SynthesisThe oncologist2026
eNose technologies in the detection of cancer: a systematic review and meta-analysis.
Synthesis in The oncologist, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundIn 2004, researchers in the United Kingdom were able to train six dogs to distinguish between urine samples from bladder cancer patients and healthy controls using smell, achieving a detection rate of 41% (95% confidence interval [CI]: 23-58%), far above the 14% expected by chance. After numerous subsequent studies validated that the odor of breath and urine samples could be used by dogs to detect cancer, researchers pivoted to electronic noses (eNoses), sensor-based systems that mimic the sense of smell using arrays of chemical detectors. In this study, we review the potential efficacy of eNoses in the detection of selected cancer types in human biological samples.
methodsWe identified and performed a meta-analysis on 37 studies of eNose technology, comprising 1 365 cancer patients and 2 249 control subjects. We calculated the pooled sensitivity and specificity stratified by cancer type, sample type, and sensor type. Meta-regressions were conducted on these variables as well as the number of sensors used in the sensor array.
resultsAll six cancer types analyzed-breast, colorectal, gastric, lung, ovarian, and prostate-achieved pooled sensitivities and specificities above 70%, with most around 85%. The overall pooled sensitivity was 85.9% (95% CI: 82.3-88.9%) and specificity was 83.6% (95% CI: 78.6-87.7%). Meta-regression revealed that the number of sensors in the sensor arrays, up to 15 sensors, was predictive of sensitivity with PFDR < 0.001.
conclusionThis analysis found that eNoses constitute a promising tool in the early detection of cancer. However, more research is necessary before it can be introduced into clinical settings.
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