Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study
Abstract Background Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invas...
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12916-023-02898-4 |
| _version_ | 1797811453403594752 |
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| author | Chao Ma Liyang Wang Dengpan Song Chuntian Gao Linkai Jing Yang Lu Dongkang Liu Weitao Man Kaiyuan Yang Zhe Meng Huifang Zhang Ping Xue Yupeng Zhang Fuyou Guo Guihuai Wang |
| author_facet | Chao Ma Liyang Wang Dengpan Song Chuntian Gao Linkai Jing Yang Lu Dongkang Liu Weitao Man Kaiyuan Yang Zhe Meng Huifang Zhang Ping Xue Yupeng Zhang Fuyou Guo Guihuai Wang |
| author_sort | Chao Ma |
| collection | DOAJ |
| description | Abstract Background Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. Methods A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. Results The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I–II or WHO III–IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. Conclusions This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas. |
| first_indexed | 2024-03-13T07:23:14Z |
| format | Article |
| id | doaj.art-62ba34c3c1e349a697266f6e63242d20 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-03-13T07:23:14Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-62ba34c3c1e349a697266f6e63242d202023-06-04T11:30:54ZengBMCBMC Medicine1741-70152023-05-0121111310.1186/s12916-023-02898-4Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective studyChao Ma0Liyang Wang1Dengpan Song2Chuntian Gao3Linkai Jing4Yang Lu5Dongkang Liu6Weitao Man7Kaiyuan Yang8Zhe Meng9Huifang Zhang10Ping Xue11Yupeng Zhang12Fuyou Guo13Guihuai Wang14School of Clinical Medicine, Tsinghua UniversitySchool of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou UniversitySchool of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityInstitute for Precision Medicine, Tsinghua UniversityDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou UniversityDepartment of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua UniversityAbstract Background Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. Methods A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. Results The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I–II or WHO III–IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. Conclusions This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.https://doi.org/10.1186/s12916-023-02898-4Intramedullary gliomasAlpha thalassemia/mental retardation syndrome X-linkedTumor protein p53MultimodalMachine learning |
| spellingShingle | Chao Ma Liyang Wang Dengpan Song Chuntian Gao Linkai Jing Yang Lu Dongkang Liu Weitao Man Kaiyuan Yang Zhe Meng Huifang Zhang Ping Xue Yupeng Zhang Fuyou Guo Guihuai Wang Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study BMC Medicine Intramedullary gliomas Alpha thalassemia/mental retardation syndrome X-linked Tumor protein p53 Multimodal Machine learning |
| title | Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study |
| title_full | Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study |
| title_fullStr | Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study |
| title_full_unstemmed | Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study |
| title_short | Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study |
| title_sort | multimodal based machine learning strategy for accurate and non invasive prediction of intramedullary glioma grade and mutation status of molecular markers a retrospective study |
| topic | Intramedullary gliomas Alpha thalassemia/mental retardation syndrome X-linked Tumor protein p53 Multimodal Machine learning |
| url | https://doi.org/10.1186/s12916-023-02898-4 |
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