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Zhang Z, Wu Y, Zhao X, Ji W, Li L, Zhai X, Liang P, Cheng Y, Zhou J. Neurosurgical short-term outcomes for pediatric medulloblastoma patients and molecular correlations: a 10-year single-center observation cohort study. Neurosurg Rev 2024; 47:283. [PMID: 38904885 DOI: 10.1007/s10143-024-02526-6] [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: 04/01/2024] [Revised: 05/25/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024]
Abstract
This study examined the risk factors for short-term outcomes, focusing particularly on the associations among molecular subgroups. The analysis focused on the data of pediatric patients with medulloblastoma between 2013 and 2023, as well as operative complications, length of stay from surgery to adjuvant treatment, 30-day unplanned reoperation, unplanned readmission, and mortality. 148 patients were included. Patients with the SHH TP53-wildtype exhibited a lower incidence of complications (45.2% vs. 66.0%, odds ratio [OR] 0.358, 95% confidence interval [CI] 0.160 - 0.802). Female sex (0.437, 0.207 - 0.919) was identified as an independent protective factor for complications, and brainstem involvement (1.900, 1.297 - 2.784) was identified as a risk factor. Surgical time was associated with an increased risk of complications (1.004, 1.001 - 1.008), duration of hospitalization (1.006, 1.003 - 1.010), and reoperation (1.003, 1.001 - 1.006). Age was found to be a predictor of improved outcomes, as each additional year was associated with a 14.1% decrease in the likelihood of experiencing a prolonged length of stay (0.859, 0.772 - 0.956). Patients without metastasis exhibited a reduced risk of reoperation (0.322, 0.133 - 0.784) and readmission (0.208, 0.074 - 0.581). There is a significant degree of variability in the occurrence of operative complications in pediatric patients with medulloblastoma. SHH TP53-wildtype medulloblastoma is commonly correlated with a decreased incidence of complications. The short-term outcomes of patients are influenced by various unmodifiable endogenous factors. These findings could enhance the knowledge of onconeurosurgeons and alleviate the challenges associated with patient/parent education through personalized risk communication. However, the importance of a dedicated center with expertise surgical team and experienced neurosurgeon in improving neurosurgical outcomes appears self-evident.
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Affiliation(s)
- Zaiyu Zhang
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Yuxin Wu
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Xueling Zhao
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Wenyuan Ji
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Lusheng Li
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Xuan Zhai
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Ping Liang
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Yuan Cheng
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jianjun Zhou
- Department of Neurosurgery, Children's Hospital of Chongqing Medical University, Chongqing, China.
- National Clinical Research Center for Child Health and Disorders, Chongqing, China.
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
- Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China.
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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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Nguyen TM, Bertolus C, Giraud P, Burgun A, Saintigny P, Bibault JE, Foy JP. A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas. Cancers (Basel) 2023; 15:5369. [PMID: 38001629 PMCID: PMC10670096 DOI: 10.3390/cancers15225369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/05/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. METHOD We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. RESULTS A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. CONCLUSIONS AND RELEVANCE We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.
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Affiliation(s)
- Tan Mai Nguyen
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
| | - Chloé Bertolus
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
| | - Paul Giraud
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
- Sorbonne Université, Department of Radiation Oncology, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France
| | - Anita Burgun
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
| | - Pierre Saintigny
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
- Department of Medical Oncology, Centre Léon Bérard, 69008 Lyon, France
| | - Jean-Emmanuel Bibault
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
- Department of Radiation Oncology, Hôpital Européen Georges-Pompidou, Université Paris Cité, 75015 Paris, France
| | - Jean-Philippe Foy
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Sorbonne Université, INSERM UMRS 938, Centre de Recherche de Saint Antoine, Team Cancer Biology and Therapeutics, 75011 Paris, France
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Ciobanu-Caraus O, Czech T, Peyrl A, Haberler C, Kasprian G, Furtner J, Kool M, Sill M, Frischer JM, Cho A, Slavc I, Rössler K, Gojo J, Dorfer C. The Site of Origin of Medulloblastoma: Surgical Observations Correlated to Molecular Groups. Cancers (Basel) 2023; 15:4877. [PMID: 37835571 PMCID: PMC10571892 DOI: 10.3390/cancers15194877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Developmental gene expression data from medulloblastoma (MB) suggest that WNT-MB originates from the region of the embryonic lower rhombic lip (LRL), whereas SHH-MB and non-WNT/non-SHH MB arise from cerebellar precursor matrix regions. This study aimed to analyze detailed intraoperative data with regard to the site of origin (STO) and compare these findings with the hypothesized regions of origin associated with the molecular group. A review of the institutional database identified 58 out of 72 pediatric patients who were operated for an MB at our department between 1996 and 2020 that had a detailed operative report and a surgical video as well as clinical and genetic classification data available for analysis. The STO was assessed based on intraoperative findings. Using the intraoperatively defined STO, "correct" prediction of molecular groups was feasible in 20% of WNT-MB, 60% of SHH-MB and 71% of non-WNT/non-SHH MB. The positive predictive values of the neurosurgical inspection to detect the molecular group were 0.21 (95% CI 0.08-0.48) for WNT-MB, 0.86 (95% CI 0.49-0.97) for SHH-MB and 0.73 (95% CI 0.57-0.85) for non-WNT/non-SHH MB. The present study demonstrated a limited predictive value of the intraoperatively observed STO for the prediction of the molecular group of MB.
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Affiliation(s)
- Olga Ciobanu-Caraus
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria (T.C.); (A.C.)
| | - Thomas Czech
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria (T.C.); (A.C.)
| | - Andreas Peyrl
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria (I.S.)
- Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Christine Haberler
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Gregor Kasprian
- Department of Radiology, Medical University of Vienna, 1090 Vienna, Austria; (G.K.); (J.F.)
| | - Julia Furtner
- Department of Radiology, Medical University of Vienna, 1090 Vienna, Austria; (G.K.); (J.F.)
| | - Marcel Kool
- Hopp Children’s Cancer Center (KiTZ), 69120 Heidelberg, Germany
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Princess Máxima Center for Pediatric Oncology, 3584 Utrecht, The Netherlands
| | - Martin Sill
- Hopp Children’s Cancer Center (KiTZ), 69120 Heidelberg, Germany
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Josa M. Frischer
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria (T.C.); (A.C.)
| | - Anna Cho
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria (T.C.); (A.C.)
| | - Irene Slavc
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria (I.S.)
- Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Karl Rössler
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria (T.C.); (A.C.)
- Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Johannes Gojo
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria (I.S.)
- Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Christian Dorfer
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria (T.C.); (A.C.)
