Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features
Purpose To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning. Methods This cohort study retrospectively included a total of 345 neuroblastoma patients who underwent testing for bone marrow metastatic...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-10-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338241290386 |
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| author | Haoru Wang MD Ling He MD Xin Chen MD Shuang Ding MD Mingye Xie MD Jinhua Cai MD |
| author_facet | Haoru Wang MD Ling He MD Xin Chen MD Shuang Ding MD Mingye Xie MD Jinhua Cai MD |
| author_sort | Haoru Wang MD |
| collection | DOAJ |
| description | Purpose To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning. Methods This cohort study retrospectively included a total of 345 neuroblastoma patients who underwent testing for bone marrow metastatic status. Tumor lesions on CECT images were delineated by two radiologists, and 1409 radiomics features were extracted. Correlation analysis, Least Absolute Shrinkage and Selection Operator regression, and one-way analysis of variance were used to identify radiomics features associated with bone marrow metastasis. A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. The performance of the radiomics model was evaluated using the area under the curve (AUC), 95% confidence interval (CI), accuracy, sensitivity, and specificity. Results The radiomics model included 16 features, with a predominant focus on texture features (12/16, 75%). In the training set, the model demonstrated an AUC of 0.891 (95% CI: 0.848-0.933), an accuracy of 0.831 (95% CI: 0.829-0.832), a sensitivity of 0.893 (95% CI: 0.840-0.946), and a specificity of 0.757 (95% CI: 0.677-0.837). In the test set, the AUC, accuracy, sensitivity, and specificity were 0.807 (95% CI: 0.720-0.893), 0.767 (95% CI: 0.764-0.770), 0.696 (95% CI: 0.576-0.817), and 0.851 (95% CI: 0.749-0.953), respectively. Conclusion Radiomics features extracted from CECT images are associated with the presence of bone marrow metastasis in neuroblastoma, providing potential new imaging biomarkers for predicting bone marrow metastasis in this disease. |
| format | Article |
| id | doaj-art-5fb73d2c1c8745aa848c4fda0ff0af7c |
| institution | OA Journals |
| issn | 1533-0338 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj-art-5fb73d2c1c8745aa848c4fda0ff0af7c2025-08-20T01:54:16ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382024-10-012310.1177/15330338241290386Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics FeaturesHaoru Wang MDLing He MDXin Chen MDShuang Ding MDMingye Xie MDJinhua Cai MDPurpose To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning. Methods This cohort study retrospectively included a total of 345 neuroblastoma patients who underwent testing for bone marrow metastatic status. Tumor lesions on CECT images were delineated by two radiologists, and 1409 radiomics features were extracted. Correlation analysis, Least Absolute Shrinkage and Selection Operator regression, and one-way analysis of variance were used to identify radiomics features associated with bone marrow metastasis. A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. The performance of the radiomics model was evaluated using the area under the curve (AUC), 95% confidence interval (CI), accuracy, sensitivity, and specificity. Results The radiomics model included 16 features, with a predominant focus on texture features (12/16, 75%). In the training set, the model demonstrated an AUC of 0.891 (95% CI: 0.848-0.933), an accuracy of 0.831 (95% CI: 0.829-0.832), a sensitivity of 0.893 (95% CI: 0.840-0.946), and a specificity of 0.757 (95% CI: 0.677-0.837). In the test set, the AUC, accuracy, sensitivity, and specificity were 0.807 (95% CI: 0.720-0.893), 0.767 (95% CI: 0.764-0.770), 0.696 (95% CI: 0.576-0.817), and 0.851 (95% CI: 0.749-0.953), respectively. Conclusion Radiomics features extracted from CECT images are associated with the presence of bone marrow metastasis in neuroblastoma, providing potential new imaging biomarkers for predicting bone marrow metastasis in this disease.https://doi.org/10.1177/15330338241290386 |
| spellingShingle | Haoru Wang MD Ling He MD Xin Chen MD Shuang Ding MD Mingye Xie MD Jinhua Cai MD Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features Technology in Cancer Research & Treatment |
| title | Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features |
| title_full | Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features |
| title_fullStr | Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features |
| title_full_unstemmed | Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features |
| title_short | Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features |
| title_sort | predicting bone marrow metastasis in neuroblastoma an explainable machine learning approach using contrast enhanced computed tomography radiomics features |
| url | https://doi.org/10.1177/15330338241290386 |
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