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1. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Zapaishchykova, Anna, Pavaine, Julija, Shah, Lubdha M., Jones, Blaise V., Sheth, Nakul, Prabhu, Sanjay P., McAllister, Aaron S., Tu, Wenxin, Nandolia, Khanak K., Rodriguez, Andres F., Shaikh, Ibraheem Salman, Montano, Mariana Sanchez, Lai, Hollie Anne, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anderson, Hannah, Anwar, Syed Muhammed, Aristizabal, Alejandro, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Eddy, James, Ezhov, Ivan, Familiar, Ariana M., Farahani, Keyvan, Gandhi, Deep, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Elaine, Elaine, Karargyris, Alexandros, Kassem, Hasan, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei B., Maleki, Nazanin, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W., Pati, Sarthak, Piraud, Marie, Poussaint, Tina, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Sheller, MIcah, Shinohara, Russell Takeshi, Viswanathan, Karthik, Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter F., Davatzikos, Christos, Storm, Phillip B., Bornhorst, Miriam, Packer, Roger, Hummel, Trent, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Ware, Jeffrey B., Kann, Benjamin H., Rood, Brian, Resnick, Adam, Bakas, Spyridon, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Published
2024

2. The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Gandhi, Deep, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Franson, Andrea, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Sheth, Nakul, Tu, Wenxin, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Zapaishchykova, Anna, Bornhorst, Miriam, Deutsch, Michelle, Fouladi, Maryam, Lazow, Margot, Mikael, Leonie, Hummel, Trent, Kann, Benjamin, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Packer, Roger, Bakas, Spyridon, Resnick, Adam, Rood, Brian, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Published
2024

4. Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Author

Vossough, Arastoo, Khalili, Nastaran, Familiar, Ariana M., Gandhi, Deep, Viswanathan, Karthik, Tu, Wenxin, Haldar, Debanjan, Bagheri, Sina, Anderson, Hannah, Haldar, Shuvanjan, Storm, Phillip B., Resnick, Adam, Ware, Jeffrey B., Nabavizadeh, Ali, and Kazerooni, Anahita Fathi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics
Abstract

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Published
2024

6. A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network

Author

Familiar, Ariana M., Kazerooni, Anahita Fathi, Anderson, Hannah, Lubneuski, Aliaksandr, Viswanathan, Karthik, Breslow, Rocky, Khalili, Nastaran, Bagheri, Sina, Haldar, Debanjan, Kim, Meen Chul, Arif, Sherjeel, Madhogarhia, Rachel, Nguyen, Thinh Q., Frenkel, Elizabeth A., Helili, Zeinab, Harrison, Jessica, Farahani, Keyvan, Linguraru, Marius George, Bagci, Ulas, Velichko, Yury, Stevens, Jeffrey, Leary, Sarah, Lober, Robert M., Campion, Stephani, Smith, Amy A., Morinigo, Denise, Rood, Brian, Diamond, Kimberly, Pollack, Ian F., Williams, Melissa, Vossough, Arastoo, Ware, Jeffrey B., Mueller, Sabine, Storm, Phillip B., Heath, Allison P., Waanders, Angela J., Lilly, Jena V., Mason, Jennifer L., Resnick, Adam C., and Nabavizadeh, Ali

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-na\"ive images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