- Comprehensive Center for Pediatrics and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
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Tsang B, Gupta A, Takahashi MS, Baffi H, Ola T, Doria AS. Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment. Jpn J Radiol 2023; 41:1127-1147. [PMID: 37395982 DOI: 10.1007/s11604-023-01437-8] [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: 10/05/2022] [Accepted: 04/18/2023] [Indexed: 07/04/2023]
Abstract
PURPOSES To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines. MATERIALS AND METHODS A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients. RESULTS Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%-73%) CLAIM items reported. Adherence has improved over time based on publication year. CONCLUSION The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.
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Affiliation(s)
- Brian Tsang
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Aaryan Gupta
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Marcelo Straus Takahashi
- Instituto de Radiologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (InRad/HC-FMUSP), São Paulo, SP, Brazil
- Instituto da Criança do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (ICr/HC-FMUSP), São Paulo, SP, Brazil
- DasaInova, Diagnósticos da América SA (Dasa), São Paulo, SP, Brazil
| | | | - Tolulope Ola
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Andrea S Doria
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
- Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
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Albalkhi I, Bhatia A, Lösch N, Goetti R, Mankad K. Current state of radiomics in pediatric neuro-oncology practice: a systematic review. Pediatr Radiol 2023; 53:2079-2091. [PMID: 37195305 DOI: 10.1007/s00247-023-05679-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Radiomics is the process of converting radiological images into high-dimensional data that may be used to create machine learning models capable of predicting clinical outcomes, such as disease progression, treatment response and survival. Pediatric central nervous system (CNS) tumors differ from adult CNS tumors in terms of their tissue morphology, molecular subtype and textural features. We set out to appraise the current impact of this technology in clinical pediatric neuro-oncology practice. OBJECTIVES The aims of the study were to assess radiomics' current impact and potential utility in pediatric neuro-oncology practice; to evaluate the accuracy of radiomics-based machine learning models and compare this to the current standard which is stereotactic brain biopsy; and finally, to identify the current limitations of radiomics applications in pediatric neuro-oncology. MATERIALS AND METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, a systematic review of the literature was carried out with protocol number CRD42022372485 in the prospective register of systematic reviews (PROSPERO). We performed a systematic literature search via PubMed, Embase, Web of Science and Google Scholar. Studies involving CNS tumors, studies that utilized radiomics and studies involving pediatric patients (age<18 years) were included. Several parameters were collected including imaging modality, sample size, image segmentation technique, machine learning model used, tumor type, radiomics utility, model accuracy, radiomics quality score and reported limitations. RESULTS The study included a total of 17 articles that underwent full-text review, after excluding duplicates, conference abstracts and studies that did not meet the inclusion criteria. The most commonly used machine learning models were support vector machines (n=7) and random forests (n=6), with an area under the curve (AUC) range of 0.60-0.94. The included studies investigated several pediatric CNS tumors, with ependymoma and medulloblastoma being the most frequently studied. Radiomics was primarily used for lesion identification, molecular subtyping, survival prognostication and metastasis prediction in pediatric neuro-oncology. The low sample size of studies was a commonly reported limitation. CONCLUSION The current state of radiomics in pediatric neuro-oncology is promising, in terms of distinguishing between tumor types; however, its utility in response assessment requires further evaluation which, given the relatively low number of pediatric tumors, calls for multicenter collaboration.
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Affiliation(s)
- Ibrahem Albalkhi
- College of Medicine Research Lab, Alfaisal University, Riyadh, KSA, Saudi Arabia.
- Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St, London, WC1N 3JH, UK.
| | - Aashim Bhatia
- Department of Neuroradiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nico Lösch
- Biomedical Data Science Lab, University of Technology Sydney, Ultimo, Australia
| | - Robert Goetti
- Department of Medical Imaging, The Children's Hospital at Westmead, University of Sydney, Sydney, Australia
| | - Kshitij Mankad
- Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St, London, WC1N 3JH, UK
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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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8
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Ntenti C, Lallas K, Papazisis G. Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand? Diagnostics (Basel) 2023; 13:diagnostics13111915. [PMID: 37296767 DOI: 10.3390/diagnostics13111915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/26/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas.
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Affiliation(s)
- Charikleia Ntenti
- First Department of Pharmacology, School of Medicine, Aristotle University of Thessaloniki, 54621 Thessaloniki, Greece
| | - Konstantinos Lallas
- Department of Medical Oncology, School of Medicine, Aristotle University of Thessaloniki, 54621 Thessaloniki, Greece
| | - Georgios Papazisis
- Clinical Research Unit, Special Unit for Biomedical Research and Education (BRESU), School of Medicine, Aristotle University of Thessaloniki, 54621 Thessaloniki, Greece
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Karabacak M, Ozkara BB, Ozturk A, Kaya B, Cirak Z, Orak E, Ozcan Z. Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance. Acta Radiol 2023; 64:1994-2003. [PMID: 36510435 DOI: 10.1177/02841851221143496] [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: 12/15/2022]
Abstract
BACKGROUND Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. PURPOSE To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. MATERIAL AND METHODS A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. RESULTS Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. CONCLUSION Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.
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Affiliation(s)
- Mert Karabacak
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Fatih, Istanbul, Turkey
| | - Burak Berksu Ozkara
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Fatih, Istanbul, Turkey
| | - Admir Ozturk
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Fatih, Istanbul, Turkey
| | - Busra Kaya
- Faculty of Medicine, Istanbul Altinbas University, Bakirkoy, Istanbul, Turkey
| | - Zeynep Cirak
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Fatih, Istanbul, Turkey
| | - Ece Orak
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Fatih, Istanbul, Turkey
| | - Zeynep Ozcan
- Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Fatih, Istanbul, Turkey
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10
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Steyaert S, Pizurica M, Nagaraj D, Khandelwal P, Hernandez-Boussard T, Gentles AJ, Gevaert O. Multimodal data fusion for cancer biomarker discovery with deep learning. NAT MACH INTELL 2023; 5:351-362. [PMID: 37693852 PMCID: PMC10484010 DOI: 10.1038/s42256-023-00633-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/17/2023] [Indexed: 09/12/2023]
Abstract
Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.
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Affiliation(s)
- Sandra Steyaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
| | - Marija Pizurica
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
| | | | | | - Tina Hernandez-Boussard
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
| | - Andrew J Gentles
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
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11
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Changes to pediatric brain tumors in 2021 World Health Organization classification of tumors of the central nervous system. Pediatr Radiol 2023; 53:523-543. [PMID: 36348014 DOI: 10.1007/s00247-022-05546-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/12/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022]
Abstract
New tumor types are continuously being described with advances in molecular testing and genomic analysis resulting in better prognostics, new targeted therapy options and improved patient outcomes. As a result of these advances, pathological classification of tumors is periodically updated with new editions of the World Health Organization (WHO) Classification of Tumors books. In 2021, WHO Classification of Tumors of the Central Nervous System, 5th edition (CNS5), was published with major changes in pediatric brain tumors officially recognized including pediatric gliomas being separated from adult gliomas, ependymomas being categorized based on anatomical compartment and many new tumor types, most of them seen in children. Additional general changes, such as tumor grading now being done within tumor types rather than across entities and changes in definition of glioblastoma, are also relevant to pediatric neuro-oncology practice. The purpose of this manuscript is to highlight the major changes in pediatric brain tumors in CNS5 most relevant to radiologists. Additionally, brief descriptions of newly recognized entities will be presented with a focus on imaging findings.
