1. Is an Electronic Nose Able to Predict Clinical Response following Neoadjuvant Treatment of Rectal Cancer? A Prospective Pilot Study.
- Author
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Schoenaker, Ivonne J. H., Pennings, Alexander, van Westreenen, Henderik L., Finnema, Evelyn J., Brohet, Richard M., Hanevelt, Julia, de Vos Tot Nederveen Cappel, Wouter H., and Melenhorst, Jarno
- Subjects
ELECTRONIC noses ,NEOADJUVANT chemotherapy ,MACHINE learning ,VOLATILE organic compounds ,RECEIVER operating characteristic curves ,RECTAL cancer - Abstract
Introduction: A watch-and-wait strategy for patients with rectal cancer who achieve a clinical complete response after neoadjuvant (chemo) radiotherapy is a valuable alternative to rectal resection. In this pilot study, we explored the use of an electronic nose to predict response to neoadjuvant therapy by analyzing breath-derived volatile organic compounds. Materials and Methods: A pilot study was performed between 2020 and 2022 on patients diagnosed with intermediate- or high-risk rectal cancer who were scheduled for neoadjuvant therapy. Breath samples were collected before and after (chemo) radiotherapy. A machine-learning model was developed to predict clinical response using curatively treated rectal cancer patients as controls. Results: For developing the machine-learning model, a total of 99 patients were included: 45 patients with rectal cancer and 54 controls. In the training set, the model successfully discriminated between patients with and without rectal cancer, with a sensitivity and specificity of 0.80 and 0.65, respectively, and an accuracy of 0.72. In the test set, the model predicted partial or (near) complete response with a sensitivity and specificity of 0.64 and 0.47, respectively, and an accuracy of 0.58. The AUC of the ROC curve was 0.63. Conclusions: The prediction model developed in this pilot study lacks the ability to accurately differentiate between partial and (near) complete responders with an electronic nose. Machine-learning studies demand a substantial number of patients and operate in a rapidly evolving field. Therefore, the prevalence of disease and duration of a study are crucial considerations for future research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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