Published
2023

7. An international study presenting a federated learning AI platform for pediatric brain tumors

Author

Lee, Edward H., Han, Michelle, Wright, Jason, Kuwabara, Michael, Mevorach, Jacob, Fu, Gang, Choudhury, Olivia, Ratan, Ujjwal, Zhang, Michael, Wagner, Matthias W., Goetti, Robert, Toescu, Sebastian, Perreault, Sebastien, Dogan, Hakan, Altinmakas, Emre, Mohammadzadeh, Maryam, Szymanski, Kathryn A., Campen, Cynthia J., Lai, Hollie, Eghbal, Azam, Radmanesh, Alireza, Mankad, Kshitij, Aquilina, Kristian, Said, Mourad, Vossough, Arastoo, Oztekin, Ozgur, Ertl-Wagner, Birgit, Poussaint, Tina, Thompson, Eric M., Ho, Chang Y., Jaju, Alok, Curran, John, Ramaswamy, Vijay, Cheshier, Samuel H., Grant, Gerald A., Wong, S. Simon, Moseley, Michael E., Lober, Robert M., Wilms, Mattias, Forkert, Nils D., Vitanza, Nicholas A., Miller, Jeffrey H., Prolo, Laura M., and Yeom, Kristen W.

Published
2024
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10. Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction

Author

Sadique, M. S., Farzana, W., Temtam, A., Lappinen, E., Vossough, A., and Iftekharuddin, K. M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

GBM (Glioblastoma multiforme) is the most aggressive type of brain tumor in adults that has a short survival rate even after aggressive treatment with surgery and radiation therapy. The changes on magnetic resonance imaging (MRI) for patients with GBM after radiotherapy are indicative of either radiation-induced necrosis (RN) or recurrent brain tumor (rBT). Screening for rBT and RN at an early stage is crucial for facilitating faster treatment and better outcomes for the patients. Differentiating rBT from RN is challenging as both may present with similar radiological and clinical characteristics on MRI. Moreover, learning-based rBT versus RN classification using MRI may suffer from class imbalance due to lack of patient data. While synthetic data generation using generative models has shown promise to address class imbalance, the underlying data representation may be different in synthetic or augmented data. This study proposes computational modeling with statistically rigorous repeated random sub-sampling to balance the subset sample size for rBT and RN classification. The proposed pipeline includes multiresolution radiomic feature (MRF) extraction followed by feature selection with statistical significance testing (p<0.05). The five-fold cross validation results show the proposed model with MRF features classifies rBT from RN with an area under the curve (AUC) of 0.8920+-.055. Moreover, considering the dependence between survival time and censor time (where patients are not followed up until death), we demonstrate the feasibility of using MRF radiomic features as a non-invasive biomarker to identify patients who are at higher risk of recurrence or radiation necrosis. The cross-validated results show that the MRF model provides the best overall performance with an AUC of 0.770+-.032.

Published
2023

11. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rodriguez, Andres, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Shah, Lubdha M., Sheth, Nakul, Shinohara, Russel Taki, Tu, Wenxin, Viswanathan, Karthik, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Wiggins, Walter, Zapaishchykova, Anna, Aboian, Mariam, Bornhorst, Miriam, de Blank, Peter, Deutsch, Michelle, Fouladi, Maryam, Hoffman, Lindsey, Kann, Benjamin, Lazow, Margot, Mikael, Leonie, Nabavizadeh, Ali, Packer, Roger, Resnick, Adam, Rood, Brian, Vossough, Arastoo, Bakas, Spyridon, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Published
2023

12. An international study presenting a federated learning AI platform for pediatric brain tumors

Author

Edward H. Lee, Michelle Han, Jason Wright, Michael Kuwabara, Jacob Mevorach, Gang Fu, Olivia Choudhury, Ujjwal Ratan, Michael Zhang, Matthias W. Wagner, Robert Goetti, Sebastian Toescu, Sebastien Perreault, Hakan Dogan, Emre Altinmakas, Maryam Mohammadzadeh, Kathryn A. Szymanski, Cynthia J. Campen, Hollie Lai, Azam Eghbal, Alireza Radmanesh, Kshitij Mankad, Kristian Aquilina, Mourad Said, Arastoo Vossough, Ozgur Oztekin, Birgit Ertl-Wagner, Tina Poussaint, Eric M. Thompson, Chang Y. Ho, Alok Jaju, John Curran, Vijay Ramaswamy, Samuel H. Cheshier, Gerald A. Grant, S. Simon Wong, Michael E. Moseley, Robert M. Lober, Mattias Wilms, Nils D. Forkert, Nicholas A. Vitanza, Jeffrey H. Miller, Laura M. Prolo, and Kristen W. Yeom

