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Ghosh A, Li H, Towbin AJ, Turpin BK, Trout AT. Histogram Analysis of Apparent Diffusion Coefficient Maps Provides Genotypic and Pretreatment Phenotypic Information in Pediatric and Young Adult Rhabdomyosarcoma. Acad Radiol 2024; 31:2550-2561. [PMID: 38296742 DOI: 10.1016/j.acra.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 02/02/2024]
Abstract
INTRODUCTION We evaluate the role of apparent diffusion coefficient (ADC) histogram metrics in stratifying pediatric and young adult rhabdomyosarcomas. METHODS We retrospectively evaluated baseline diffusion-weighted imaging (DWI) from 38 patients with rhabdomyosarcomas (Not otherwise specified: 2; Embryonal: 21; Spindle Cell: 2; Alveolar: 13, mean ± std dev age: 8.1 ± 7.76 years). The diffusion images were obtained on a wide range of 1.5 T and 3 T scanners at multiple sites. FOXO1 fusion status was available for 35 patients, nine of whom harbored the fusion. 13 patients were TNM stage 1, eight had stage 2 disease, nine were stage 3, and eight had stage 4 disease. 23 patients belonged to Clinical Group III and seven to Group IV, while two and five were CG I and II, respectively. Nine patients were classified as low risk, while 21 and five were classified as intermediate and high risk respectively. Histogram parameters of the apparent diffusion coefficient (ADC) map from the entire tumor were obtained based on manual tumor contouring. A two-tailed Mann-Whitney U test was used for all two-group, and the Kruskal-Wallis's test was used for multiple-group comparisons. Bootstrapped receiver operating characteristic (ROC) curves and areas under the curve (AUC) were generated for the statistically significant histogram parameters to differentiate genotypic and phenotypic parameters. RESULTS Alveolar rhabdomyosarcomas had a statistically significant lower 10th Percentile (586.54 ± 164.52, mean ± std dev, values are in ×10-6mm2/s) than embryonal rhabdomyosarcomas (966.51 ± 481.33) with an AUC of 0.85 (95%CI. 0.73-0.95) for differentiating the two. The 10th percentile was also significantly different between FOXO1 fusion-positive (553.87 ± 187.64) and negative (898.07 ± 449.38) rhabdomyosarcomas with an AUC of 0.83 (95% CI 0.71-0.94). Alveolar rhabdomyosarcomas also had statistically significant lower Mean, Median, and Root Mean Squared ADC histogram values than embryonal rhabdomyosarcomas. Four, five, and seven of the 18 histogram parameters evaluated demonstrated a statistically significant increase with higher TNM stage, clinical group, assignment, and pretreatment risk stratification, respectively. For example, Entropy had an AUC of 0.8 (95% CI. 0.67-0.92) for differentiating TNM stage 1 from ≥ stage 2 and 0.9 (95% CI. 0.8-0.98) for differentiating low from intermediate or high-risk stratification. CONCLUSION Our findings demonstrate the potential of ADC histogram metrics to predict clinically relevant variables for rhabdomyosarcoma, including FOXO1 fusion status, histopathology, Clinical Group, TNM staging, and risk stratification.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
| | - Hailong Li
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Alexander J Towbin
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Brian K Turpin
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA; Division of Oncology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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Jackson K, Packer RJ. Recent Advances in Pediatric Medulloblastoma. Curr Neurol Neurosci Rep 2023; 23:841-848. [PMID: 37943476 PMCID: PMC10724301 DOI: 10.1007/s11910-023-01316-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 11/10/2023]
Abstract
PURPOSE OF REVIEW Review recent advances in the understanding of pediatric medulloblastoma including etiology, biology, radiology, and management of pediatric medulloblastoma. RECENT FINDINGS The classic four subgroups have been reclassified and further subdivided based on new molecular findings. Research is revealing the cell origins of the different subtypes of medulloblastoma. There has been continued personalization of management based on molecular parameters. While many advances have been made in the knowledge base of this most common malignant pediatric brain tumor, there has not yet been translation into more effective therapies to prolong survival in all subgroups with the possible exception of children with group 3 disease. Quality of life remains a major challenge for long-term survivors.
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Affiliation(s)
- Kasey Jackson
- Brain Tumor Institute, Children's National Hospital, Washington D C, USA.
- Division of Hematology and Oncology, Children's National Hospital, Washington D C, USA.
| | - Roger J Packer
- Brain Tumor Institute, Children's National Hospital, Washington D C, USA
- Center for Neuroscience and Behavioral Medicine, Children's National Hospital, Washington D C, USA
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Nabavizadeh A, Barkovich MJ, Mian A, Ngo V, Kazerooni AF, Villanueva-Meyer JE. Current state of pediatric neuro-oncology imaging, challenges and future directions. Neoplasia 2023; 37:100886. [PMID: 36774835 PMCID: PMC9945752 DOI: 10.1016/j.neo.2023.100886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/20/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
Imaging plays a central role in neuro-oncology including primary diagnosis, treatment planning, and surveillance of tumors. The emergence of quantitative imaging and radiomics provided an uprecedented opportunity to compile mineable databases that can be utilized in a variety of applications. In this review, we aim to summarize the current state of conventional and advanced imaging techniques, standardization efforts, fast protocols, contrast and sedation in pediatric neuro-oncologic imaging, radiomics-radiogenomics, multi-omics and molecular imaging approaches. We will also address the existing challenges and discuss future directions.
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Affiliation(s)
- Ali Nabavizadeh
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania, USA; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
| | - Matthew J Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Ali Mian
- Division of Neuroradiology, Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri, USA
| | - Van Ngo
- Saint Louis University School of Medicine, St. Louis, Missouri, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Javier E Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
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Wang Y, Wang L, Qin B, Hu X, Xiao W, Tong Z, Li S, Jing Y, Li L, Zhang Y. Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters. Front Neurosci 2023; 17:1157858. [PMID: 37113160 PMCID: PMC10126354 DOI: 10.3389/fnins.2023.1157858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/27/2023] [Indexed: 04/29/2023] Open
Abstract
Purpose To construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB). Methods The preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models. Results A total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work. Conclusion The combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.
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Affiliation(s)
- Yuanlin Wang
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Longlun Wang
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Bin Qin
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Xihong Hu
- Department of Radiology, Children’s Hospital of Fudan University, Shanghai, China
| | - Wenjiao Xiao
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Zanyong Tong
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Shuang Li
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd., Beijing, China
| | - Lusheng Li
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Neurosurgery, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Lusheng Li,
| | - Yuting Zhang
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Department of Radiology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yuting Zhang,
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