Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas
Abstract Background 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-04-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-024-01262-z |
| _version_ | 1827284057187155968 |
|---|---|
| author | Andong Ma Xinran Yan Yaoming Qu Haitao Wen Xia Zou Xinzi Liu Mingjun Lu Jianhua Mo Zhibo Wen |
| author_facet | Andong Ma Xinran Yan Yaoming Qu Haitao Wen Xia Zou Xinzi Liu Mingjun Lu Jianhua Mo Zhibo Wen |
| author_sort | Andong Ma |
| collection | DOAJ |
| description | Abstract Background 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. Methods This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. Results The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. Conclusions Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG. |
| first_indexed | 2024-04-24T09:48:13Z |
| format | Article |
| id | doaj.art-f0c6fd3ed6404ca589b679497ea5fd89 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-04-24T09:48:13Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-f0c6fd3ed6404ca589b679497ea5fd892024-04-14T11:32:52ZengBMCBMC Medical Imaging1471-23422024-04-0124111110.1186/s12880-024-01262-zAmide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomasAndong Ma0Xinran Yan1Yaoming Qu2Haitao Wen3Xia Zou4Xinzi Liu5Mingjun Lu6Jianhua Mo7Zhibo Wen8Department of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Haizhu DistrictAbstract Background 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. Methods This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. Results The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. Conclusions Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.https://doi.org/10.1186/s12880-024-01262-zLow-grade glioma1p/19q Co-deletionPredictionRadiomicsMagnetic resonance imagingAmide proton transfer weighted imaging |
| spellingShingle | Andong Ma Xinran Yan Yaoming Qu Haitao Wen Xia Zou Xinzi Liu Mingjun Lu Jianhua Mo Zhibo Wen Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas BMC Medical Imaging Low-grade glioma 1p/19q Co-deletion Prediction Radiomics Magnetic resonance imaging Amide proton transfer weighted imaging |
| title | Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas |
| title_full | Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas |
| title_fullStr | Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas |
| title_full_unstemmed | Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas |
| title_short | Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas |
| title_sort | amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p 19q co deletion status in low grade gliomas |
| topic | Low-grade glioma 1p/19q Co-deletion Prediction Radiomics Magnetic resonance imaging Amide proton transfer weighted imaging |
| url | https://doi.org/10.1186/s12880-024-01262-z |
| work_keys_str_mv | AT andongma amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT xinranyan amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT yaomingqu amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT haitaowen amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT xiazou amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT xinziliu amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT mingjunlu amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT jianhuamo amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas AT zhibowen amideprotontransferweightedanddiffusionweightedimagingbasedradiomicsclassificationalgorithmforpredicting1p19qcodeletionstatusinlowgradegliomas |