Subjects
Science
Abstract

Abstract While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance on a diverse, realistic, multi-center cohort. Pediatric brain tumors were targeted due to the scarcity of such datasets, even in tertiary care hospitals. Our platform orchestrates federated training for joint tumor classification and segmentation across 19 international sites. FL-PedBrain exhibits less than a 1.5% decrease in classification and a 3% reduction in segmentation performance compared to centralized data training. FL boosts segmentation performance by 20 to 30% on three external, out-of-network sites. Finally, we explore the sources of data heterogeneity and examine FL robustness in real-world scenarios with data imbalances.

Published
2024
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13. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Author

Fathi Kazerooni, Anahita, Arif, Sherjeel, Madhogarhia, Rachel, Khalili, Nastaran, Haldar, Debanjan, Bagheri, Sina, Familiar, Ariana, Anderson, Hannah, Haldar, Shuvanjan, Tu, Wenxin, Chul Kim, Meen, Viswanathan, Karthik, Muller, Sabine, Prados, Michael, Kline, Cassie, Vidal, Lorenna, Aboian, Mariam, Storm, Phillip, Resnick, Adam, Ware, Jeffrey, Vossough, Arastoo, Davatzikos, Christos, and Nabavizadeh, Ali

Subjects
deep learning, magnetic resonance imaging, pediatric brain tumor, skull-stripping, tumor segmentation
Abstract

BACKGROUND: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. METHODS: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. RESULTS: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. CONCLUSIONS: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Published
2023

14. Racial and ethnic disparities in gallbladder cancer: A two‐decade analysis of incidence and mortality rates in the US

Author

Yazan Abboud, Lawanya Singh, Madison Fraser, Chun‐Wei Pan, Ibrahim Abboud, Islam H. Mohamed, David Kim, Saqr Alsakarneh, Fouad Jaber, Benjamin Richter, Ahmed Al‐Khazraji, Kaveh Hajifathalian, and Sima Vossough‐Teehan

Subjects
disparities, gallbladder cancer, incidence, mortality, race, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Gallbladder cancer (GBC) is an aggressive malignancy that is usually diagnosed at a late stage. Prior data showed increasing incidence of GBC in the US. However, little is known about race/ethnic‐specific incidence and mortality trends of GBC per stage at diagnosis. Therefore, we aimed to conduct a time‐trend analysis of GBC incidence and mortality rates categorized by race/ethnicity and stage‐at‐diagnosis. Methods Age‐adjusted GBC incidence and mortality rates were calculated using SEER*Stat software from the United States Cancer Statistics database (covers ~98% of US population between 2001 and 2020) and NCHS (covers ~100% of the US population between 2000 and 2020) databases, respectively. Race/Ethnic groups were Non‐Hispanic‐White (NHW), Non‐Hispanic‐Black (NHB), Hispanic, Non‐Hispanic‐Asian/Pacific‐Islander (NHAPI), and Non‐Hispanic‐American‐Indian/Alaska‐Native (NHAIAN). Stage‐at‐diagnoses were all stages, early, regional, and distant stages. Joinpoint regression was used to generate time‐trends [annual percentage change (APC) and average APC (AAPC)] with parametric estimations and a two‐sided t‐test (p‐value cut‐off 0.05). Results 76,873 patients were diagnosed with GBC with decreasing incidence rates in all races/ethnicities except NHB who experienced an increasing trend between 2001 and 2014 (APC = 2.08, p

Published
2024
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21. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023.

