Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion
Abstract Objective Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomi...
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BMC
2024-11-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01499-8 |
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| author | Zifeng Zhang Ning Li Yuhang Qian Huilin Cheng |
| author_facet | Zifeng Zhang Ning Li Yuhang Qian Huilin Cheng |
| author_sort | Zifeng Zhang |
| collection | DOAJ |
| description | Abstract Objective Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomics model for distinguishing scTDL from IMSCT before treatment initiation. Methods A total of 75 patients were analyzed in this retrospective study, comprising 55 with IMSCT and 20 with scTDL. Radiomics features were extracted from T1- and T2-weighted imaging (T1&T2WI) scans upon admission. Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). The performance of the optimal model was then compared to radiologists' assessments. Results This study developed 30 predictive models using ten classifiers across two imaging sequences. The MLP model with two sequences (T1&T2WI) emerged as the most effective one, showing superior accuracy in MRI analysis with an area under the curve (AUC) of 0.991 in training and 0.962 in testing. Moreover, statistical analyses highlighted the radiomics model significantly outperformed radiologists' assessments (p < 0.05) in distinguishing between IMSCT and scTDL. Conclusion We present an MRI-based radiomics model with high diagnostic accuracy in differentiating IMSCT from scTDL. The model’s performance was comparable to junior radiologists, highlighting its potential as an effective diagnostic aid in clinical practice. |
| format | Article |
| id | doaj-art-c73fa05a701047389d2d7d52aa1368fb |
| institution | OA Journals |
| issn | 1471-2342 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-c73fa05a701047389d2d7d52aa1368fb2025-08-20T02:33:00ZengBMCBMC Medical Imaging1471-23422024-11-0124111310.1186/s12880-024-01499-8Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesionZifeng Zhang0Ning Li1Yuhang Qian2Huilin Cheng3School of Medicine, Southeast UniversityDepartment of Neurosurgery, Affiliated Zhongda Hospital, Southeast UniversitySchool of Medicine, Southeast UniversitySchool of Medicine, Southeast UniversityAbstract Objective Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomics model for distinguishing scTDL from IMSCT before treatment initiation. Methods A total of 75 patients were analyzed in this retrospective study, comprising 55 with IMSCT and 20 with scTDL. Radiomics features were extracted from T1- and T2-weighted imaging (T1&T2WI) scans upon admission. Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). The performance of the optimal model was then compared to radiologists' assessments. Results This study developed 30 predictive models using ten classifiers across two imaging sequences. The MLP model with two sequences (T1&T2WI) emerged as the most effective one, showing superior accuracy in MRI analysis with an area under the curve (AUC) of 0.991 in training and 0.962 in testing. Moreover, statistical analyses highlighted the radiomics model significantly outperformed radiologists' assessments (p < 0.05) in distinguishing between IMSCT and scTDL. Conclusion We present an MRI-based radiomics model with high diagnostic accuracy in differentiating IMSCT from scTDL. The model’s performance was comparable to junior radiologists, highlighting its potential as an effective diagnostic aid in clinical practice.https://doi.org/10.1186/s12880-024-01499-8Intramedullary spinal cord tumorTumefactive demyelinating lesionMagnetic resonance imagesRadiomics |
| spellingShingle | Zifeng Zhang Ning Li Yuhang Qian Huilin Cheng Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion BMC Medical Imaging Intramedullary spinal cord tumor Tumefactive demyelinating lesion Magnetic resonance images Radiomics |
| title | Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion |
| title_full | Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion |
| title_fullStr | Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion |
| title_full_unstemmed | Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion |
| title_short | Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion |
| title_sort | establishment of an mri based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion |
| topic | Intramedullary spinal cord tumor Tumefactive demyelinating lesion Magnetic resonance images Radiomics |
| url | https://doi.org/10.1186/s12880-024-01499-8 |
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