Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer
Abstract Background This study aimed to develop and validate machine learning models for preoperative identification of metastasis to station 4 mediastinal lymph nodes (MLNM) in non-small cell lung cancer (NSCLC) patients at pathological N0-N2 (pN0-pN2) stage, thereby enhancing the precision of clin...
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| Language: | English |
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BMC
2025-06-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01686-1 |
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| author | Yanru Kang Mei Li Xizi Xing Kaixuan Qian Hongxia Liu Yafei Qi Yanguo Liu Yi Cui Hua Zhang |
| author_facet | Yanru Kang Mei Li Xizi Xing Kaixuan Qian Hongxia Liu Yafei Qi Yanguo Liu Yi Cui Hua Zhang |
| author_sort | Yanru Kang |
| collection | DOAJ |
| description | Abstract Background This study aimed to develop and validate machine learning models for preoperative identification of metastasis to station 4 mediastinal lymph nodes (MLNM) in non-small cell lung cancer (NSCLC) patients at pathological N0-N2 (pN0-pN2) stage, thereby enhancing the precision of clinical decision-making. Methods We included a total of 356 NSCLC patients at pN0-pN2 stage, divided into training (n = 207), internal test (n = 90), and independent test (n = 59) sets. Station 4 mediastinal lymph nodes (LNs) regions of interest (ROIs) were semi-automatically segmented on venous-phase computed tomography (CT) images for radiomics feature extraction. Using least absolute shrinkage and selection operator (LASSO) regression to select features with non-zero coefficients. Four machine learning algorithms—decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM)—were employed to construct radiomics models. Clinical predictors were identified through univariate and multivariate logistic regression, which were subsequently integrated with radiomics features to develop combined models. Models performance were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and DeLong’s test. Results Out of 1721 radiomics features, eight radiomics features were selected using LASSO regression. The RF-based combined model exhibited the strongest discriminative power, with an area under the curve (AUC) of 0.934 for the training set and 0.889 for the internal test set. The calibration curve and DCA further indicated the superior performance of the combined model based on RF. The independent test set further verified the model’s robustness. Conclusions The combined model based on RF, integrating radiomics and clinical features, effectively and non-invasively identifies metastasis to the station 4 mediastinal LNs in NSCLC patients at pN0-pN2 stage. This model serves as an effective auxiliary tool for clinical decision-making and has the potential to optimize treatment strategies and improve prognostic assessment for pN0-pN2 patients. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-66907e85ed144d2d932723df0996bc7a |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-66907e85ed144d2d932723df0996bc7a2025-08-20T03:06:31ZengBMCBMC Medical Imaging1471-23422025-06-0125111610.1186/s12880-025-01686-1Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancerYanru Kang0Mei Li1Xizi Xing2Kaixuan Qian3Hongxia Liu4Yafei Qi5Yanguo Liu6Yi Cui7Hua Zhang8School of Clinical and Basic Medicine, Shandong First Medical UniversitySchool of Clinical and Basic Medicine, Shandong First Medical UniversitySchool of Clinical and Basic Medicine, Shandong First Medical UniversitySchool of Clinical and Basic Medicine, Shandong First Medical UniversityDepartment of Radiology, The Affiliated of Shandong Traditional Medical UniversityDepartment of Radiology, Qilu Hospital of Shandong UniversityDepartment of Medical Oncology, Qilu Hospital of Shandong UniversityDepartment of Radiology, Qilu Hospital of Shandong UniversitySchool of Clinical and Basic Medicine, Shandong First Medical UniversityAbstract Background This study aimed to develop and validate machine learning models for preoperative identification of metastasis to station 4 mediastinal lymph nodes (MLNM) in non-small cell lung cancer (NSCLC) patients at pathological N0-N2 (pN0-pN2) stage, thereby enhancing the precision of clinical decision-making. Methods We included a total of 356 NSCLC patients at pN0-pN2 stage, divided into training (n = 207), internal test (n = 90), and independent test (n = 59) sets. Station 4 mediastinal lymph nodes (LNs) regions of interest (ROIs) were semi-automatically segmented on venous-phase computed tomography (CT) images for radiomics feature extraction. Using least absolute shrinkage and selection operator (LASSO) regression to select features with non-zero coefficients. Four machine learning algorithms—decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM)—were employed to construct radiomics models. Clinical predictors were identified through univariate and multivariate logistic regression, which were subsequently integrated with radiomics features to develop combined models. Models performance were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and DeLong’s test. Results Out of 1721 radiomics features, eight radiomics features were selected using LASSO regression. The RF-based combined model exhibited the strongest discriminative power, with an area under the curve (AUC) of 0.934 for the training set and 0.889 for the internal test set. The calibration curve and DCA further indicated the superior performance of the combined model based on RF. The independent test set further verified the model’s robustness. Conclusions The combined model based on RF, integrating radiomics and clinical features, effectively and non-invasively identifies metastasis to the station 4 mediastinal LNs in NSCLC patients at pN0-pN2 stage. This model serves as an effective auxiliary tool for clinical decision-making and has the potential to optimize treatment strategies and improve prognostic assessment for pN0-pN2 patients. Clinical trial number Not applicable.https://doi.org/10.1186/s12880-025-01686-1Non-small cell lung cancerMediastinal lymph node metastasisMachine learningRadiomics |
| spellingShingle | Yanru Kang Mei Li Xizi Xing Kaixuan Qian Hongxia Liu Yafei Qi Yanguo Liu Yi Cui Hua Zhang Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer BMC Medical Imaging Non-small cell lung cancer Mediastinal lymph node metastasis Machine learning Radiomics |
| title | Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer |
| title_full | Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer |
| title_fullStr | Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer |
| title_full_unstemmed | Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer |
| title_short | Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer |
| title_sort | computed tomography based radiomics model for predicting station 4 lymph node metastasis in non small cell lung cancer |
| topic | Non-small cell lung cancer Mediastinal lymph node metastasis Machine learning Radiomics |
| url | https://doi.org/10.1186/s12880-025-01686-1 |
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