Author

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Debanjan Haldar, Zhifan Jiang, Anna Zapaishchykova, Julija Pavaine, Lubdha M. Shah, Blaise V. Jones, Nakul Sheth, Sanjay P. Prabhu, Aaron S. McAllister, Wenxin Tu, Khanak K. Nandolia, Andres Felipe Rodriguez, Ibraheem Salman Shaikh, Mariana Sanchez-Montano, Hollie Anne Lai, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Hannah Anderson, Syed Muhammed Anwar, Alejandro Aristizabal, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Austin J. Borja, Evan Calabrese, Verena Chung, Gian Marco Conte, James A. Eddy, Ivan Ezhov, Ariana M. Familiar, Keyvan Farahani, Deep Gandhi, Anurag Gottipati, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Elaine, Alexandros Karargyris, Hasan Kassem, Neda Khalili, Florian Kofler, Dominic LaBella, Koen Van Leemput, Hongwei Li 0004, Nazanin Maleki, Zeke Meier, Bjoern H. Menze, Ahmed W. Moawad, Sarthak Pati, Marie Piraud, Tina Poussaint, Zachary J. Reitman, Jeffrey D. Rudie, Rachit Saluja, Micah J. Sheller, Russell Takeshi Shinohara, Karthik Viswanathan, Chunhao Wang, Benedikt Wiestler, Walter F. Wiggins, Christos Davatzikos, Phillip B. Storm, Miriam Bornhorst, Roger J. Packer, Trent Hummel, Peter de Blank, Lindsey Hoffman, Mariam Aboian, Ali Nabavizadeh, Jeffrey B. Ware, Benjamin H. Kann, Brian Rood, Adam C. Resnick, Spyridon Bakas, Arastoo Vossough, and Marius George Linguraru

Published
2024
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23. The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Author

Anahita Fathi Kazerooni, Nastaran Khalili, Deep Gandhi, Xinyang Liu, Zhifan Jiang, Syed Muhammed Anwar, Jake Albrecht, Maruf Adewole, Udunna Anazodo, Hannah Anderson, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Austin J. Borja, Evan Calabrese, Verena Chung, Gian Marco Conte, Farouk Dako, James A. Eddy, Ivan Ezhov, Ariana Familiar, Keyvan Farahani, Anurag Gottipati, Debanjan Haldar, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Johansen, Blaise V. Jones, Neda Khalili, Florian Kofler, Dominic LaBella, Hollie Anne Lai, Koen Van Leemput, Hongwei Bran Li, Nazanin Maleki, Aaron S. McAllister, Zeke Meier, Bjoern H. Menze, Ahmed W. Moawad, Khanak K. Nandolia, Julija Pavaine, Marie Piraud, Tina Poussaint, Sanjay P. Prabhu, Zachary Reitman, Andres Felipe Rodriguez, Jeffrey D. Rudie, Mariana Sanchez-Montano, Ibraheem Salman Shaikh, Lubdha M. Shah, Nakul Sheth, Russel Taki Shinohara, Wenxin Tu, Karthik Viswanathan, Chunhao Wang, Jeffrey B. Ware, Benedikt Wiestler, Walter I. Wiggins, Anna Zapaishchykova, Mariam Aboian, Miriam Bornhorst, Peter de Blank, Michelle Deutsch, Maryam Fouladi, Lindsey Hoffman, Benjamin H. Kann, Margot Lazow, Leonie Mikael, Ali Nabavizadeh, Roger J. Packer, Spyridon Bakas, Adam C. Resnick, Brian Rood, Arastoo Vossough, and Marius George Linguraru

Published
2024
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26. P783: Two fetuses with hereditary hemorrhagic telangiectasia presenting with rare finding of arteriovenous malformations

Author

Natalie Burrill, Christina Paidas Teefey, Nankee Kumar, Sonika Agarwal, Lauren Beslow, Edward Oliver, Minh-Huy Le Huynh, Elizabeth Goldmuntz, Bryan Pukenas, Alexandra Borst, Allison Britt, Arastoo Vossough, Stephanie Fuller, Shih-Shan Lang Chen, Erica Schindewolf, Lisa Pilchman, Renee Wright, and Haley Crane

