Cancer Detection
Cancer Detection - Related Videos
05:17
Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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2025
This protocol describes an automated, ISO15189-accredited next-generation sequencing workflow for detecting targetable genomic alterations in non-small cell lung cancer (NSCLC) formalin-fixed paraffin-embedded tissues.
06:08
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Cited by 69 •
2011
We demonstrated that malignant pigmented lesions with increased metabolic activity generate quantifiable amounts of heat and the measurement of the transient thermal response of the skin to a cooling excitation allows quantitative identification of melanoma and other skin cancers (vs. non-proliferative nevi) at an early stage of the disease.
08:45
Cerenkov Luminescence Imaging (CLI) for Cancer Therapy Monitoring
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Cited by 8 •
2012
Use of Cerenkov Luminescence Imaging (CLI) for monitoring preclinical cancer treatment is described here. This method takes advantage of Cerenkov Radiation (CR) and optical imaging (OI) to visualize radiolabeled probes and thus provides an alternative to PET in preclinical therapeutic monitoring and drug screening.
08:09
Tubal Cytology of the Fallopian Tube as a Promising Tool for Ovarian Cancer Early Detection
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Cited by 11 •
2017
We explored a tubal cytologic method by sampling the fallopian tube directly post-surgical excision as a tool of ovarian cancer early detection. Here, we present a protocol to collect fallopian tube cells from freshly received surgical specimens.
09:57
Harnessing the DNA Dye-triggered Side Population Phenotype to Detect and Purify Cancer Stem Cells from Biological Samples
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Cited by 4 •
2017
Methods allowing the characterization and isolation of stem cell populations from biological samples are critical for the advance of stem cell-targeted treatments in cancer and beyond. Here, we provide a detailed protocol for cancer stem cell isolation using the dye-triggered side population phenotype.
FAQs
These recognition elements bind selected molecular biomarkers, creating a specific interaction that can be converted into an optical or electrical readout. The measured signal provides evidence about the presence of a target in a sample, while the choice of biomarker and detection format influences what the system can measure. This mechanism underlies biosensor-based approaches to cancer research and care.
Engineering systems can measure molecular biomarkers, cell morphology, tissue structure, or physiological signals. Molecular measurements focus on target-associated substances, morphology on cellular appearance, structure on organization within tissue, and physiological signals on body function. This range allows developers to select a measurement type suited to the biological level they want to examine.
Image-processing and machine-learning methods analyze patterns in image data so systems can distinguish abnormal patterns from healthy variation. This is especially relevant when cancer-related changes appear in cell morphology or tissue structure. Adding computational analysis to imaging can support consistent interpretation of measurements, extending engineering beyond signal capture to the evaluation of patterns relevant to research and clinical care.
Imaging systems can examine cell morphology and tissue structure, whereas biosensors can translate molecular recognition into optical or electrical signals. The two approaches therefore observe different features of potential disease-related change. Choosing between them depends on whether the investigation requires structural or visual information, molecular measurements, or a combination of these engineering readouts.
An engineered workflow connects a biological source, a measurement method, and an analysis step. The source may be cells, tissues, or bodily fluids; the measurement may use biomarkers, imaging, biosensors, or physiological signals; and computational analysis may evaluate the resulting patterns. Linking these stages turns biological observations into information that can support evaluation and further clinical attention.
They can support screening, diagnosis, treatment monitoring, and risk assessment, so the appropriate design depends on the decision or stage being addressed. Because these applications address different clinical questions, engineers may select molecular, structural, morphological, or physiological measurements according to the information required. The same broad engineering field can therefore serve both research and clinical purposes.
Development efforts emphasize faster, more sensitive, and less invasive tools. These priorities apply across imaging systems, biosensors, and computational analysis, and they support both cancer research and clinical care. Faster operation can improve timeliness, higher sensitivity can strengthen the ability to measure target signals, and reduced invasiveness can expand the practicality of evaluating cells, tissues, or bodily fluids.