| Autor: |
Schoenaker IJH; Oncology Center Isala, Isala Dokter van Heesweg 2, 8025 AB Zwolle, The Netherlands.; Department of Health Science, University Medical Center Groningen, Section of Nursing Research, Hanzeplein 1, 9713 GZ Groningen, The Netherlands., Pennings A; Department of Surgery, Maastricht University Medical Center, P. Debyelaan 25, GROW School for Oncology and Reproduction, Maastricht University, 6229 HX Maastricht, The Netherlands., van Westreenen HL; Department of Surgery, Isala, Dokter van Heesweg 2, 8025 AB Zwolle, The Netherlands., Finnema EJ; Department of Health Science, University Medical Center Groningen, Section of Nursing Research, Hanzeplein 1, 9713 GZ Groningen, The Netherlands., Brohet RM; Department of Epidemiology & Statistics, Isala, Dokter van Heesweg 2, 8025 AB Zwolle, The Netherlands., Hanevelt J; Department of Gastroenterology and Hepatology, Isala, Dokter van Heesweg 2, 8025 AB Zwolle, The Netherlands., de Vos Tot Nederveen Cappel WH; Department of Gastroenterology and Hepatology, Isala, Dokter van Heesweg 2, 8025 AB Zwolle, The Netherlands., Melenhorst J; Department of Surgery, Maastricht University Medical Center, P. Debyelaan 25, GROW School for Oncology and Reproduction, Maastricht University, 6229 HX Maastricht, The Netherlands. |
| Abstrakt: |
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. |
