Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model
Objective·To verify the performance and explore the clinical application value of a multi-modal pulmonary nodule diagnosis model combined with metabolic fingerprints, protein biomarker CEA and Image-AI via random forest (MPI-RF). Methods·This study enrolled 289 patients with pulmonary nodules who we...
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Editorial Office of Journal of Shanghai Jiao Tong University (Medical Science)
2024-08-01
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| Series: | Shanghai Jiaotong Daxue xuebao. Yixue ban |
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| author | XU Wanxing WANG Lin GUO Qiaomei WANG Xueqing LOU Jiatao |
| author_facet | XU Wanxing WANG Lin GUO Qiaomei WANG Xueqing LOU Jiatao |
| author_sort | XU Wanxing |
| collection | DOAJ |
| description | Objective·To verify the performance and explore the clinical application value of a multi-modal pulmonary nodule diagnosis model combined with metabolic fingerprints, protein biomarker CEA and Image-AI via random forest (MPI-RF). Methods·This study enrolled 289 patients with pulmonary nodules who were admitted to the Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine and were detected by low-dose helical computed tomography (LDCT). The patients were divided into malignant nodule group ( n=197) and benign nodule group ( n=92) based on postoperative pathological results, and the basic information of the two groups was collected and compared. Electrochemiluminescence was used to detect the preoperative serum CEA levels of the patients in the two groups, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was used to detect the serum metabolic fingerprints, and the CT image artificial intelligence model Image-AI was used to calculate the image scores. CEA data, serum metabolic fingerprints data and image scores were integrated and input into MPI-RF to calculate the malignant probability score of each patient. The receiver operator characteristic curve (ROC curve) and area under the curve (AUC) were used to evaluate the performance of different models, and the DeLong test was used for comparative analysis, including the diagnostic performance of MPI-RF in different types (solid nodule, pure ground-glass nodule and part-solid nodule) and sizes (diameter<8 mm and diameter≥8 mm) of pulmonary nodules, the diagnostic performance comparison of MPI-RF with Mayo Clinic model, veterans administration (VA) model and Brock model, and the diagnostic performance comparison of MPI-RF with lung imaging reporting and data system (Lung-RADS) in benign and malignant nodules. Results·MPI-RF had good diagnostic performance in the differentiation of benign and malignant pulmonary nodules (AUC=0.887, 95% CI 0.848‒0.925, sensitivity 81.22%, specificity 83.70%). Among them, the AUC of MPI-RF for solid nodules was 0.877 (95% CI 0.820‒0.934), for part-solid nodules was 0.858 (95% CI 0.771‒0.946), and for pure ground-glass nodules was 0.978 (95% CI 0.923‒1.000). The AUC of MPI-RF was 0.840 (95% CI 0.716‒0.963) for nodules within 8 mm diameter and 0.891 (95% CI 0.849‒0.933) for nodules larger than 8 mm diameter. Compared with the existing models, the diagnostic performance of MPI-RF was better than that of Mayo Clinic model, VA model and Brock model (all P=0.000). Compared with Lung-RADS, MPI-RF had better diagnostic performance in the total samples and different types of nodules (all P=0.000). Conclusion·MPI-RF is a model for the differential diagnosis of benign and malignant pulmonary nodules with excellent performance, and has potential clinical application value. |
| format | Article |
| id | doaj-art-a824b2ad251a4cf8816cf6e380ff8e4c |
| institution | Kabale University |
| issn | 1674-8115 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University (Medical Science) |
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| series | Shanghai Jiaotong Daxue xuebao. Yixue ban |
