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Mohammed I, Elbashir MK, Faggad AS. Singular Value Decomposition-Based Penalized Multinomial Regression for Classifying Imbalanced Medulloblastoma Subgroups Using Methylation Data. J Comput Biol 2024; 31:458-471. [PMID: 38752890 DOI: 10.1089/cmb.2023.0198] [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] [Indexed: 05/23/2024] Open
Abstract
Medulloblastoma (MB) is a molecularly heterogeneous brain malignancy with large differences in clinical presentation. According to genomic studies, there are at least four distinct molecular subgroups of MB: sonic hedgehog (SHH), wingless/INT (WNT), Group 3, and Group 4. The treatment and outcomes depend on appropriate classification. It is difficult for the classification algorithms to identify these subgroups from an imbalanced MB genomic data set, where the distribution of samples among the MB subgroups may not be equal. To overcome this problem, we used singular value decomposition (SVD) and group lasso techniques to find DNA methylation probe features that maximize the separation between the different imbalanced MB subgroups. We used multinomial regression as a classification method to classify the four different molecular subgroups of MB using the reduced DNA methylation data. Coordinate descent is used to solve our loss function associated with the group lasso, which promotes sparsity. By using SVD, we were able to reduce the 321,174 probe features to just 200 features. Less than 40 features were successfully selected after applying the group lasso, which we then used as predictors for our classification models. Our proposed method achieved an average overall accuracy of 99% based on fivefold cross-validation technique. Our approach produces improved classification performance compared with the state-of-the-art methods for classifying MB molecular subgroups.
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Affiliation(s)
- Isra Mohammed
- Department of Statistics, Faculty of Mathematical and Computer Sciences, University of Gezira, Wad Madani, Sudan
| | - Murtada K Elbashir
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
- Department of Computer Science, Faculty of Mathematical and Computer Sciences, University of Gezira, Wad Madani, Sudan
| | - Areeg S Faggad
- Department of Molecular Biology, National Cancer Institute-University of Gezira, Wad Madani, Sudan
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Ismail M, Craig S, Ahmed R, de Blank P, Tiwari P. Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors. Diagnostics (Basel) 2023; 13:2727. [PMID: 37685265 PMCID: PMC10487205 DOI: 10.3390/diagnostics13172727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.
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Affiliation(s)
- Marwa Ismail
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
| | - Stephen Craig
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
| | - Raheel Ahmed
- Department of Neurosurgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Peter de Blank
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA;
| | - Pallavi Tiwari
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
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Zhang M, Wong SW, Wright JN, Wagner MW, Toescu S, Han M, Tam LT, Zhou Q, Ahmadian SS, Shpanskaya K, Lummus S, Lai H, Eghbal A, Radmanesh A, Nemelka J, Harward S, Malinzak M, Laughlin S, Perreault S, Braun KRM, Lober RM, Cho YJ, Ertl-Wagner B, Ho CY, Mankad K, Vogel H, Cheshier SH, Jacques TS, Aquilina K, Fisher PG, Taylor M, Poussaint T, Vitanza NA, Grant GA, Pfister S, Thompson E, Jaju A, Ramaswamy V, Yeom KW. MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study. Radiology 2022; 304:406-416. [PMID: 35438562 PMCID: PMC9340239 DOI: 10.1148/radiol.212137] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/09/2021] [Accepted: 02/08/2022] [Indexed: 08/03/2023]
Abstract
Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.
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Pringle C, Kilday JP, Kamaly-Asl I, Stivaros SM. The role of artificial intelligence in paediatric neuroradiology. Pediatr Radiol 2022; 52:2159-2172. [PMID: 35347371 PMCID: PMC9537195 DOI: 10.1007/s00247-022-05322-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/22/2021] [Accepted: 02/11/2022] [Indexed: 01/17/2023]
Abstract
Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.
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Affiliation(s)
- Catherine Pringle
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - John-Paul Kilday
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Ian Kamaly-Asl
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Stavros Michael Stivaros
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK. .,Department of Paediatric Radiology, Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Oxford Road, Manchester, M13 9WL, UK. .,The Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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Iyer S, Ismail M, Tamrazi B, Salloum R, de Blank P, Margol A, Correa R, Chen J, Bera K, Statsevych V, Ho ML, Vaidya P, Verma R, Hawes D, Judkins A, Fu P, Madabhushi A, Tiwari P. Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study. Front Oncol 2022; 12:915143. [PMID: 36620600 PMCID: PMC9811390 DOI: 10.3389/fonc.2022.915143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Medulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT, SHH, Group 3, Group 4), each with distinct clinical behaviors. We hypothesize that (1) aggressive MB tumors, growing heterogeneously, induce pronounced local structural deformations in the surrounding parenchyma, and (b) these local deformations as captured on Gadolinium (Gd)-enhanced-T1w MRI are independently associated with molecular subgroups, as well as overall survival in MB patients. Methods In this work, a total of 88 MB studies from 2 institutions were analyzed. Following tumor delineation, Gd-T1w scan for every patient was registered to a normal age-specific T1w-MRI template via deformable registration. Following patient-atlas registration, local structural deformations in the brain parenchyma were obtained for every patient by computing statistics from deformation magnitudes obtained from every 5mm annular region, 0 < d < 60 mm, where d is the distance from the tumor infiltrating edge. Results Multi-class comparison via ANOVA yielded significant differences between deformation magnitudes obtained for Group 3, Group 4, and SHH molecular subgroups, observed up to 60-mm outside the tumor edge. Additionally, Kaplan-Meier survival analysis showed that the local deformation statistics, combined with the current clinical risk-stratification approaches (molecular subgroup information and Chang's classification), could identify significant differences between high-risk and low-risk survival groups, achieving better performance results than using any of these approaches individually. Discussion These preliminary findings suggest there exists significant association of our tumor-induced deformation descriptor with overall survival in MB, and that there could be an added value in using the proposed radiomic descriptor along with the current risk classification approaches, towards more reliable risk assessment in pediatric MB.
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Affiliation(s)
- Sukanya Iyer
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Marwa Ismail
- Department of Radiology and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Benita Tamrazi
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Ralph Salloum
- Division of Hematology, Oncology & Bone Marrow Transplant, Nationwide Children's Hospital, Columbus, OH, United States
| | - Peter de Blank
- Division of Oncology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Ashley Margol
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Ramon Correa
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Jonathan Chen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Volodymyr Statsevych
- Department of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH, United States
| | - Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Ruchika Verma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Debra Hawes
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Alexander Judkins
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Pingfu Fu
- Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Anant Madabhushi
- Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Pallavi Tiwari
- Department of Radiology and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
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