Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China
Abstract Background Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed to improve the DL’s specificity for the eff...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Pulmonary Medicine |
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| Online Access: | https://doi.org/10.1186/s12890-025-03807-6 |
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| author | Qingcheng Meng Pengfei Ren Lanwei Guo Pengrui Gao Tong Liu Wenda Chen Wentao Liu Hui Peng Mengjia Fang Shuo Meng Hong Ge Meng Li Xuejun Chen |
| author_facet | Qingcheng Meng Pengfei Ren Lanwei Guo Pengrui Gao Tong Liu Wenda Chen Wentao Liu Hui Peng Mengjia Fang Shuo Meng Hong Ge Meng Li Xuejun Chen |
| author_sort | Qingcheng Meng |
| collection | DOAJ |
| description | Abstract Background Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed to improve the DL’s specificity for the efficiency of LC screening in China. Purpose To develop and evaluate a risk model combining 7-TAAbs test and DL scores for diagnosing LC with pulmonary lesions < 70 mm. Materials and methods Four hundreds and six patients with 406 lesions were enrolled and assigned into training set (n = 313) and test set (n = 93) randomly. The malignant lesions were defined as those lesions with high malignant risks by DL or those with positive expression of 7-TAAbs panel. Model performance was assessed using the area under the receiver operating characteristic curves (AUC). Results In the training set, the AUCs for DL, 7-TAAbs, combined model (DL and 7-TAAbs) and combined model (DL or 7-TAAbs) were 0.771, 0.638, 0.606, 0.809 seperately. In the test set, the combined model (DL or 7-TAAbs) achieved achieved the highest sensitivity (82.6%), NPV (81.8%) and accuracy (79.6%) among four models, and the AUCs of DL model, 7-TAAbs model, combined model (DL and 7-TAAbs), and combined model (DL or 7-TAAbs) were 0.731, 0.679, 0.574, and 0.794, respectively. Conclusion The 7-TAAbs test significantly enhances DL performance in predicting LC with pulmonary leisons < 70 mm in China. |
| format | Article |
| id | doaj-art-490b6f6af9664e89b30d3268c8a1194f |
| institution | Kabale University |
| issn | 1471-2466 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj-art-490b6f6af9664e89b30d3268c8a1194f2025-08-20T03:42:37ZengBMCBMC Pulmonary Medicine1471-24662025-07-0125111110.1186/s12890-025-03807-6Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in ChinaQingcheng Meng0Pengfei Ren1Lanwei Guo2Pengrui Gao3Tong Liu4Wenda Chen5Wentao Liu6Hui Peng7Mengjia Fang8Shuo Meng9Hong Ge10Meng Li11Xuejun Chen12Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Clinical Research Management, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Mechanical and Electrical Engineering, Beijing University of Chemical TechnologyDepartment of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalDepartment of Radiology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences, Peking Union Medical CollegeDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalAbstract Background Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed to improve the DL’s specificity for the efficiency of LC screening in China. Purpose To develop and evaluate a risk model combining 7-TAAbs test and DL scores for diagnosing LC with pulmonary lesions < 70 mm. Materials and methods Four hundreds and six patients with 406 lesions were enrolled and assigned into training set (n = 313) and test set (n = 93) randomly. The malignant lesions were defined as those lesions with high malignant risks by DL or those with positive expression of 7-TAAbs panel. Model performance was assessed using the area under the receiver operating characteristic curves (AUC). Results In the training set, the AUCs for DL, 7-TAAbs, combined model (DL and 7-TAAbs) and combined model (DL or 7-TAAbs) were 0.771, 0.638, 0.606, 0.809 seperately. In the test set, the combined model (DL or 7-TAAbs) achieved achieved the highest sensitivity (82.6%), NPV (81.8%) and accuracy (79.6%) among four models, and the AUCs of DL model, 7-TAAbs model, combined model (DL and 7-TAAbs), and combined model (DL or 7-TAAbs) were 0.731, 0.679, 0.574, and 0.794, respectively. Conclusion The 7-TAAbs test significantly enhances DL performance in predicting LC with pulmonary leisons < 70 mm in China.https://doi.org/10.1186/s12890-025-03807-6X-ray computed tomographyTumor biomarkersDeep learningNeoplasm, pulmonary lesions |
| spellingShingle | Qingcheng Meng Pengfei Ren Lanwei Guo Pengrui Gao Tong Liu Wenda Chen Wentao Liu Hui Peng Mengjia Fang Shuo Meng Hong Ge Meng Li Xuejun Chen Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China BMC Pulmonary Medicine X-ray computed tomography Tumor biomarkers Deep learning Neoplasm, pulmonary lesions |
| title | Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China |
| title_full | Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China |
| title_fullStr | Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China |
| title_full_unstemmed | Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China |
| title_short | Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China |
| title_sort | multiple tumor related autoantibodies test enhances ct based deep learning performance in diagnosing lung cancer with diameters 70 mm a prospective study in china |
| topic | X-ray computed tomography Tumor biomarkers Deep learning Neoplasm, pulmonary lesions |
| url | https://doi.org/10.1186/s12890-025-03807-6 |
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