Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning

Madhogarhia, Rachel; Kazerooni, Anahita Fathi; Arif, Sherjeel; Ware, Jeffrey B.; Familiar, Ariana; Vidal, Lorenna; Bagheri, Sina; Anderson, Hannah; Haldar, Debanjan; Yagoda, Sophie; Graves, Erin; Spadola, Michael; Yan, Rachel; Dahmane, Nadia; Sako, Chiharu; Vossough, Arastoo; Storm, Phillip B.; Resnick, Adam; Davatzikos, Christos; Nabavizadeh, Ali

doi:10.1117/12.2611551

Cited by 10 publications

(11 citation statements)

References 17 publications

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“…299 To enhance the efficiency of tumor delineation, various machine learning-based models and tools have been developed. 263,266,268,269,[271][272][273][274][275][276][278][279][280][281][282][283][284]300,301 In a study reported by Lin et al, their machine learningassisted delineation tool improved the efficiency by nearly 40% compared to manual delineation. 265 However, in some cases, such as advancedstage NPCs, inferior delineation performance was observed.…”

Section: Tumor Delineationmentioning

confidence: 99%

“…In general, the critical delineation task is manually performed by experienced oncologists, and the process is quite time‐consuming and tedious 299 . To enhance the efficiency of tumor delineation, various machine learning‐based models and tools have been developed 263,266,268,269,271–276,278–284,300,301 . In a study reported by Lin et al., their machine learning‐assisted delineation tool improved the efficiency by nearly 40% compared to manual delineation 265 .…”

Section: Machine Learning In Multiparametric Mrimentioning

confidence: 99%

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Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

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Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting.However,the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI.

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Section: Tumor Delineationmentioning

confidence: 99%

Section: Machine Learning In Multiparametric Mrimentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

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“…One potential solution is to develop an automatic segmentation module for pediatric brain cancer, thereby allowing for more consistent segmentations across radiomic studies, a reduction in segmentation time, and easier translation of radiomic models to clinical settings. 57 …”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Radiomics and radiogenomics in pediatric neuro-oncology: A review

Madhogarhia

Haldar

Bagheri

et al. 2022

Neuro-Oncology Advances

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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 (i.e., 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 two-fold: 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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“…Secondly, we provide a detailed literature review of the radiomic and radiogenomic approaches that have thus far been explored for pediatric MB. We have grouped these studies by their endpoint, resulting in three main categories: (1) applications in survival prognostication [28][29][30][31][32], (2) applications in molecular subgroup classification [32][33][34][35][36][37][38][39][40] (i.e., radiogenomics), and (3) MB tumor segmentation [41,42]. Finally, we address the current challenges and future directions pertaining to applications of radiomic and radiogenomic approaches in pediatric MB tumors.…”

Section: Introductionmentioning

confidence: 99%

Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors

Ismail,

Craig,

Ahmed

et al. 2023

Diagnostics

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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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Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning

Cited by 10 publications

References 17 publications

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Radiomics and radiogenomics in pediatric neuro-oncology: A review

Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors

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