Subjects
Genetics, QH426-470, Medicine
Published
2024
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27. Integrating neuroimaging biomarkers into the multicentre, high-dose erythropoietin for asphyxia and encephalopathy (HEAL) trial: rationale, protocol and harmonisation

Author

Wisnowski, Jessica L, Bluml, Stefan, Panigrahy, Ashok, Mathur, Amit M, Berman, Jeffrey, Chen, Ping-Sun Keven, Dix, James, Flynn, Trevor, Fricke, Stanley, Friedman, Seth D, Head, Hayden W, Ho, Chang Y, Kline-Fath, Beth, Oveson, Michael, Patterson, Richard, Pruthi, Sumit, Rollins, Nancy, Ramos, Yanerys M, Rampton, John, Rusin, Jerome, Shaw, Dennis W, Smith, Mark, Tkach, Jean, Vasanawala, Shreyas, Vossough, Arastoo, Whitehead, Matthew T, Xu, Duan, Yeom, Kristen, Comstock, Bryan, Heagerty, Patrick J, Juul, Sandra E, Wu, Yvonne W, McKinstry, Robert C, Ahmed, Kaashif, Beserga, Mariana, Bendel-Stenzel, Ellen, Chalak, Lina, Chang, Taeun, Flibotte, John, Gonzalez, Fernando, Lampland, Andrea, Maitre, Nathalie, Mayock, Dennis, Mietzsch, Ulrike, Poindexter, Brenda, Rao, Rakesh, Riley, David, Sokol, Gregory M, Van Meurs, Krisa, Weitkamp, Hendrik, Wu, Tai-Wei, and Yanowitz, Toby

Subjects
Neurodegenerative, Biomedical Imaging, Bioengineering, Patient Safety, Neurosciences, Clinical Research, Clinical Trials and Supportive Activities, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Neurological, Asphyxia, Biomarkers, Clinical Trial Protocols as Topic, Erythropoietin, Humans, Hypoxia-Ischemia, Brain, Infant, Newborn, Multicenter Studies as Topic, Neuroimaging, neonatal intensive & critical care, neonatology, neurological injury, neuroradiology, developmental neurology & neurodisability, paediatric radiology, HEAL Study Group, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences
Abstract

IntroductionMRI and MR spectroscopy (MRS) provide early biomarkers of brain injury and treatment response in neonates with hypoxic-ischaemic encephalopathy). Still, there are challenges to incorporating neuroimaging biomarkers into multisite randomised controlled trials. In this paper, we provide the rationale for incorporating MRI and MRS biomarkers into the multisite, phase III high-dose erythropoietin for asphyxia and encephalopathy (HEAL) Trial, the MRI/S protocol and describe the strategies used for harmonisation across multiple MRI platforms.Methods and analysisNeonates with moderate or severe encephalopathy enrolled in the multisite HEAL trial undergo MRI and MRS between 96 and 144 hours of age using standardised neuroimaging protocols. MRI and MRS data are processed centrally and used to determine a brain injury score and quantitative measures of lactate and n-acetylaspartate. Harmonisation is achieved through standardisation-thereby reducing intrasite and intersite variance, real-time quality assurance monitoring and phantom scans.Ethics and disseminationIRB approval was obtained at each participating site and written consent obtained from parents prior to participation in HEAL. Additional oversight is provided by an National Institutes of Health-appointed data safety monitoring board and medical monitor.Trial registration numberNCT02811263; Pre-result.

