An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs)
Abstract Background An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, impro...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-024-13268-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585652389019648 |
|---|---|
| author | Ran Ni Yongjie Huang Lei Wang Hongjie Chen Guorui Zhang Yali Yu Yinglan Kuang Yuyan Tang Xing Lu Hong Liu |
| author_facet | Ran Ni Yongjie Huang Lei Wang Hongjie Chen Guorui Zhang Yali Yu Yinglan Kuang Yuyan Tang Xing Lu Hong Liu |
| author_sort | Ran Ni |
| collection | DOAJ |
| description | Abstract Background An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers. Methods We retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building. Results Among LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). Conclusions These results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC. |
| format | Article |
| id | doaj-art-96fad96261f34c9c82697406c8e2304b |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-96fad96261f34c9c82697406c8e2304b2025-01-26T12:38:04ZengBMCBMC Cancer1471-24072025-01-0125111110.1186/s12885-024-13268-5An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs)Ran Ni0Yongjie Huang1Lei Wang2Hongjie Chen3Guorui Zhang4Yali Yu5Yinglan Kuang6Yuyan Tang7Xing Lu8Hong Liu9Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Geriatric Respiratory Sleep, The First Affiliated Hospital of Zhengzhou UniversityZhuhai Sanmed Biotech Ltd.Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou UniversityZhuhai Sanmed Biotech Ltd.Zhuhai Sanmed Biotech Ltd.Zhuhai Sanmed Biotech Ltd.Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou UniversityAbstract Background An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers. Methods We retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building. Results Among LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). Conclusions These results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.https://doi.org/10.1186/s12885-024-13268-5LCNon-smokerArtificial intelligenceLiquid biopsyPrediction modelEarly diagnosis |
| spellingShingle | Ran Ni Yongjie Huang Lei Wang Hongjie Chen Guorui Zhang Yali Yu Yinglan Kuang Yuyan Tang Xing Lu Hong Liu An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) BMC Cancer LC Non-smoker Artificial intelligence Liquid biopsy Prediction model Early diagnosis |
| title | An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) |
| title_full | An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) |
| title_fullStr | An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) |
| title_full_unstemmed | An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) |
| title_short | An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) |
| title_sort | early lung cancer diagnosis model for non smokers incorporating ct imaging analysis and circulating genetically abnormal cells cacs |
| topic | LC Non-smoker Artificial intelligence Liquid biopsy Prediction model Early diagnosis |
| url | https://doi.org/10.1186/s12885-024-13268-5 |
| work_keys_str_mv | AT ranni anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yongjiehuang anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT leiwang anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT hongjiechen anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT guoruizhang anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yaliyu anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yinglankuang anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yuyantang anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT xinglu anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT hongliu anearlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT ranni earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yongjiehuang earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT leiwang earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT hongjiechen earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT guoruizhang earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yaliyu earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yinglankuang earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT yuyantang earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT xinglu earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs AT hongliu earlylungcancerdiagnosismodelfornonsmokersincorporatingctimaginganalysisandcirculatinggeneticallyabnormalcellscacs |