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12
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Hartmann K, Sadée CY, Satwah I, Carrillo-Perez F, Gevaert O. Imaging genomics: data fusion in uncovering disease heritability. Trends Mol Med 2023; 29:141-151. [PMID: 36470817 PMCID: PMC10507799 DOI: 10.1016/j.molmed.2022.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/28/2022] [Accepted: 11/03/2022] [Indexed: 12/04/2022]
Abstract
Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.
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Affiliation(s)
- Katherine Hartmann
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | - Christoph Y Sadée
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Ishan Satwah
- College of Medicine, Drexel University, Philadelphia, PA, USA
| | - Francisco Carrillo-Perez
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Granada, Spain
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
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13
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Familiar AM, Mahtabfar A, Fathi Kazerooni A, Kiani M, Vossough A, Viaene A, Storm PB, Resnick AC, Nabavizadeh A. Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges. Neurooncol Adv 2023; 5:vdad119. [PMID: 37841693 PMCID: PMC10576517 DOI: 10.1093/noajnl/vdad119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023] Open
Abstract
With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models.
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Affiliation(s)
- Ariana M Familiar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Aria Mahtabfar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mahsa Kiani
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Angela Viaene
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip B Storm
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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14
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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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15
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Zheng H, Li J, Liu H, Ting G, Yin Q, Li R, Liu M, Zhang Y, Duan S, Li Y, Wang D. MRI
Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional
MR
Imaging Features. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Hui Zheng
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Huanhuan Liu
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Gui Ting
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Qiufeng Yin
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Rui Li
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Ming Liu
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Yuzhen Zhang
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | | | - Yuhua Li
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
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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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2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist. Neuroradiology 2022; 64:1919-1950. [DOI: 10.1007/s00234-022-03008-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/01/2022] [Indexed: 10/17/2022]
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Advanced Neuroimaging Approaches to Pediatric Brain Tumors. Cancers (Basel) 2022; 14:cancers14143401. [PMID: 35884462 PMCID: PMC9318188 DOI: 10.3390/cancers14143401] [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: 06/26/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors. Abstract Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.
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Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
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Saju AC, Chatterjee A, Sahu A, Gupta T, Krishnatry R, Mokal S, Sahay A, Epari S, Prasad M, Chinnaswamy G, Agarwal JP, Goda JS. Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics. Br J Radiol 2022; 95:20211359. [DOI: 10.1259/bjr.20211359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Objective: Image-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR–Texture analysis. Methods: Thirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model. Results: A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively. Conclusion: Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs. Advances in knowledge: Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.
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Affiliation(s)
- Ann Christy Saju
- Department of Radiation Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Abhishek Chatterjee
- Department of Radiation Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Arpita Sahu
- Department of Radiodiagnosis, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Tejpal Gupta
- Department of Radiation Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Rahul Krishnatry
- Department of Radiation Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Smruti Mokal
- Clinical Research Secretariat, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Ayushi Sahay
- Department of Pathology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Sridhar Epari
- Department of Pathology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Maya Prasad
- Department of Pediatric Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Girish Chinnaswamy
- Department of Pediatric Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Jai Prakash Agarwal
- Department of Radiation Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
| | - Jayant S Goda
- Department of Radiation Oncology, Tata Memorial Center, Mumbai, Maharashtra, India
- Homi Bhabha National Institute, Mumbai, Maharashtra, India
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Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma. Childs Nerv Syst 2022; 38:1085-1094. [PMID: 35394210 DOI: 10.1007/s00381-022-05507-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 03/18/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE To develop and validate a radiomics signature for progression-free survival (PFS) and radiotherapeutic benefits in pediatric medulloblastoma. MATERIALS AND METHODS We retrospectively enrolled 253 consecutive children with medulloblastoma from two hospitals. A total of 1294 radiomic features were extracted from the region of tumor on the T1-weighted and contrast-enhanced T1-weighted (CE-T1w) MRI. Radiomic feature selection and machine learning modelling were performed to build radiomics signature for the prediction of PFS on the training set. Moreover, the prognostic performance of the clinical parameters was investigated for PFS. The Concordance index (a value of 0.5 indicates no predictive discrimination, and a value of 1 indicates perfect predictive discrimination) was used to measure and compare the prognostic performance of these models. RESULTS The radiomics signature for the prediction of the PFS yielded Concordance indices of 0.711, 0.707, and 0.717 on the training and held-out test sets 1 and 2, respectively. The radiomics nomogram integrating the radiomics signature, age, and metastasis performed better than the nomogram incorporating only clinicopathological factors (C-index, 0.723 vs. 0.665 and 0.722 vs. 0.677 on the held-out test sets 1 and 2, respectively), which was also validated by the good calibration and decision curve analysis. Further analysis demonstrated that patients with lower value of radiomics signature were associated with better clinical outcomes after postoperative radiotherapy (p < 0.001). CONCLUSION The radiomics signature and nomogram performed well for the prediction of PFS and could stratify patients underwent postoperative radiotherapy into the high- and low-risk groups with significantly different clinical outcomes.
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Vagvala S, Guenette JP, Jaimes C, Huang RY. Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics. Cancer Imaging 2022; 22:19. [PMID: 35436952 PMCID: PMC9014574 DOI: 10.1186/s40644-022-00455-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/29/2022] [Indexed: 01/12/2023] Open
Abstract
Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.
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Affiliation(s)
- Saivenkat Vagvala
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA
| | - Jeffrey P Guenette
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA
| | - Camilo Jaimes
- Division of Neuroradiology, Boston Children's, 300 Longwood Ave., 2nd floor, Main Building, Boston, MA, 02115, USA
| | - Raymond Y Huang
- Division of Neuroradiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis St, Boston, MA, 02115, USA.
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23
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MR Imaging of Pediatric Brain Tumors. Diagnostics (Basel) 2022; 12:diagnostics12040961. [PMID: 35454009 PMCID: PMC9029699 DOI: 10.3390/diagnostics12040961] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/02/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023] Open
Abstract
Primary brain tumors are the most common solid neoplasms in children and a leading cause of mortality in this population. MRI plays a central role in the diagnosis, characterization, treatment planning, and disease surveillance of intracranial tumors. The purpose of this review is to provide an overview of imaging methodology, including conventional and advanced MRI techniques, and illustrate the MRI appearances of common pediatric brain tumors.