Published
2021

29. Arterial Spin-Labeling Perfusion for PHACE Syndrome

Author

Mamlouk, MD, Vossough, A, Caschera, L, Maheshwari, M, and Hess, CP

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Stroke, Pediatric, Rare Diseases, Cardiovascular, Neurosciences, Clinical Trials and Supportive Activities, Clinical Research, Adolescent, Aortic Coarctation, Cerebrovascular Circulation, Child, Child, Preschool, Electron Spin Resonance Spectroscopy, Eye Abnormalities, Female, Humans, Infant, Infant, Newborn, Magnetic Resonance Imaging, Male, Neurocutaneous Syndromes, Perfusion Imaging, Spin Labels, Syndrome, Clinical Sciences, Nuclear Medicine & Medical Imaging, Clinical sciences, Physical chemistry
Abstract

Background and purposeArterial stroke is a rare-but-reported complication in patients with posterior fossa brain malformations, hemangiomas, arterial anomalies, coarctation of the aorta and cardiac defects, and eye abnormalities (PHACE) syndrome. Currently, stroke risk is inferred by the severity of arterial anomalies identified on MRA, though no evidenced-based data exist. The purpose of our study was to determine whether arterial spin-labeling MR imaging perfusion can detect alterations in CBF in patients with PHACE syndrome.Materials and methodsRecords were reviewed from 3 institutions for all patients with PHACE syndrome who underwent arterial spin-labeling from 2000 to 2019. CBF was qualitatively investigated with arterial spin-labeling to determine whether there was decreased or normal perfusion. Arterial anomalies were characterized on MRA imaging, and parenchymal brain findings were evaluated on conventional MR imaging sequences.ResultsForty-one patients with PHACE syndrome had arterial spin-labeling imaging. There were 30 females and 11 males (age range, 7 days to 15 years). Of the 41 patients, 10 (24%) had decreased CBF signal corresponding to a major arterial territory. Ten of 10 patients had decreased CBF signal in the anterior circulation, 2/10 had decreased anterior and posterior circulation CBF signal, 2/10 had decreased bilateral anterior circulation CBF signal, and 1/10 had globally decreased CBF signal. Forty of 41 (97.5%) patients had at least 1 arteriopathy, and in those with decreased CBF signal, the arteriopathy corresponded to the CBF signal alteration in 10/10 patients.ConclusionsArterial spin-labeling can potentially characterize hemodynamic changes in patients with PHACE syndrome.

Published
2021

30. Intracranial Traumatic Hematoma Detection in Children Using a Portable Near-infrared Spectroscopy Device

Author

Kirschen, Matthew P., Myers, Sage R., Neuman, Mark I., Grubenhoff, Joseph A., Mannix, Rebekah, Stence, Nicholas, Yang, Edward, Woodford, Ashley L., Rogers, Tyson, Nordell, Anna, Vossough, Arastoo, and Zonfrillo, Mark R.

Subjects
intracranial hematoma, brain injury, head injury, spectroscopy
Abstract

Introduction: We sought to validate a handheld, near-infrared spectroscopy (NIRS) device for detecting intracranial hematomas in children with head injury.Methods: Eligible patients were those 3.5 milliliters in volume and 0.2 on three successive scans. We calculated diagnostic performance measures with corresponding exact two-sided 95% Clopper-Pearson confidence intervals (CI). Hypothesis test evaluated whether predictive performance exceeded chance agreement (predictive Youden’s index > 0).Results: A total of 464 patients were enrolled and 344 met inclusion for primary data analysis: 10.5% (36/344) had evidence of a hematoma on HCT, and 4.7% (16/344) had qualifying hematomas. The handheld brain scanner demonstrated a sensitivity of 58.3% (21/36) and specificity of 67.9% (209/308) for hematomas of any size. For qualifying hematomas the scanner was designed to detect, sensitivity was 81% (13/16) and specificity was 67.4% (221/328). Predictive performance exceeded chance agreement with a predictive Youden’s index of 0.11 (95% CI, 0.10 – 0.15; P < 0.001) for all hematomas, and 0.09 (95% CI, 0.08 – 0.12; P < 0.001) for qualifying hematomas.Conclusion: The handheld brain scanner can non-invasively detect a subset of intracranial hematomas in children and may serve an adjunctive role to head-injury neuroimaging decision rules that predict the risk of clinically significant intracranial pathology after head trauma.

