

2020 ◽  
Author(s):  
Shiyu Li ◽  
Ming Yi ◽  
Bing Dong ◽  
Ximin Tan ◽  
Suxia Luo ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Liquid Biopsy ◽  
Prognosis Prediction ◽  
Diagnosis And Prognosis ◽  
Cancer Diagnosis And Prognosis

scholarly journals Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kezhong Chen ◽  
Jianlong Sun ◽  
Heng Zhao ◽  
Ruijingfang Jiang ◽  
Jianchao Zheng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Liquid Biopsy ◽  
Non Invasive ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Cancer Diagnosis And Prognosis

scholarly journals Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Ahsan Bin Tufail ◽  
Yong-Kui Ma ◽  
Mohammed K. A. Kaabar ◽  
Francisco Martínez ◽  
A. R. Junejo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
Computer Aided Design ◽  
Feature Recognition ◽  
Cancer Prognosis ◽  
Generic Model ◽  
Prognosis Prediction ◽  
Cad System ◽  
Diagnosis And Prognosis ◽  
Cancer Diagnosis And Prognosis

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

scholarly journals Biomechanical Properties of Cancer Cells

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 887
Author(s):  
Gaël Runel ◽  
Noémie Lopez-Ramirez ◽  
Julien Chlasta ◽  
Ingrid Masse
Keyword(s):  
Cancer Cells ◽  
Cancer Diagnosis ◽  
Crucial Role ◽  
Biomechanical Properties ◽  
Supplementary Information ◽  
Cancer Cell Growth ◽  
Diagnosis And Prognosis ◽  
Surrounding Environment ◽  
Surrounding Extracellular Matrix

Since the crucial role of the microenvironment has been highlighted, many studies have been focused on the role of biomechanics in cancer cell growth and the invasion of the surrounding environment. Despite the search in recent years for molecular biomarkers to try to classify and stratify cancers, much effort needs to be made to take account of morphological and nanomechanical parameters that could provide supplementary information concerning tissue complexity adaptation during cancer development. The biomechanical properties of cancer cells and their surrounding extracellular matrix have actually been proposed as promising biomarkers for cancer diagnosis and prognosis. The present review first describes the main methods used to study the mechanical properties of cancer cells. Then, we address the nanomechanical description of cultured cancer cells and the crucial role of the cytoskeleton for biomechanics linked with cell morphology. Finally, we depict how studying interaction of tumor cells with their surrounding microenvironment is crucial to integrating biomechanical properties in our understanding of tumor growth and local invasion.

Ultra-sensitive MicroRNA-21 detection based on multiple cascaded strand displacement amplification and CRISPR/Cpf1 (MC-SDA/CRISPR/Cpf1)

2021 ◽  
Author(s):  
Xiaolong Chen ◽  
Yuanyi Deng ◽  
Gaihua Cao ◽  
Yifan Xiong ◽  
Danqun Huo ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Analytical Method ◽  
Strand Displacement ◽  
Strand Displacement Amplification ◽  
Diagnosis And Prognosis ◽  
Potential Biomarker ◽  
Cancer Diagnosis And Prognosis

MicroRNA-21 (miR-21) has been considered as a potential biomarker for cancer diagnosis and prognosis due to its highly expressed in tumors. Here, an analytical method which integrates the multiple cascaded...

Emerging technologies and commercial products in Exosome-Based Cancer Diagnosis and Prognosis

2021 ◽  
pp. 113176
Author(s):  
Mehdi Mohammadi ◽  
Hossein Zargartalebi ◽  
Razieh Salahandish ◽  
Raied Aburashed ◽  
Kar Wey Yong ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Emerging Technologies ◽  
Commercial Products ◽  
Diagnosis And Prognosis ◽  
Cancer Diagnosis And Prognosis

scholarly journals Potential applications of MEG3 in cancer diagnosis and prognosis

Oncotarget ◽  
2017 ◽  
Vol 8 (42) ◽  
pp. 73282-73295 ◽  
Author(s):  
Yuqing He ◽  
Yanhong Luo ◽  
Biyu Liang ◽  
Lei Ye ◽  
Guangxing Lu ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Diagnosis And Prognosis ◽  
Potential Applications ◽  
Cancer Diagnosis And Prognosis

scholarly journals Agreement between patients’ and radiation oncologists’ cancer diagnosis and prognosis perceptions: A cross sectional study in Japan

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198437 ◽  
Author(s):  
Lisa Jane Mackenzie ◽  
Mariko Leanne Carey ◽  
Eiji Suzuki ◽  
Robert William Sanson-Fisher ◽  
Hiromi Asada ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Diagnosis And Prognosis ◽  
Radiation Oncologists ◽  
Cancer Diagnosis And Prognosis
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