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24
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Cui Z, Ren G, Cai R, Wu C, Shi H, Wang X, Zhu M. MRI-based texture analysis for differentiate between pediatric posterior fossa ependymoma type A and B. Eur J Radiol 2022; 152:110288. [DOI: 10.1016/j.ejrad.2022.110288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/01/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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25
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Multidisciplinary Management of Medulloblastoma: Consensus, Challenges, and Controversies. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2423:215-235. [PMID: 34978701 DOI: 10.1007/978-1-0716-1952-0_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Medulloblastoma is a highly aggressive "small round blue cell tumor" of the posterior fossa predominantly seen in children. Historically aggressive multimodality regimens have achieved encouraging outcomes with the caveat of severe long-term toxicities. The last decade has unleashed a revolution in terms of evolved understanding of this heterogeneous disease entity in terms of molecular biology. Medulloblastoma as of today is grouped into one of four canonical molecular subgroups (WNT, SHH, Group 3, and Group 4) each characterized by different putative cells of origin, characteristic aberrations at the molecular level, radiogenomics, and outcomes. Our understanding continues to grow in this regard. The future promises much in terms of personalized medicine in tailoring therapy to the needs of individual patients based on their clinical and molecular profile in order to maximize individual and population based outcomes at the cost of minimizing toxicity.
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26
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Valvi S, Hansford JR. Radiomics-A new age of presurgical assessment to improve outcomes in pediatric neuro-oncology. Neuro Oncol 2022; 24:995-996. [PMID: 35171286 PMCID: PMC9159459 DOI: 10.1093/neuonc/noac046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Santosh Valvi
- Department of Paediatric and Adolescent Oncology/Haematology, Perth Children’s Hospital, Nedlands, West Australia, Australia,Brain Tumour Research Laboratory, Telethon Kids Institute, Nedlands, West Australia, Australia,Division of Paediatrics, University of Western Australia Medical School, Nedlands, West Australia, Australia
| | - Jordan R Hansford
- Corresponding Author: Jordan R. Hansford, BScH, MSc, MBBS, FRACP, Michael Rice Cancer Centre, Women’s and Children’s Hospital, 72 King William Rd, North Adelaide, SA 5006, Australia ()
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27
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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28
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Watal P, Patel RP, Chandra T. Pearls and Pitfalls of Imaging in Pediatric Brain Tumors. Semin Ultrasound CT MR 2022; 43:31-46. [PMID: 35164908 DOI: 10.1053/j.sult.2021.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The central nervous system (CNS) tumors constitute the most common type of solid tumors in the pediatric population. The cerebral and cerebellar parenchyma are the most common site of pediatric CNS neoplasms. Imaging plays an important role in detection, characterization, staging and prognostication of brain tumors. The focus of the current article is pediatric brain tumor imaging with emphasis on pearls and pitfalls of conventional and advanced imaging in various pediatric brain tumor subtypes. The article also elucidates changes in brain tumor terms and entities as applicable to pediatric patients, updated as per World Health Organization (WHO) 2016 classification of primary CNS tumors. This classification introduced the genetic and/or molecular information of primary CNS neoplasms as part of comprehensive tumor pathology report in the routine clinical workflow. The concepts from 2016 classification have been further refined based on current research, by the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) group and published in the form of updates. The updates serve as guidelines in the time interval between WHO updates and expect to be broadly adopted in the subsequent WHO classification. The current review covers most pediatric brain tumors except pituitary tumors, meningeal origin tumors, nerve sheath tumors and CNS lymphoma/leukemia.
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Affiliation(s)
- Pankaj Watal
- University of Central Florida College of Medicine and Nemours Children's Hospital, Orlando, FL.
| | - Rajan P Patel
- Section of Neuroradiology, Department of Diagnostic and Interventional Imaging The University of Texas Health Sciences Center at Houston, TX
| | - Tushar Chandra
- University of Central Florida College of Medicine and Nemours Children's Hospital, Orlando, FL
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29
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Prognostic impact of semantic MRI features on survival outcomes in molecularly subtyped medulloblastoma. Strahlenther Onkol 2022; 198:291-303. [DOI: 10.1007/s00066-021-01889-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/28/2021] [Indexed: 10/19/2022]
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30
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Zhou L, Peng H, Ji Q, Li B, Pan L, Chen F, Jiao Z, Wang Y, Huang M, Liu G, Liu Y, Li W. Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma. ANNALS OF TRANSLATIONAL MEDICINE 2022; 9:1665. [PMID: 34988174 PMCID: PMC8667089 DOI: 10.21037/atm-21-5348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/10/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.
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Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bo Li
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Lexin Pan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Yali Wang
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mengqian Huang
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Gaifen Liu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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31
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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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32
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Dasgupta A, Maitre M, Pungavkar S, Gupta T. Magnetic Resonance Imaging in the Contemporary Management of Medulloblastoma: Current and Emerging Applications. Methods Mol Biol 2022; 2423:187-214. [PMID: 34978700 DOI: 10.1007/978-1-0716-1952-0_18] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Medulloblastoma, the most common malignant primary brain tumor in children, is now considered to comprise of four distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 medulloblastoma, each associated with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical behavior. Due to its exquisite anatomic resolution, multiparametric nature, and ability to image the entire craniospinal axis, magnetic resonance imaging (MRI) is the preferred and recommended first-line imaging modality for suspected brain tumors including medulloblastoma. Preoperative MRI can reliably differentiate medulloblastoma from other common childhood posterior fossa masses such as ependymoma, pilocytic astrocytoma, and brainstem glioma. On T1-weighted images, medulloblastoma is generally iso- to hypointense, while on T2-weighted images, the densely packed cellular component of the tumor is significantly hypointense and displays restricted diffusion on diffusion-weighted imaging. Following intravenous gadolinium, medulloblastoma shows significant but variable and heterogeneous contrast enhancement. Given the propensity of neuraxial spread in medulloblastoma, sagittal fat-suppressed T1-postcontrast spinal MRI is recommended to rule out leptomeningeal metastases for accurate staging. Following neurosurgical excision, postoperative MRI done within 24-48 h confirms the extent of resection, accurately quantifying residual tumor burden imperative for risk assignment. Post-treatment MRI is needed to assess response and effectiveness of adjuvant radiotherapy and systemic chemotherapy. After completion of planned therapy, surveillance MRI is recommended periodically on follow-up for early detection of recurrence for timely institution of salvage therapy, as well as for monitoring treatment-related late complications. Recent studies suggest that preoperative MRI can reliably identify SHH and Group 4 medulloblastoma but has suboptimal predictive accuracy for WNT and Group 3 tumors. In this review, we focus on the role of MRI in the diagnosis, staging, and quantifying residual disease; post-treatment response assessment; and periodic surveillance, and provide a brief summary on radiogenomics in the contemporary management of medulloblastoma.
