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1. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Author

Baid, Ujjwal, Ghodasara, Satyam, Mohan, Suyash, Bilello, Michel, Calabrese, Evan, Colak, Errol, Farahani, Keyvan, Kalpathy-Cramer, Jayashree, Kitamura, Felipe C., Pati, Sarthak, Prevedello, Luciano M., Rudie, Jeffrey D., Sako, Chiharu, Shinohara, Russell T., Bergquist, Timothy, Chai, Rong, Eddy, James, Elliott, Julia, Reade, Walter, Schaffter, Thomas, Yu, Thomas, Zheng, Jiaxin, Moawad, Ahmed W., Coelho, Luiz Otavio, McDonnell, Olivia, Miller, Elka, Moron, Fanny E., Oswood, Mark C., Shih, Robert Y., Siakallis, Loizos, Bronstein, Yulia, Mason, James R., Miller, Anthony F., Choudhary, Gagandeep, Agarwal, Aanchal, Besada, Cristina H., Derakhshan, Jamal J., Diogo, Mariana C., Do-Dai, Daniel D., Farage, Luciano, Go, John L., Hadi, Mohiuddin, Hill, Virginia B., Iv, Michael, Joyner, David, Lincoln, Christie, Lotan, Eyal, Miyakoshi, Asako, Sanchez-Montano, Mariana, Nath, Jaya, Nguyen, Xuan V., Nicolas-Jilwan, Manal, Jimenez, Johanna Ortiz, Ozturk, Kerem, Petrovic, Bojan D., Shah, Chintan, Shah, Lubdha M., Sharma, Manas, Simsek, Onur, Singh, Achint K., Soman, Salil, Statsevych, Volodymyr, Weinberg, Brent D., Young, Robert J., Ikuta, Ichiro, Agarwal, