Cancer Detection Rate
Cancer Detection Rate - 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.
06:39
A High Performance Impedance-based Platform for Evaporation Rate Detection
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Cited by 1 •
2016
This paper presents an impedance-based apparatus for evaporation rate detection of solutions. It offers clear advantages over a conventional weight loss approach: a fast response, high-sensitivity detection, a small sample requirement, multiple sample measurements, and easy disassembly for cleaning and reuse purposes.
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.
FAQs
Disease prevalence can raise or lower the observed rate even when the diagnostic approach is unchanged. If the assessed population contains more people with cancer, more cases may be detected; a lower-prevalence population may yield fewer detected cases. Therefore, comparisons should account for population context rather than treating rates as universal test properties.
Sensitivity influences the observed rate, but rate comparisons should not be interpreted as sensitivity measurements alone. A lower rate may reflect differences in prevalence, participation, tumor type, or stage rather than only the detection approach. Considering these factors helps clinicians and researchers interpret whether a result reflects the test, the population, or both.
Tumor type and stage can change how often cancer is identified. Different tumor types may be represented unevenly across assessed populations, while stage provides clinical context for when disease is recognized. Reporting the rate with these characteristics helps prevent unlike populations from being compared as though they had the same detection circumstances.
False-positive findings provide an essential counterpoint to cancer detection rate. A method may identify more suspected cancers, yet some findings may not represent cancer. Reviewing both measures helps evaluate the overall diagnostic pathway and prevents a higher detection rate from being interpreted as an uncomplicated improvement in screening performance.
A calculation should state the assessed population, the time period, the number of detected cancer cases, and the number of people assessed. Keeping these elements explicit makes the rate easier to interpret and compare. Researchers can then examine whether differences arise from population composition, participation, or the approach being evaluated.
Imaging, pathology, molecular assays, and other detection strategies can be evaluated by comparing their cancer detection rates within clearly described populations and time periods. The comparison becomes more informative when researchers also review false-positive findings and survival outcomes. This broader assessment links case identification with false findings and subsequent clinical outcomes.
In medicine, differences in cancer detection rate can reveal where people are not entering screening or where detected cases are not progressing through follow-up. Examining rates alongside participation and follow-up information helps identify access gaps and weaknesses in the diagnostic pathway. This supports more informed clinical and public health decisions.