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Affiliation(s)
- Archya Dasgupta
- Department of Radiation Oncology, Neuro-Oncology Disease Management Group, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India.
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
| | - Madan Maitre
- Department of Radiation Oncology, Neuro-Oncology Disease Management Group, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Sona Pungavkar
- Department of Radiodiagnosis and Imaging, Global Hospitals, Mumbai, India
| | - Tejpal Gupta
- Department of Radiation Oncology, Neuro-Oncology Disease Management Group, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Mumbai, India
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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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Madhogarhia R, Haldar D, Bagheri S, Familiar A, Anderson H, Arif S, Vossough A, Storm P, Resnick A, Davatzikos C, Fathi Kazerooni A, Nabavizadeh A. Radiomics and radiogenomics in pediatric neuro-oncology: A review. Neurooncol Adv 2022; 4:vdac083. [PMID: 35795472 PMCID: PMC9252112 DOI: 10.1093/noajnl/vdac083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.
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Affiliation(s)
- Rachel Madhogarhia
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Debanjan Haldar
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Hannah Anderson
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sherjeel Arif
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Phillip Storm
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adam Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Ramesh S, Chokkara S, Shen T, Major A, Volchenboum SL, Mayampurath A, Applebaum MA. Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review. JCO Clin Cancer Inform 2021; 5:1208-1219. [PMID: 34910588 DOI: 10.1200/cci.21.00102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field. METHODS We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed. RESULTS A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts. CONCLUSION We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.
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Affiliation(s)
- Siddhi Ramesh
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Sukarn Chokkara
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Timothy Shen
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Ajay Major
- Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Samuel L Volchenboum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Anoop Mayampurath
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Mark A Applebaum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
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Sabin ND, Hwang SN, Klimo P, Chambwe N, Tatevossian RG, Patni T, Li Y, Boop FA, Anderson E, Gajjar A, Merchant TE, Ellison DW. Anatomic Neuroimaging Characteristics of Posterior Fossa Type A Ependymoma Subgroups. AJNR Am J Neuroradiol 2021; 42:2245-2250. [PMID: 34674998 DOI: 10.3174/ajnr.a7322] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 08/09/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Posterior fossa type A (PFA) ependymomas have 2 molecular subgroups (PFA-1 and PFA-2) and 9 subtypes. Gene expression profiling suggests that PFA-1 and PFA-2 tumors have distinct developmental origins at different rostrocaudal levels of the brainstem. We, therefore, tested the hypothesis that PFA-1 and PFA-2 ependymomas have different anatomic MR imaging characteristics at presentation. MATERIALS AND METHODS Two neuroradiologists reviewed the preoperative MR imaging examinations of 122 patients with PFA ependymomas and identified several anatomic characteristics, including extension through the fourth ventricular foramina and encasement of major arteries and tumor type (midfloor, roof, or lateral). Deoxyribonucleic acid methylation profiling assigned ependymomas to PFA-1 or PFA-2. Information on PFA subtype from an earlier study was also available for a subset of tumors. Associations between imaging variables and subgroup or subtype were evaluated. RESULTS No anatomic imaging variable was significantly associated with the PFA subgroup, but 5 PFA-2c subtype ependymomas in the cohort had a more circumscribed appearance and showed less tendency to extend through the fourth ventricular foramina or encase blood vessels, compared with other PFA subtypes. CONCLUSIONS PFA-1 and PFA-2 ependymomas did not have different anatomic MR imaging characteristics, and these results do not support the hypothesis that they have distinct anatomic origins. PFA-2c ependymomas appear to have a more anatomically circumscribed MR imaging appearance than the other PFA subtypes; however, this needs to be confirmed in a larger study.
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Affiliation(s)
- N D Sabin
- From the Departments of Diagnostic Imaging (N.D.S., S.N.H., E.A.)
| | - S N Hwang
- From the Departments of Diagnostic Imaging (N.D.S., S.N.H., E.A.)
| | - P Klimo
- Surgery (P.K., F.A.B.,), St. Jude Children's Research Hospital, Memphis, Tennessee
- Semmes Murphey (P.K., F.A.B.), Memphis, Tennessee
| | | | | | | | - Y Li
- Biostatistics (T.P., Y.L.)
| | - F A Boop
- Surgery (P.K., F.A.B.,), St. Jude Children's Research Hospital, Memphis, Tennessee
- Semmes Murphey (P.K., F.A.B.), Memphis, Tennessee
| | - E Anderson
- From the Departments of Diagnostic Imaging (N.D.S., S.N.H., E.A.)
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Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
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Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
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Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021; 43:792-801. [PMID: 34649914 DOI: 10.3174/ajnr.a7297] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.
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Affiliation(s)
- M Ak
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S A Toll
- Department of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan
| | - K Z Hein
- Department of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - R R Colen
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S Khatua
- Department of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota.
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Huang J, Shlobin NA, Lam SK, DeCuypere M. Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review. World Neurosurg 2021; 157:99-105. [PMID: 34648981 DOI: 10.1016/j.wneu.2021.10.068] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 10/04/2021] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. METHODS PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. RESULTS Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. CONCLUSIONS AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.
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Affiliation(s)
- Jonathan Huang
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA
| | - Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA
| | - Sandi K Lam
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA
| | - Michael DeCuypere
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Division of Pediatric Neurosurgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois, USA.
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Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma. PLoS One 2021; 16:e0255500. [PMID: 34324588 PMCID: PMC8321137 DOI: 10.1371/journal.pone.0255500] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/17/2021] [Indexed: 01/06/2023] Open
Abstract
Purpose Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. Material and methods Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. Results Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature—Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). Conclusion The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.
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Ahn SS, Cha S. Pre- and Post-Treatment Imaging of Primary Central Nervous System Tumors in the Molecular and Genetic Era. Korean J Radiol 2021; 22:1858-1874. [PMID: 34402244 PMCID: PMC8546137 DOI: 10.3348/kjr.2020.1450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022] Open
Abstract
Recent advances in the molecular and genetic characterization of central nervous system (CNS) tumors have ushered in a new era of tumor classification, diagnosis, and prognostic assessment. In this emerging and rapidly evolving molecular genetic era, imaging plays a critical role in the preoperative diagnosis and surgical planning, molecular marker prediction, targeted treatment planning, and post-therapy assessment of CNS tumors. This review provides an overview of the current imaging methods relevant to the molecular genetic classification of CNS tumors. Specifically, we focused on 1) the correlates between imaging features and specific molecular genetic markers and 2) the post-therapy imaging used for therapeutic assessment.
