Cancer Detection and Diagnosis

A Handbook of Emerging Technologies
1. Auflage 2025
ISBN: 978-1-032-57701-2
Verlag: Taylor & Francis

Buch, Englisch, 580 Seiten, Format (B × H): 210 mm x 280 mm, Gewicht: 1230 g

Emerging technologies for cancer detection and diagnosis are providing more and more advance warning of pathologies of clinical significance. Research devoted to cancers are revealing new ways of finding and treating these complex diseases. This volume reviews a broad array of new technologies for cancer detection and diagnosis. While there are several clinical books describing cancer diagnosis, and general molecular analytical technologies, these books are not focused on cancer detection and diagnosis. The aim of this book is to describe emerging cancer detection and diagnosis technologies.

Key Features

- Presents myriad new experimental cancer detection technologies

- Describes technology so the reader may conduct similar analyses

- Outlines clinical applications of technology for specific cancer and summarizes results

- Discusses pitfalls and limitations, future trends and potential technological developments

Zielgruppe

Academic, Postgraduate, and Professional Reference

Autoren/Hrsg.

Ossandon, Miguel

Prickril, Ben

Rasooly, Avraham

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie Medizinische Biotechnologie
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

Near-infrared macroscopic and mesoscopic fluorescence lifetime FRET imaging to measure intra-tumor heterogeneity of antibody-target engagement. Combined Reflectance Confocal Microscopy- Optical Coherence Tomography for Skin Cancer Detection and Therapy GuidanceIn Vivo Confocal Laser Endomicroscopy: An Imaging Biomarker for Risk Stratification of Precancerous Pancreatic Cystic Lesions. Portable confocal microscopy for aiding diagnosis and treatment of skin cancer
Quantitative fluorescence imaging, light-triggering and monitoring of chemodrug release from liposomes in vivo by a mesoscopic-scale theranostic endoscope.Fluorescence Imitating Brightfield Imaging (FIBI): A simple slide-free microscopy approach.Fluorescently-labeled tyrosine kinase inhibitors for intracellular protein target imaging.Real-time Cancer Metabolism Detection Using a Nanocoil Integrated Hyperpolarized Micromagnetic Resonance Spectrometer. Detection of TMEM doorways and their activity required for metastasis. Reading the Reader: Utilizing Eye-Movements and Machine Learning to Enhance Accuracy. During Diagnostic Visual Search.Optical Imaging Technology for In Vivo Tumor Detection.Adapting Image Foundational Model to Identify Tumor Budding from H&E Images in Colorectal Cancer Diagnostics.Quantitative Phase Imaging for Assessing Tumor Cell Adaptability.An Exo-PROS Biosensor Simultaneously Detects Tumor-Derived Exosomal Protein-MicroRNA Pairs for Lung and Breast Cancer Diagnosis.Epigenetic Tools for Guiding Low Dose Computerized Tomography (LDCT) Screening Decision Making.Multi-View Models for Colorectal Polyps Detection in CT Colonography.Hybrid Multi-dimensional MRI of Prostate Cancer.Detection and Characterization of Brain Metastases Using Quantitative Chemical Exchange Saturation Transfer MRFingerprinting (CEST-MRF).Narrow-Beam CT: A Solution to the Limitations in Breast Cancer Screening.Broadband Coherent Anti-Stokes Raman Scattering Microscopy for Metabolic and Phenotypic Imaging in Cancer.
Spatial multiplexed immunofluorescence imaging

Über Autor(innen)

Miguel Ossandon has a dual background in clinical laboratory and computer science. He started working in cancer research at the Lombardi Cancer Center at Georgetown University where he also began his undergraduate training in computer science. He has been working for the National Cancer Institute since 2007. Miguel received his master’s degree at the George Washington University and PhD in computer science at the University of Maryland Baltimore County. As a program director in the Diagnostic Biomarkers and Technology Branch, he manages a grant portfolio related to computational modeling and machine learning approaches for cancer diagnosis, digital image processing/analysis, and microfluidic and circulating tumor cell technology.

Ben Prickril is a former US government official working at the US National Institutes of Health, National Cancer Institute. He has a background in medically related chemistry, microbiology, immunology, patenting and intellectual property, and global health. International research development includes experience in France, Turkey, Ukraine, Czech Republic and Burkina Faso. He received his PhD from the University of Georgia.

Avraham Rasooly is in the Division of Cancer Treatment and Diagnosis, National Cancer Institute. He has been responsible for developing research programs on new approaches for cancer therapy, including technologies for microbial-based cancer therapy. He received his PhD from Michigan State University.