Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics
PurposeThis study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB.MethodOne hundred and twenty-two pediatric patients (median age 3. 2 years, range, 0.2–9.8 years) with NB were retrospectively enrolled. Significant features by multi...
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.840777/full |
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| author | Luodan Qian Shen Yang Shuxin Zhang Hong Qin Wei Wang Ying Kan Lei Liu Jixia Li Jixia Li Hui Zhang Jigang Yang |
| author_facet | Luodan Qian Shen Yang Shuxin Zhang Hong Qin Wei Wang Ying Kan Lei Liu Jixia Li Jixia Li Hui Zhang Jigang Yang |
| author_sort | Luodan Qian |
| collection | DOAJ |
| description | PurposeThis study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB.MethodOne hundred and twenty-two pediatric patients (median age 3. 2 years, range, 0.2–9.8 years) with NB were retrospectively enrolled. Significant features by multivariable logistic regression were retained to establish a clinical model (C_model), which included clinical characteristics. 18F-FDG PET/CT radiomic features were extracted by Computational Environment for Radiological Research. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomic features and build models (R-model). The predictive performance of models constructed by clinical characteristic (C_model), radiomic signature (R_model), and their combinations (CR_model) were compared using receiver operating curves (ROCs). Nomograms based on the radiomic score (rad-score) and clinical parameters were developed.ResultsThe patients were classified into a training set (n = 86) and a test set (n = 36). Accordingly, 6, 8, and 7 radiomic features were selected to establish R_models for predicting MYCN, 1p and 11q status. The R_models showed a strong power for identifying these aberrations, with area under ROC curves (AUCs) of 0.96, 0.89, and 0.89 in the training set and 0.92, 0.85, and 0.84 in the test set. When combining clinical characteristics and radiomic signature, the AUCs increased to 0.98, 0.91, and 0.93 in the training set and 0.96, 0.88, and 0.89 in the test set. The CR_models had the greatest performance for MYCN, 1p and 11q predictions (P < 0.05).ConclusionsThe pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice. |
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| language | English |
| last_indexed | 2024-12-13T00:58:17Z |
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| spelling | doaj.art-9ac8e7174d664a2084b7699aff087c002022-12-22T00:04:45ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-03-01910.3389/fmed.2022.840777840777Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT RadiomicsLuodan Qian0Shen Yang1Shuxin Zhang2Hong Qin3Wei Wang4Ying Kan5Lei Liu6Jixia Li7Jixia Li8Hui Zhang9Jigang Yang10Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of Surgical Oncology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, ChinaDepartment of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of Surgical Oncology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, ChinaDepartment of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaSinounion Medical Technology (Beijing) Co., Ltd., Beijing, ChinaDepartment of Molecular Medicine and Pathology, School of Medical Science, The University of Auckland, Auckland, New ZealandDepartment of Laboratory Medicine of Medical School, Foshan University, Foshan, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaDepartment of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaPurposeThis study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB.MethodOne hundred and twenty-two pediatric patients (median age 3. 2 years, range, 0.2–9.8 years) with NB were retrospectively enrolled. Significant features by multivariable logistic regression were retained to establish a clinical model (C_model), which included clinical characteristics. 18F-FDG PET/CT radiomic features were extracted by Computational Environment for Radiological Research. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomic features and build models (R-model). The predictive performance of models constructed by clinical characteristic (C_model), radiomic signature (R_model), and their combinations (CR_model) were compared using receiver operating curves (ROCs). Nomograms based on the radiomic score (rad-score) and clinical parameters were developed.ResultsThe patients were classified into a training set (n = 86) and a test set (n = 36). Accordingly, 6, 8, and 7 radiomic features were selected to establish R_models for predicting MYCN, 1p and 11q status. The R_models showed a strong power for identifying these aberrations, with area under ROC curves (AUCs) of 0.96, 0.89, and 0.89 in the training set and 0.92, 0.85, and 0.84 in the test set. When combining clinical characteristics and radiomic signature, the AUCs increased to 0.98, 0.91, and 0.93 in the training set and 0.96, 0.88, and 0.89 in the test set. The CR_models had the greatest performance for MYCN, 1p and 11q predictions (P < 0.05).ConclusionsThe pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice.https://www.frontiersin.org/articles/10.3389/fmed.2022.840777/full18F-FDG PET/CTradiomicsneuroblastomaMYCN amplification1p aberration11q aberration |
| spellingShingle | Luodan Qian Shen Yang Shuxin Zhang Hong Qin Wei Wang Ying Kan Lei Liu Jixia Li Jixia Li Hui Zhang Jigang Yang Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics Frontiers in Medicine 18F-FDG PET/CT radiomics neuroblastoma MYCN amplification 1p aberration 11q aberration |
| title | Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics |
| title_full | Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics |
| title_fullStr | Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics |
| title_full_unstemmed | Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics |
| title_short | Prediction of MYCN Amplification, 1p and 11q Aberrations in Pediatric Neuroblastoma via Pre-therapy 18F-FDG PET/CT Radiomics |
| title_sort | prediction of mycn amplification 1p and 11q aberrations in pediatric neuroblastoma via pre therapy 18f fdg pet ct radiomics |
| topic | 18F-FDG PET/CT radiomics neuroblastoma MYCN amplification 1p aberration 11q aberration |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.840777/full |
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