Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

Autor:	Yimit Y; Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000., Yasin P; Department of Spine Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054., Tuersun A; Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000., Wang J; Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080., Wang X; Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, 510630., Huang C; Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080., Abudoubari S; Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000., Chen X; Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, PR China, 100080., Ibrahim I; Department of General Surgery, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000., Nijiati P; Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000., Wang Y; Department of Imaging Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054., Zou X; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000; Clinical Medical Research Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000., Nijiati M; Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000. Electronic address: mydl0911@163.com.
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Aug; Vol. 31 (8), pp. 3384-3396. Date of Electronic Publication: 2024 Mar 20.
DOI:	10.1016/j.acra.2024.02.040
Abstrakt:	Rationale and Objectives: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set. Materials and Methods: Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. Results: 31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. Conclusion: The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets. Competing Interests: Declaration of Competing Interest The authors declare that they have no identified economic or personal conflicts that would have seemed to have an impact on the research presented in this study. Competing Interest The authors affirm that they have no identified economic or personal conflicts that would have seemed to impact the research presented in this study. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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