A I G Buma, M B Muntinghe-Wagenaar, V van der Noort, R de Vries, M M F Schuurbiers, P J Sterk, S P M Schipper, J Meurs, S M Cristescu, T J N Hiltermann, M M van den Heuvel
{"title":"呼气电子鼻分析肺癌检测:一项多中心前瞻性外部验证研究。","authors":"A I G Buma, M B Muntinghe-Wagenaar, V van der Noort, R de Vries, M M F Schuurbiers, P J Sterk, S P M Schipper, J Meurs, S M Cristescu, T J N Hiltermann, M M van den Heuvel","doi":"10.1016/j.annonc.2025.03.013","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in chronic obstructive pulmonary disease (COPD) patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.</p><p><strong>Patients and methods: </strong>This multicentre prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in the Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used.</p><p><strong>Results: </strong>Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (CI) 0.85-0.99] in COPD patients (n = 98/116; 84%) and 0.80 (95% CI 0.75-0.85) in all participants (n = 216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n = 72/121; 60%) with an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics.</p><p><strong>Conclusion: </strong>This multicentre prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic centre, and clinical characteristics.</p>","PeriodicalId":8000,"journal":{"name":"Annals of Oncology","volume":" ","pages":""},"PeriodicalIF":56.7000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lung cancer detection by electronic nose analysis of exhaled breath: a multicentre prospective external validation study.\",\"authors\":\"A I G Buma, M B Muntinghe-Wagenaar, V van der Noort, R de Vries, M M F Schuurbiers, P J Sterk, S P M Schipper, J Meurs, S M Cristescu, T J N Hiltermann, M M van den Heuvel\",\"doi\":\"10.1016/j.annonc.2025.03.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in chronic obstructive pulmonary disease (COPD) patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.</p><p><strong>Patients and methods: </strong>This multicentre prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in the Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used.</p><p><strong>Results: </strong>Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (CI) 0.85-0.99] in COPD patients (n = 98/116; 84%) and 0.80 (95% CI 0.75-0.85) in all participants (n = 216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n = 72/121; 60%) with an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics.</p><p><strong>Conclusion: </strong>This multicentre prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic centre, and clinical characteristics.</p>\",\"PeriodicalId\":8000,\"journal\":{\"name\":\"Annals of Oncology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":56.7000,\"publicationDate\":\"2025-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.annonc.2025.03.013\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.annonc.2025.03.013","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Lung cancer detection by electronic nose analysis of exhaled breath: a multicentre prospective external validation study.
Background: Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in chronic obstructive pulmonary disease (COPD) patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.
Patients and methods: This multicentre prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in the Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used.
Results: Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (CI) 0.85-0.99] in COPD patients (n = 98/116; 84%) and 0.80 (95% CI 0.75-0.85) in all participants (n = 216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n = 72/121; 60%) with an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics.
Conclusion: This multicentre prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic centre, and clinical characteristics.
期刊介绍:
Annals of Oncology, the official journal of the European Society for Medical Oncology and the Japanese Society of Medical Oncology, offers rapid and efficient peer-reviewed publications on innovative cancer treatments and translational research in oncology and precision medicine.
The journal primarily focuses on areas such as systemic anticancer therapy, with a specific emphasis on molecular targeted agents and new immune therapies. We also welcome randomized trials, including negative results, as well as top-level guidelines. Additionally, we encourage submissions in emerging fields that are crucial to personalized medicine, such as molecular pathology, bioinformatics, modern statistics, and biotechnologies. Manuscripts related to radiotherapy, surgery, and pediatrics will be considered if they demonstrate a clear interaction with any of the aforementioned fields or if they present groundbreaking findings.
Our international editorial board comprises renowned experts who are leaders in their respective fields. Through Annals of Oncology, we strive to provide the most effective communication on the dynamic and ever-evolving global oncology landscape.