ArticlePloS one2026
Diagnostic performance of eNose technology in detecting colorectal cancer recurrence: A prospective evaluation.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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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.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAfter curative treatment for colorectal cancer (CRC), there is a 15% risk of recurrence. Early detection of an asymptomatic recurrence may lead to curative treatment options. To date, follow-up strategies do not have optimal sensitivity and specificity. In this prospective study, we aimed to assess the diagnostic performance of eNose technology to detect recurrent CRC following curative surgery. MATERIALS AND
methodsA prospective evaluation study was performed to investigate whether eNose can discriminate patients with recurrent CRC following curative resection from patients without recurrent CRC based on VOC patterns during follow-up. The primary outcome measure is the diagnostic accuracy of eNose for detecting recurrence in CRC patients. With machine learning, a model was developed, and several performance metrics were used to evaluate the diagnostic performance of the eNose model.
resultsA total of 406 patients who underwent curative resection for CRC between 2018-2023, were included in the study. VOC analysis was used to detect recurrent CRC during follow-up. Although the eNose model demonstrated promising results in the train set with an AUC of 0.90 (95% CI:0.84-0.96), the corresponding accuracy of 0.56 was low. Moreover, with a corresponding sensitivity of 0.52, accuracy of 0.51, and AUC of 0.51 (95% CI:0.38-0.64), the performances in the test set, declined.
conclusioneNose technology is not able to accurately detect recurrent CRC after curative resection of the primary tumour. Larger studies are needed before clinical implementation can be realized, while the lack of reproducibility must be addressed.
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