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Affiliation(s)
- Sung Soo Ahn
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
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Abstract
Die Radiologie ist von stetem Wandel geprägt und definiert sich über den technologischen Fortschritt. Künstliche Intelligenz (KI) wird die praktische Tätigkeit in der Kinder- und Jugendradiologie künftig in allen Belangen verändern. Bildakquisition, Befunderkennung und -segmentierung sowie die Erkennung von Gewebeeigenschaften und deren Kombination mit Big Data werden die Haupteinsatzgebiete in der Radiologie sein. Höhere Effektivität, Beschleunigung von Untersuchung und Befundung sowie Kosteneinsparung sind mit der Anwendung von KI verbundene Erwartungshaltungen. Ein verbessertes Patientenmanagement, Arbeitserleichterungen für medizinisch-technische Radiologieassistenten und Kinder- und Jugendradiologen sowie schnellere Untersuchungs- und Befundzeiten markieren die Meilensteine der KI-Entwicklung in der Radiologie. Von der Terminkommunikation und Gerätesteuerung bis zu Therapieempfehlung und -monitoring wird der Alltag durch Elemente der KI verändert. Kinder- und Jugendradiologen müssen daher grundlegend über KI informiert sein und mit Datenwissenschaftlern bei der Etablierung und Anwendung von KI-Elementen zusammenarbeiten.
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Attallah O. CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes. Front Neuroinform 2021; 15:663592. [PMID: 34122031 PMCID: PMC8193683 DOI: 10.3389/fninf.2021.663592] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 04/26/2021] [Indexed: 12/28/2022] Open
Abstract
Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
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Chen X, Wang H, Huang K, Liu H, Ding H, Zhang L, Zhang T, Yu W, He L. CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma. Front Oncol 2021; 11:687884. [PMID: 34109133 PMCID: PMC8181422 DOI: 10.3389/fonc.2021.687884] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/03/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose MYCN amplification plays a critical role in defining high-risk subgroup of patients with neuroblastoma. We aimed to develop and validate the CT-based machine learning models for predicting MYCN amplification in pediatric abdominal neuroblastoma. Methods A total of 172 patients with MYCN amplified (n = 47) and non-amplified (n = 125) were enrolled. The cohort was randomly stratified sampling into training and testing groups. Clinicopathological parameters and radiographic features were selected to construct the clinical predictive model. The regions of interest (ROIs) were segmented on three-phrase CT images to extract first-, second- and higher-order radiomics features. The ICCs, mRMR and LASSO methods were used for dimensionality reduction. The selected features from the training group were used to establish radiomics models using Logistic regression, Support Vector Machine (SVM), Bayes and Random Forest methods. The performance of four different radiomics models was evaluated according to the area under the receiver operator characteristic (ROC) curve (AUC), and then compared by Delong test. The nomogram incorporated of clinicopathological parameters, radiographic features and radiomics signature was developed through multivariate logistic regression. Finally, the predictive performance of the clinical model, radiomics models, and nomogram was evaluated in both training and testing groups. Results In total, 1,218 radiomics features were extracted from the ROIs on three-phrase CT images, and then 14 optimal features, including one original first-order feature and eight wavelet-transformed features and five LoG-transformed features, were identified and selected to construct the radiomics models. In the training group, the AUC of the Logistic, SVM, Bayes and Random Forest model was 0.940, 0.940, 0.780 and 0.927, respectively, and the corresponding AUC in the testing group was 0.909, 0.909, 0.729, 0.851, respectively. There was no significant difference among the Logistic, SVM and Random Forest model, but all better than the Bayes model (p <0.005). The predictive performance of the Logistic radiomics model based on three-phrase is similar to nomogram, but both better than the clinical model and radiomics model based on single venous phase. Conclusion The CT-based radiomics signature is able to predict MYCN amplification of pediatric abdominal NB with high accuracy based on SVM, Logistic and Random Forest classifiers, while Bayes classifier yields lower predictive performance. When combined with clinical and radiographic qualitative features, the clinics-radiomics nomogram can improve the performance of predicting MYCN amplification.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Kaiping Huang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | | | - Hao Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ting Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Wenqing Yu
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging. AJR Am J Roentgenol 2021; 217:3-15. [PMID: 33502214 DOI: 10.2214/ajr.20.25246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.
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Alves CAPF, Löbel U, Martin-Saavedra JS, Toescu S, Tsunemi MH, Teixeira SR, Mankad K, Hargrave D, Jacques TS, da Costa Leite C, Gonçalves FG, Vossough A, D'Arco F. A Diagnostic Algorithm for Posterior Fossa Tumors in Children: A Validation Study. AJNR Am J Neuroradiol 2021; 42:961-968. [PMID: 33664107 DOI: 10.3174/ajnr.a7057] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 11/23/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE Primary posterior fossa tumors comprise a large group of neoplasias with variable aggressiveness and short and long-term outcomes. This study aimed to validate the clinical usefulness of a radiologic decision flow chart based on previously published neuroradiologic knowledge for the diagnosis of posterior fossa tumors in children. MATERIALS AND METHODS A retrospective study was conducted (from January 2013 to October 2019) at 2 pediatric referral centers, Children's Hospital of Philadelphia, United States, and Great Ormond Street Hospital, United Kingdom. Inclusion criteria were younger than 18 years of age and histologically and molecularly confirmed posterior fossa tumors. Subjects with no available preoperative MR imaging and tumors located primarily in the brain stem were excluded. Imaging characteristics of the tumors were evaluated following a predesigned, step-by-step flow chart. Agreement between readers was tested with the Cohen κ, and each diagnosis was analyzed for accuracy. RESULTS A total of 148 cases were included, with a median age of 3.4 years (interquartile range, 2.1-6.1 years), and a male/female ratio of 1.24. The predesigned flow chart facilitated identification of pilocytic astrocytoma, ependymoma, and medulloblastoma sonic hedgehog tumors with high sensitivity and specificity. On the basis of the results, the flow chart was adjusted so that it would also be able to better discriminate atypical teratoid/rhabdoid tumors and medulloblastoma groups 3 or 4 (sensitivity = 75%-79%; specificity = 92%-99%). Moreover, our adjusted flow chart was useful in ruling out ependymoma, pilocytic astrocytomas, and medulloblastoma sonic hedgehog tumors. CONCLUSIONS The modified flow chart offers a structured tool to aid in the adjunct diagnosis of pediatric posterior fossa tumors. Our results also establish a useful starting point for prospective clinical studies and for the development of automated algorithms, which may provide precise and adequate diagnostic tools for these tumors in clinical practice.
