Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions
Abstract Objective To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiological and clinical features in differentiating lipid-poor adrenal adenomas from metastatic tumors, and to explain the model’s prediction result...
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01798-8 |
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| author | Shao-Cai Wang Sheng-Nan Yin Zi-You Wang Ning Ding Yi-Ding Ji Long Jin |
| author_facet | Shao-Cai Wang Sheng-Nan Yin Zi-You Wang Ning Ding Yi-Ding Ji Long Jin |
| author_sort | Shao-Cai Wang |
| collection | DOAJ |
| description | Abstract Objective To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiological and clinical features in differentiating lipid-poor adrenal adenomas from metastatic tumors, and to explain the model’s prediction results through SHAP(Shapley Additive Explanations) analysis. Methods A retrospective analysis was conducted on abdominal contrast-enhanced CT images and clinical data from 416 pathologically confirmed adrenal tumor patients at our hospital from July 2019 to December 2024. Patients were randomly divided into training and testing sets in a 7:3 ratio. Six convolutional neural network (CNN)-based deep learning models were employed, and the model with the highest diagnostic performance was selected based on the area under curve(AUC) of the ROC. Subsequently, multiple machine learning models incorporating clinical and radiological features were developed and evaluated using various indicators and AUC.The best-performing machine learning model was further analyzed using SHAP plots to enhance interpretability and quantify feature contributions. Results All six deep learning models demonstrated excellent diagnostic performance, with AUC values exceeding 0.8, among which ResNet50 achieved the highest AUC. Among the 10 machine learning models incorporating clinical and imaging features, the extreme gradient boosting(XGBoost) model exhibited the best accuracy(ACC), sensitivity, and AUC, indicating superior diagnostic performance.SHAP analysis revealed contributions from ResNet50, RPW, age, and other key features in model predictions. Conclusion Machine learning models based on contrast-enhanced CT combined with clinical and imaging features exhibit outstanding diagnostic performance in differentiating lipid-poor adrenal adenomas from metastases. |
| format | Article |
| id | doaj-art-6edf7c0177c4473eaed5d9e2c4cab1f2 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-6edf7c0177c4473eaed5d9e2c4cab1f22025-08-20T03:45:41ZengBMCBMC Medical Imaging1471-23422025-07-012511910.1186/s12880-025-01798-8Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesionsShao-Cai Wang0Sheng-Nan Yin1Zi-You Wang2Ning Ding3Yi-Ding Ji4Long Jin5Suzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow UniversitySuzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow UniversitySuzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow UniversitySuzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow UniversitySuzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow UniversitySuzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow UniversityAbstract Objective To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiological and clinical features in differentiating lipid-poor adrenal adenomas from metastatic tumors, and to explain the model’s prediction results through SHAP(Shapley Additive Explanations) analysis. Methods A retrospective analysis was conducted on abdominal contrast-enhanced CT images and clinical data from 416 pathologically confirmed adrenal tumor patients at our hospital from July 2019 to December 2024. Patients were randomly divided into training and testing sets in a 7:3 ratio. Six convolutional neural network (CNN)-based deep learning models were employed, and the model with the highest diagnostic performance was selected based on the area under curve(AUC) of the ROC. Subsequently, multiple machine learning models incorporating clinical and radiological features were developed and evaluated using various indicators and AUC.The best-performing machine learning model was further analyzed using SHAP plots to enhance interpretability and quantify feature contributions. Results All six deep learning models demonstrated excellent diagnostic performance, with AUC values exceeding 0.8, among which ResNet50 achieved the highest AUC. Among the 10 machine learning models incorporating clinical and imaging features, the extreme gradient boosting(XGBoost) model exhibited the best accuracy(ACC), sensitivity, and AUC, indicating superior diagnostic performance.SHAP analysis revealed contributions from ResNet50, RPW, age, and other key features in model predictions. Conclusion Machine learning models based on contrast-enhanced CT combined with clinical and imaging features exhibit outstanding diagnostic performance in differentiating lipid-poor adrenal adenomas from metastases.https://doi.org/10.1186/s12880-025-01798-8Adrenal glandAdenomaMetastasisDeep learningMachine learningAutomatic segmentation |
| spellingShingle | Shao-Cai Wang Sheng-Nan Yin Zi-You Wang Ning Ding Yi-Ding Ji Long Jin Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions BMC Medical Imaging Adrenal gland Adenoma Metastasis Deep learning Machine learning Automatic segmentation |
| title | Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions |
| title_full | Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions |
| title_fullStr | Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions |
| title_full_unstemmed | Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions |
| title_short | Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions |
| title_sort | evaluation of a fusion model combining deep learning models based on enhanced ct images with radiological and clinical features in distinguishing lipid poor adrenal adenoma from metastatic lesions |
| topic | Adrenal gland Adenoma Metastasis Deep learning Machine learning Automatic segmentation |
| url | https://doi.org/10.1186/s12880-025-01798-8 |
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