Published
2021

38. Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers

Author

Haldar, Debanjan, Kazerooni, Anahita Fathi, Arif, Sherjeel, Familiar, Ariana, Madhogarhia, Rachel, Khalili, Nastaran, Bagheri, Sina, Anderson, Hannah, Shaikh, Ibraheem Salman, Mahtabfar, Aria, Kim, Meen Chul, Tu, Wenxin, Ware, Jefferey, Vossough, Arastoo, Davatzikos, Christos, Storm, Phillip B., Resnick, Adam, and Nabavizadeh, Ali

Published
2023
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39. Simple versus cross-mattress sutures for nondisplaced flaps of the maxillary molar region: a randomized controlled trial

Author

Hossein Babazade, Farshad Vossough, Nafise Maftouhi, and Shokoofeh Konarizadeh

Subjects
Sutures, Cross-mattress, Nondisplaced flap surgery, Maxillary, Molar, Dentistry, RK1-715
Abstract

Abstract Background The type of suture used in periodontal surgery can affect post-surgical complications. This study aimed to compare simple with cross-mattress sutures for nondisplaced flaps of the maxillary molar region. Methods This randomized controlled trial included 32 candidates of nondisplaced flap surgery of the maxillary molar region referred to the private office of a periodontist in Bandar Abbas, Iran from January 21 to May 4, 2020. First, the patients’ age, sex, and plaque index were recorded. Then, they were randomized into two equal groups. In the first group, the interdental suturing was done using simple sutures with 4–0 vicryl threads, and in the second group, interdental suturing was performed using cross-mattress sutures with the same threads. The primary outcome was suture time, including the duration of the first suture and the total duration of all sutures. The secondary outcomes were bleeding on probing and the requirement of supplementary sutures immediately after the surgery, as well as the gingival index (at suture removal and one month after surgery). Results The two groups were comparable regarding age, sex, and plaque index. The first suture duration was significantly longer in the simple group compared to the cross-mattress group (P

Published
2022
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42. Impaired Maternal‐Fetal Environment and Risk for Preoperative Focal White Matter Injury in Neonates With Complex Congenital Heart Disease

Author

Daniel J. Licht, Marin Jacobwitz, Jennifer M. Lynch, Tiffany Ko, Timothy Boorady, Mahima Devarajan, Kristina N. Heye, Kobina Mensah‐Brown, John J. Newland, Alexander Schmidt, Peter Schwab, Madeline Winters, Susan C. Nicolson, Lisa M. Montenegro, Stephanie Fuller, Christopher Mascio, J. William Gaynor, Arjun G. Yodh, Juliana Gebb, Arastoo Vossough, Grace H. Choi, and Mary E. Putt

Subjects
congenital brain injuries, heart defects, maternal fetal environment, placenta, risk factors, white matter injury, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Infants with congenital heart disease (CHD) are at risk for white matter injury (WMI) before neonatal heart surgery. Better knowledge of the causes of preoperative WMI may provide insights into interventions that improve neurodevelopmental outcomes in these patients. Methods and Results A prospective single‐center study of preoperative WMI in neonates with CHD recorded data on primary cardiac diagnosis, maternal‐fetal environment (MFE), delivery type, subject anthropometrics, and preoperative care. Total maturation score and WMI were assessed, and stepwise logistic regression modeling selected risk factors for WMI. Among subjects with severe CHD (n=183) who received a preoperative brain magnetic resonance imaging, WMI occurred in 40 (21.9%) patients. WMI prevalence (21.4%–22.1%) and mean volumes (119.7–160.4 mm3) were similar across CHD diagnoses. Stepwise logistic regression selected impaired MFE (odds ratio [OR], 2.85 [95% CI, 1.29–6.30]), male sex (OR, 2.27 [95% CI, 1.03–5.36]), and older age at surgery/magnetic resonance imaging (OR, 1.20 per day [95% CI, 1.03–1.41]) as risk factors for preoperative WMI and higher total maturation score values (OR, 0.65 per unit increase [95% CI, 0.43–0.95]) as protective. A quarter (24.6%; n=45) of subjects had ≥1 components of impaired MFE (gestational diabetes [n=12; 6.6%], gestational hypertension [n=11; 6.0%], preeclampsia [n=2; 1.1%], tobacco use [n=9; 4.9%], hypothyroidism [n=6; 3.3%], and other [n=16; 8.7%]). In a subset of 138 subjects, an exploratory analysis of additional MFE‐related factors disclosed other potential risk factors for WMI. Conclusions This study is the first to identify impaired MFE as an important risk factor for preoperative WMI. Vulnerability to preoperative WMI was shared across CHD diagnoses.