| spelling | doaj-art-a824b2ad251a4cf8816cf6e380ff8e4c2024-12-31T14:53:45ZzhoEditorial Office of Journal of Shanghai Jiao Tong University (Medical Science)Shanghai Jiaotong Daxue xuebao. Yixue ban1674-81152024-08-014481030103610.3969/j.issn.1674-8115.2024.08.0121674-8115（2024）08-1030-07Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis modelXU Wanxing0WANG Lin1GUO Qiaomei2WANG Xueqing3LOU Jiatao4Institute of Basic Medical Sciences, School of Medicine, Jiangsu University, Zhenjiang 212013, ChinaClinical Laboratory Medicine Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, ChinaClinical Laboratory Medicine Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, ChinaClinical Laboratory Medicine Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, ChinaInstitute of Basic Medical Sciences, School of Medicine, Jiangsu University, Zhenjiang 212013, ChinaObjective·To verify the performance and explore the clinical application value of a multi-modal pulmonary nodule diagnosis model combined with metabolic fingerprints, protein biomarker CEA and Image-AI via random forest (MPI-RF). Methods·This study enrolled 289 patients with pulmonary nodules who were admitted to the Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine and were detected by low-dose helical computed tomography (LDCT). The patients were divided into malignant nodule group ( n=197) and benign nodule group ( n=92) based on postoperative pathological results, and the basic information of the two groups was collected and compared. Electrochemiluminescence was used to detect the preoperative serum CEA levels of the patients in the two groups, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was used to detect the serum metabolic fingerprints, and the CT image artificial intelligence model Image-AI was used to calculate the image scores. CEA data, serum metabolic fingerprints data and image scores were integrated and input into MPI-RF to calculate the malignant probability score of each patient. The receiver operator characteristic curve (ROC curve) and area under the curve (AUC) were used to evaluate the performance of different models, and the DeLong test was used for comparative analysis, including the diagnostic performance of MPI-RF in different types (solid nodule, pure ground-glass nodule and part-solid nodule) and sizes (diameter<8 mm and diameter≥8 mm) of pulmonary nodules, the diagnostic performance comparison of MPI-RF with Mayo Clinic model, veterans administration (VA) model and Brock model, and the diagnostic performance comparison of MPI-RF with lung imaging reporting and data system (Lung-RADS) in benign and malignant nodules. Results·MPI-RF had good diagnostic performance in the differentiation of benign and malignant pulmonary nodules (AUC=0.887, 95% CI 0.848‒0.925, sensitivity 81.22%, specificity 83.70%). Among them, the AUC of MPI-RF for solid nodules was 0.877 (95% CI 0.820‒0.934), for part-solid nodules was 0.858 (95% CI 0.771‒0.946), and for pure ground-glass nodules was 0.978 (95% CI 0.923‒1.000). The AUC of MPI-RF was 0.840 (95% CI 0.716‒0.963) for nodules within 8 mm diameter and 0.891 (95% CI 0.849‒0.933) for nodules larger than 8 mm diameter. Compared with the existing models, the diagnostic performance of MPI-RF was better than that of Mayo Clinic model, VA model and Brock model (all P=0.000). Compared with Lung-RADS, MPI-RF had better diagnostic performance in the total samples and different types of nodules (all P=0.000). Conclusion·MPI-RF is a model for the differential diagnosis of benign and malignant pulmonary nodules with excellent performance, and has potential clinical application value.https://xuebao.shsmu.edu.cn/article/2024/1674-8115/1674-8115-2024-44-8-1030.shtmlmachine learninglung adenocarcinomametabolomicsmulti-modal modelpulmonary nodule |
| spellingShingle | XU Wanxing WANG Lin GUO Qiaomei WANG Xueqing LOU Jiatao Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model Shanghai Jiaotong Daxue xuebao. Yixue ban machine learning lung adenocarcinoma metabolomics multi-modal model pulmonary nodule |
| title | Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model |
| title_full | Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model |
| title_fullStr | Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model |
| title_full_unstemmed | Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model |
| title_short | Clinical validation and application value exploration of multi-modal pulmonary nodule diagnosis model |
| title_sort | clinical validation and application value exploration of multi modal pulmonary nodule diagnosis model |
| topic | machine learning lung adenocarcinoma metabolomics multi-modal model pulmonary nodule |
| url | https://xuebao.shsmu.edu.cn/article/2024/1674-8115/1674-8115-2024-44-8-1030.shtml |
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