Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study
ABSTRACT Background Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile org...
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Wiley
2025-01-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70545 |
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| author | Guangyu Lu Zhixia Su Xiaoping Yu Yuhang He Taining Sha Kai Yan Hong Guo Yujian Tao Liting Liao Yanyan Zhang Guotao Lu Weijuan Gong |
| author_facet | Guangyu Lu Zhixia Su Xiaoping Yu Yuhang He Taining Sha Kai Yan Hong Guo Yujian Tao Liting Liao Yanyan Zhang Guotao Lu Weijuan Gong |
| author_sort | Guangyu Lu |
| collection | DOAJ |
| description | ABSTRACT Background Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored. Methods Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. Results A total of 267 participants were enrolled, including 210 with low‐risk and 57 with moderate‐risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate‐risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611–0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974–1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data. Conclusions The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability. Trial Registration: Number ChiCTR2400081283 |
| format | Article |
| id | doaj-art-12778dfa1a7d4d7bb7a319b5bc0bd4c7 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-12778dfa1a7d4d7bb7a319b5bc0bd4c72025-01-13T13:22:38ZengWileyCancer Medicine2045-76342025-01-01141n/an/a10.1002/cam4.70545Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational StudyGuangyu Lu0Zhixia Su1Xiaoping Yu2Yuhang He3Taining Sha4Kai Yan5Hong Guo6Yujian Tao7Liting Liao8Yanyan Zhang9Guotao Lu10Weijuan Gong11Department of Health Management Center Affiliated Hospital of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaSchool of Public Health Medical College of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaDepartment of Health Management Center Affiliated Hospital of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaSchool of Nursing Medical College of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaSchool of Public Health Medical College of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaSchool of Public Health Medical College of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaDepartment of Thoracic Surgery Affiliated Hospital of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaDepartment of Respiratory and Critical Care Medicine Affiliated Hospital of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaDepartment of Basic Medicine Medical College of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaTesting Center of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaYangzhou Key Laboratory of Pancreatic Disease Institute of Digestive Diseases, Affiliated Hospital of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaDepartment of Health Management Center Affiliated Hospital of Yangzhou University, Yangzhou University Yangzhou Jiangsu ChinaABSTRACT Background Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored. Methods Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. Results A total of 267 participants were enrolled, including 210 with low‐risk and 57 with moderate‐risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate‐risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611–0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974–1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data. Conclusions The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability. Trial Registration: Number ChiCTR2400081283https://doi.org/10.1002/cam4.70545breath biomarkersmalignancy riskpulmonary nodulesvolatile organic compounds |
| spellingShingle | Guangyu Lu Zhixia Su Xiaoping Yu Yuhang He Taining Sha Kai Yan Hong Guo Yujian Tao Liting Liao Yanyan Zhang Guotao Lu Weijuan Gong Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study Cancer Medicine breath biomarkers malignancy risk pulmonary nodules volatile organic compounds |
| title | Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study |
| title_full | Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study |
| title_fullStr | Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study |
| title_full_unstemmed | Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study |
| title_short | Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study |
| title_sort | differentiating pulmonary nodule malignancy using exhaled volatile organic compounds a prospective observational study |
| topic | breath biomarkers malignancy risk pulmonary nodules volatile organic compounds |
| url | https://doi.org/10.1002/cam4.70545 |
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