Published
2023
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44. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Author

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Debanjan Haldar, Zhifan Jiang, Syed Muhammad Anwar, Jake Albrecht, Maruf Adewole, Udunna Anazodo, Hannah Anderson, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Evan Calabrese, Verena Chung, Gian Marco Conte, Farouk Dako, James A. Eddy, Ivan Ezhov, Ariana Familiar, Keyvan Farahani, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Johanson, Florian Kofler, Dominic LaBella, Koen Van Leemput, Hongwei Bran Li, Nazanin Maleki, Zeke Meier, Bjoern H. Menze, Ahmed W. Moawad, Marie Piraud, Tina Poussaint, Zachary Reitman, Jeffrey D. Rudie, Ibraheem Salman Shaikh, Russell Taki Shinohara, Wenxin Tu, Karthik Viswanathan, Chunhao Wang, Jeffrey B. Ware, Benedikt Wiestler, Walter I. Wiggins, Anna Zapaishchykova, Mariam Aboian, Miriam Bornhorst, Peter de Blank, Michelle Deutsch, Maryam Fouladi, Lindsey Hoffman, Benjamin H. Kann, Margot Lazow, Leonie Mikael, Ali Nabavizadeh, Roger J. Packer, Adam C. Resnick, Brian Rood, Arastoo Vossough, Spyridon Bakas, and Marius George Linguraru

Published
2023
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45. A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Author

Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson, Aliaksandr Lubneuski, Karthik Viswanathan, Rocky Breslow, Nastaran Khalili, Sina Bagheri, Debanjan Haldar, Meen Chul Kim, Sherjeel Arif, Rachel Madhogarhia, Thinh Q. Nguyen, Elizabeth A. Frenkel, Zeinab Helili, Jessica Harrison, Keyvan Farahani, Marius George Linguraru, Ulas Bagci, Yury Velichko, Jeffrey Stevens, Sarah E. S. Leary, Robert M. Lober, Stephani Campion, Amy A. Smith, Denise Morinigo, Brian Rood, Kimberly Diamond, Ian F. Pollack, Melissa Williams, Arastoo Vossough, Jeffrey B. Ware, Sabine Müller 0002, Phillip B. Storm, Allison P. Heath, Angela J. Waanders, Jena V. Lilly, Jennifer L. Mason, Adam C. Resnick, and Ali Nabavizadeh

Published
2023
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50. Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers

Author

Debanjan Haldar, Anahita Fathi Kazerooni, Sherjeel Arif, Ariana Familiar, Rachel Madhogarhia, Nastaran Khalili, Sina Bagheri, Hannah Anderson, Ibraheem Salman Shaikh, Aria Mahtabfar, Meen Chul Kim, Wenxin Tu, Jefferey Ware, Arastoo Vossough, Christos Davatzikos, Phillip B. Storm, Adam Resnick, and Ali Nabavizadeh

Subjects
Radiomics, Radiogenomics, Pediatric low-grade glioma, Unsupervised machine learning, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Introduction: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. Methods: Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. Results: K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p < 0.05) as well as the gene expression of BRAF (p

Published
2023
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