Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma
Abstract Objectives Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC). Materials and methods A total of 466 stage I LAC pati...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-024-00668-w |
| _version_ | 1797273406640488448 |
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| author | Zhaisong Gao Pingping An Runze Li Fengyu Wu Yuhui Sun Jie Wu Guangjie Yang Zhenguang Wang |
| author_facet | Zhaisong Gao Pingping An Runze Li Fengyu Wu Yuhui Sun Jie Wu Guangjie Yang Zhenguang Wang |
| author_sort | Zhaisong Gao |
| collection | DOAJ |
| description | Abstract Objectives Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC). Materials and methods A total of 466 stage I LAC patients who underwent 2-[18F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort (n = 232, 20.3% STAS-positive), a validation cohort (n = 122, 27.0% STAS-positive), and a test cohort (n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUVmax were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC. Results The differences in age (P = 0.009), lesion density subtype (P < 0.001), spiculation sign (P < 0.001), bronchus truncation sign (P = 0.001), and SUVmax (P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [OR(95%CI):3.310(1.150–9.530), P = 0.027], lesion density subtype (P = 0.004) and SUVmax ≥ 2.5 g/ml [OR(95%CI):3.268(1.021–1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUVmax) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively. Conclusions STAS was more likely to occur in older patients, in solid lesions and higher SUVmax in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing. |
| first_indexed | 2024-03-07T14:43:55Z |
| format | Article |
| id | doaj.art-a55a398e88484d4f905ff906e13596ec |
| institution | Directory Open Access Journal |
| issn | 1470-7330 |
| language | English |
| last_indexed | 2024-03-07T14:43:55Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj.art-a55a398e88484d4f905ff906e13596ec2024-03-05T20:08:11ZengBMCCancer Imaging1470-73302024-02-012411910.1186/s40644-024-00668-wDevelopment and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinomaZhaisong Gao0Pingping An1Runze Li2Fengyu Wu3Yuhui Sun4Jie Wu5Guangjie Yang6Zhenguang Wang7Department of Nuclear Medicine, Affiliated Hospital of Qingdao UniversityDepartment of Thyroid Disease, Qingdao Municipal Hospital Group East Hospital, Qingdao Municipal Hospital GroupQingdao Medical College, Qingdao UniversityDepartment of Nuclear Medicine, Affiliated Hospital of Qingdao UniversityDepartment of Thoracic Surgery, Affiliated Hospital of Qingdao UniversityDepartment of Pathology, Affiliated Hospital of Qingdao UniversityDepartment of Nuclear Medicine, Affiliated Hospital of Qingdao UniversityDepartment of Nuclear Medicine, Affiliated Hospital of Qingdao UniversityAbstract Objectives Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC). Materials and methods A total of 466 stage I LAC patients who underwent 2-[18F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort (n = 232, 20.3% STAS-positive), a validation cohort (n = 122, 27.0% STAS-positive), and a test cohort (n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUVmax were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC. Results The differences in age (P = 0.009), lesion density subtype (P < 0.001), spiculation sign (P < 0.001), bronchus truncation sign (P = 0.001), and SUVmax (P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [OR(95%CI):3.310(1.150–9.530), P = 0.027], lesion density subtype (P = 0.004) and SUVmax ≥ 2.5 g/ml [OR(95%CI):3.268(1.021–1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUVmax) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively. Conclusions STAS was more likely to occur in older patients, in solid lesions and higher SUVmax in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing.https://doi.org/10.1186/s40644-024-00668-wLungAdenocarcinomaPositron emission tomographyComputed tomographyInvasion |
| spellingShingle | Zhaisong Gao Pingping An Runze Li Fengyu Wu Yuhui Sun Jie Wu Guangjie Yang Zhenguang Wang Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma Cancer Imaging Lung Adenocarcinoma Positron emission tomography Computed tomography Invasion |
| title | Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma |
| title_full | Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma |
| title_fullStr | Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma |
| title_full_unstemmed | Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma |
| title_short | Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma |
| title_sort | development and validation of a clinic radiological model to predict tumor spread through air spaces in stage i lung adenocarcinoma |
| topic | Lung Adenocarcinoma Positron emission tomography Computed tomography Invasion |
| url | https://doi.org/10.1186/s40644-024-00668-w |
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