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Affiliation(s)
- C A P F Alves
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Radiology (C.d.C.L.), Hospital das Clinicas, Faculdade de Medicina de Sao Paulo, Sao Paulo
| | - U Löbel
- Radiology Department (U.L., K.M., F.D.), UCL Great Ormond Street Hospital for Children, London, UK
| | - J S Martin-Saavedra
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - S Toescu
- Department of Neurosurgery (S.T.), UCL Great Ormond Street Hospital for Children, London, UK
| | - M H Tsunemi
- Department of Biostatistics (M.H.T.), Instituto de Biociências, São Paulo State University, São Paul, Brazil
| | - S R Teixeira
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - K Mankad
- Radiology Department (U.L., K.M., F.D.), UCL Great Ormond Street Hospital for Children, London, UK
| | - D Hargrave
- Pediatric Oncology Unit (D.H.), UCL Great Ormond Street Institute of Child Health, London, UK
| | - T S Jacques
- Developmental Biology and Cancer Programme (T.S.J.), UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - C da Costa Leite
- Department of Radiology (C.d.C.L.), Hospital das Clinicas, Faculdade de Medicina de Sao Paulo, Sao Paulo
| | - F G Gonçalves
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - A Vossough
- From the Division of Neuroradiology (C.A.P.F.A., J.S.M.-S. S.R.T., F.G.G., A.V.), Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - F D'Arco
- Radiology Department (U.L., K.M., F.D.), UCL Great Ormond Street Hospital for Children, London, UK
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Peng YH, Richard SA, Lan Z, Zhang Y. Radiation induced glioma in a sexagenarian: A case report. Medicine (Baltimore) 2021; 100:e25373. [PMID: 33879666 PMCID: PMC8078338 DOI: 10.1097/md.0000000000025373] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/11/2021] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Radiation induced gliomas often occurs after radiation therapy for other brain tumors. Medulloblastoma often occurs in children and its associated radiation-induced glioblastoma multiforme's (GBM) after radiotherapy often has a long latency period. Our case is very unique because the medulloblastoma was detected at an advance age and the latency period of radiation-induced GBM was relatively shorter. PATIENTS CONCERNS A 64-year-old male was first admitted at our hospital in March 2018 with dizziness, vomiting, and blurred vision. DIAGNOSIS Magnetic resonance imaging of brain revealed a lesion with local mixed density and mass enhancement in left cerebellar region. Histopathology established medulloblastoma (World Health Organization) grade 4 and a classic histological subtype after surgery. INTERVENTION Surgical resection followed by radiation therapy were the initial therapeutic modalities. OUTCOMES In April 2019, the patient was readmitted with dizziness and blurred vision. Magnetic resonance imaging showed the left cerebellar hemisphere bulky enhancement lesion. Again, a multimodal therapy comprising surgical resection, radiation therapy as well as chemotherapy was adapted after histopathology established GBM. LESION Radiotherapy for medulloblastoma patients at advance ages is a critical predisposing factor for the development of radiation-induced GBM in a very short period of time. We suggest that, radiotherapy as adjuvant therapy for medulloblastoma patients at advance ages should be chosen with extreme caution.
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Affiliation(s)
- You-Heng Peng
- Department of Neurosurgery, West China Hospital, Sichuan University; 37 Guo Xue Xiang Road, Chengdu, Sichuan, P. R. China
| | - Seidu A. Richard
- Department of Neurosurgery, West China Hospital, Sichuan University; 37 Guo Xue Xiang Road, Chengdu, Sichuan, P. R. China
- Department of Medicine, Princefield University, P. O. Box MA 128, Ho-Volta Region, Ghana, West Africa
| | - Zhigang Lan
- Department of Neurosurgery, West China Hospital, Sichuan University; 37 Guo Xue Xiang Road, Chengdu, Sichuan, P. R. China
| | - Yuekang Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University; 37 Guo Xue Xiang Road, Chengdu, Sichuan, P. R. China
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48
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Zheng H, Li J, Liu H, Wu C, Gui T, Liu M, Zhang Y, Duan S, Li Y, Wang D. Clinical-MRI radiomics enables the prediction of preoperative cerebral spinal fluid dissemination in children with medulloblastoma. World J Surg Oncol 2021; 19:134. [PMID: 33888125 PMCID: PMC8063474 DOI: 10.1186/s12957-021-02239-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 04/12/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Medulloblastoma (MB) is the most common pediatric embryonal tumor. Accurate identification of cerebral spinal fluid (CSF) dissemination is important in prognosis prediction. Both MRI of the central nervous system (CNS) and CSF cytology will appear false positive and negative. Our objective was to investigate the added value of preoperative-enhanced T1-weighted image-based radiomic features to clinical characteristics in predicting preoperative CSF dissemination for children with MB. MATERIALS AND METHODS This retrospective study included 84 children with histopathologically confirmed MB between November 2006 and November 2018 (training cohort, n=60; internal validation cohort, n=24). A set of cases between December 2018 and February 2020 were used for external validation (n=40). The children with normal head and spine magnetic resonance images (MRI) and no subsequent dissemination in 1 year were diagnosed as non-CSF dissemination. The CSF dissemination was manifested as intracranial or intraspinal nodular-enhanced lesions. Clinical features were collected, and conventional MRI features of preoperative head MRI examinations were evaluated. A total of 385 radiomic features were extracted from preoperative-enhanced T1-weighted images. Minimum redundancy, maximum correlation, and least absolute shrinkage and selection operator were performed to select the features with the best performance in predicting preoperative CSF dissemination. A combined clinical-MRI radiomic prediction model was developed using multivariable logistic regression. Receiver operating curve analysis (ROC) was used to validate the predictive performance. Nomogram and decision curve analysis (DCA) were developed to evaluate the clinical utility of the combined model. RESULTS One clinical and nine radiomic features were selected for predicting preoperative CSF dissemination. The combined model incorporating clinical and radiomic features had the best predictive performance in the training cohort with an AUC of 0.89. This was validated in the internal and external cohorts with AUCs of 0.87 and 0.73. The clinical utility of the model was confirmed by a clinical-MRI radiomic nomogram and DCA. CONCLUSIONS The combined model incorporating clinical, conventional MRI, and radiomic features could be applied to predict preoperative CSF dissemination for children with MB as a noninvasive biomarker, which could aid in risk evaluation.
