Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment
<i>Background and Objectives</i>: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies...
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MDPI AG
2025-06-01
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| author | Yan Qin Wei You Yulong Wang Yu Zhang Zhijie Xu Qingling Li Yuelong Zhao Zhiwei Mou Yitao Mao |
| author_facet | Yan Qin Wei You Yulong Wang Yu Zhang Zhijie Xu Qingling Li Yuelong Zhao Zhiwei Mou Yitao Mao |
| author_sort | Yan Qin |
| collection | DOAJ |
| description | <i>Background and Objectives</i>: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on the binary classification (i.e., high grade vs. low grade) of gliomas. In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. <i>Materials and Methods</i>: We reviewed and included patients with gliomas who underwent preoperative MRI examinations. Radiomics features were derived from contrast-enhanced T1-weighted images (CE-T<sub>1</sub>WI) using Pyradiomics and were selected based on their Spearman’s rank correlation with glioma grades. We developed an ANN model to classify the four pathological grades of glioma, assigning training and validation sets at a 3:1 ratio. A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. <i>Results</i>: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. At this point, the average overall diagnostic accuracy ratings for the training and validation sets were 91.28% and 87.04%, respectively, with corresponding coefficients of variation (CVs) of 0.0190 and 0.0272. The diagnostic accuracies for grades I, II, III, and IV in the training set were 91.9%, 89.9%, 92.1%, and 90.7%, respectively. The diagnostic accuracies for grades I, II, III, and IV in the validation set were 88.7%, 87.1%, 86.5%, and 86.9%, respectively. <i>Conclusions</i>: The MRI radiomics-based ANN model shows promising potential for the four-type classification of glioma grading, offering an objective and noninvasive method for more refined glioma grading. This model could aid in clinical decision making regarding the treatment of patients with various grades of gliomas. |
| format | Article |
| id | doaj-art-e7b193a41cd146d991b29b73edbfdba5 |
| institution | Kabale University |
| issn | 1010-660X 1648-9144 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medicina |
| spelling | doaj-art-e7b193a41cd146d991b29b73edbfdba52025-08-20T03:27:25ZengMDPI AGMedicina1010-660X1648-91442025-06-01616103410.3390/medicina61061034Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading AssessmentYan Qin0Wei You1Yulong Wang2Yu Zhang3Zhijie Xu4Qingling Li5Yuelong Zhao6Zhiwei Mou7Yitao Mao8Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, ChinaDepartment of Radiology, Xiangya Hospital, Central South University, Changsha 410008, ChinaDepartment of Radiology, Xiangya Hospital, Central South University, Changsha 410008, ChinaCommunication Sciences and Disorders, Oklahoma State University, Stillwater, OK 74075, USADepartment of Pathology, Xiangya Hospital, Central South University, Changsha 410008, ChinaDepartment of Pathology, Xiangya Hospital, Central South University, Changsha 410008, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaDepartment of Rehabilitation, The First Affiliated Hospital of Jinan University, Guangzhou 510630, ChinaDepartment of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China<i>Background and Objectives</i>: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on the binary classification (i.e., high grade vs. low grade) of gliomas. In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. <i>Materials and Methods</i>: We reviewed and included patients with gliomas who underwent preoperative MRI examinations. Radiomics features were derived from contrast-enhanced T1-weighted images (CE-T<sub>1</sub>WI) using Pyradiomics and were selected based on their Spearman’s rank correlation with glioma grades. We developed an ANN model to classify the four pathological grades of glioma, assigning training and validation sets at a 3:1 ratio. A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. <i>Results</i>: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. At this point, the average overall diagnostic accuracy ratings for the training and validation sets were 91.28% and 87.04%, respectively, with corresponding coefficients of variation (CVs) of 0.0190 and 0.0272. The diagnostic accuracies for grades I, II, III, and IV in the training set were 91.9%, 89.9%, 92.1%, and 90.7%, respectively. The diagnostic accuracies for grades I, II, III, and IV in the validation set were 88.7%, 87.1%, 86.5%, and 86.9%, respectively. <i>Conclusions</i>: The MRI radiomics-based ANN model shows promising potential for the four-type classification of glioma grading, offering an objective and noninvasive method for more refined glioma grading. This model could aid in clinical decision making regarding the treatment of patients with various grades of gliomas.https://www.mdpi.com/1648-9144/61/6/1034MRIradiomicsgliomagradingartificial neural network model |
| spellingShingle | Yan Qin Wei You Yulong Wang Yu Zhang Zhijie Xu Qingling Li Yuelong Zhao Zhiwei Mou Yitao Mao Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment Medicina MRI radiomics glioma grading artificial neural network model |
| title | Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment |
| title_full | Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment |
| title_fullStr | Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment |
| title_full_unstemmed | Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment |
| title_short | Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment |
| title_sort | magnetic resonance imaging radiomics driven artificial neural network model for advanced glioma grading assessment |
| topic | MRI radiomics glioma grading artificial neural network model |
| url | https://www.mdpi.com/1648-9144/61/6/1034 |
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