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Affiliation(s)
- Hui Zheng
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huanhuan Liu
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenqing Wu
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Gui
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ming Liu
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuzhen Zhang
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaofeng Duan
- GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China
| | - Yuhua Li
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Khatua S, Cooper LJN, Sandberg DI, Ketonen L, Johnson JM, Rytting ME, Liu DD, Meador H, Trikha P, Nakkula RJ, Behbehani GK, Ragoonanan D, Gupta S, Kotrotsou A, Idris T, Shpall EJ, Rezvani K, Colen R, Zaky W, Lee DA, Gopalakrishnan V. Phase I study of intraventricular infusions of autologous ex vivo expanded NK cells in children with recurrent medulloblastoma and ependymoma. Neuro Oncol 2021; 22:1214-1225. [PMID: 32152626 DOI: 10.1093/neuonc/noaa047] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Recurrent pediatric medulloblastoma and ependymoma have a grim prognosis. We report a first-in-human, phase I study of intraventricular infusions of ex vivo expanded autologous natural killer (NK) cells in these tumors, with correlative studies. METHODS Twelve patients were enrolled, 9 received protocol therapy up to 3 infusions weekly, in escalating doses from 3 × 106 to 3 × 108 NK cells/m2/infusion, for up to 3 cycles. Cerebrospinal fluid (CSF) was obtained for cellular profile, persistence, and phenotypic analysis of NK cells. Radiomic characterization on pretreatment MRI scans was performed in 7 patients, to develop a non-invasive imaging-based signature. RESULTS Primary objectives of NK cell harvest, expansion, release, and safety of 112 intraventricular infusions of NK cells were achieved in all 9 patients. There were no dose-limiting toxicities. All patients showed progressive disease (PD), except 1 patient showed stable disease for one month at end of study follow-up. Another patient had transient radiographic response of the intraventricular tumor after 5 infusions of NK cell before progressing to PD. At higher dose levels, NK cells increased in the CSF during treatment with repetitive infusions (mean 11.6-fold). Frequent infusions of NK cells resulted in CSF pleocytosis. Radiomic signatures were profiled in 7 patients, evaluating ability to predict upfront radiographic changes, although they did not attain statistical significance. CONCLUSIONS This study demonstrated feasibility of production and safety of intraventricular infusions of autologous NK cells. These findings support further investigation of locoregional NK cell infusions in children with brain malignancies.
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Affiliation(s)
- Soumen Khatua
- Department of Pediatrics, MD Anderson Cancer Center, Houston
| | | | - David I Sandberg
- Department of Neurosurgery, MD Anderson Cancer Center, Houston.,Department of Neurosurgery, McGovern Medical School/University of Texas Health Science Center, Houston
| | - Leena Ketonen
- Department of Diagnostic Imaging, MD Anderson Cancer Center, Houston
| | - Jason M Johnson
- Department of Diagnostic Imaging, MD Anderson Cancer Center, Houston
| | | | - Diane D Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer center
| | - Heather Meador
- Department of Pediatrics, MD Anderson Cancer Center, Houston
| | - Prashant Trikha
- Department of Hematology, Oncology and BMT, Nationwide Children's Hospital, Columbus, Ohio and Department of Hematology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Robin J Nakkula
- Department of Hematology, Oncology and BMT, Nationwide Children's Hospital, Columbus, Ohio and Department of Hematology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Gregory K Behbehani
- Department of Hematology, Oncology and BMT, Nationwide Children's Hospital, Columbus, Ohio and Department of Hematology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | | | - Sumit Gupta
- Department of Pediatrics, MD Anderson Cancer Center, Houston
| | | | - Tagwa Idris
- Department of Radiology, Harvard Medical School
| | - Elizabeth J Shpall
- Department of Stem Cell Transplantation and Cellular Therapy, MD Anderson Cancer Center, Houston
| | - Katy Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, MD Anderson Cancer Center, Houston
| | - Rivka Colen
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.,Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Wafik Zaky
- Department of Pediatrics, MD Anderson Cancer Center, Houston
| | - Dean A Lee
- Department of Hematology, Oncology and BMT, Nationwide Children's Hospital, Columbus, Ohio and Department of Hematology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
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50
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Tam LT, Yeom KW, Wright JN, Jaju A, Radmanesh A, Han M, Toescu S, Maleki M, Chen E, Campion A, Lai HA, Eghbal AA, Oztekin O, Mankad K, Hargrave D, Jacques TS, Goetti R, Lober RM, Cheshier SH, Napel S, Said M, Aquilina K, Ho CY, Monje M, Vitanza NA, Mattonen SA. MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study. Neurooncol Adv 2021; 3:vdab042. [PMID: 33977272 PMCID: PMC8095337 DOI: 10.1093/noajnl/vdab042] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61–0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49–0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64–0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51–0.67], Noether’s test P = .02). Conclusions In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.
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Affiliation(s)
- Lydia T Tam
- Stanford University School of Medicine, Stanford, California, USA.,Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California, USA
| | - Kristen W Yeom
- Stanford University School of Medicine, Stanford, California, USA.,Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California, USA
| | - Jason N Wright
- Department of Radiology, Seattle Children's Hospital, Seattle, Washington, USA.,Harborview Medical Center, Seattle, Washington, USA
| | - Alok Jaju
- Department of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Alireza Radmanesh
- Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Michelle Han
- Stanford University School of Medicine, Stanford, California, USA.,Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California, USA
| | - Sebastian Toescu
- University College London, Great Ormond Street Institute of Child Health, London, UK
| | - Maryam Maleki
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Eric Chen
- Departments of Clinical Radiology & Imaging Sciences, Riley Children's Hospital, Indiana University, Indianapolis, Indiana, USA
| | - Andrew Campion
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California, USA
| | - Hollie A Lai
- Department of Radiology, CHOC Children's Hospital, Orange, California, USA.,University of California, Irvine, California, USA
| | - Azam A Eghbal
- Department of Radiology, CHOC Children's Hospital, Orange, California, USA.,University of California, Irvine, California, USA
| | - Ozgur Oztekin
- Department of Neuroradiology, Bakircay University, Cigli Education and Research Hospital, Izmir, Turkey.,Department of Neuroradiology, Health Science University, Tepecik Education and Research Hospital, Izmir, Turkey
| | - Kshitij Mankad
- University College London, Great Ormond Street Institute of Child Health, London, UK.,Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - Darren Hargrave
- University College London, Great Ormond Street Institute of Child Health, London, UK
| | - Thomas S Jacques
- University College London, Great Ormond Street Institute of Child Health, London, UK
| | - Robert Goetti
- Department of Medical Imaging, The Children's Hospital at Westmead, The University of Sydney, Westmead, Australia
| | - Robert M Lober
- Department of Neurosurgery, Dayton Children's Hospital, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
| | - Samuel H Cheshier
- Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Mourad Said
- Radiology Department Centre International Carthage Médicale, Monastir, Tunisia
| | - Kristian Aquilina
- University College London, Great Ormond Street Institute of Child Health, London, UK
| | - Chang Y Ho
- Departments of Clinical Radiology & Imaging Sciences, Riley Children's Hospital, Indiana University, Indianapolis, Indiana, USA
| | - Michelle Monje
- Stanford University School of Medicine, Stanford, California, USA.,Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA
| | - Nicholas A Vitanza
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington, USA.,Ben Towne Center for Childhood Cancer Research, Seattle Children's Research Institute, Seattle, Washington, USA
| | - Sarah A Mattonen
- Department of Medical Biophysics, Western University, London, Onatrio, Canada.,Department of Oncology, Western University, London, Ontario, Canada
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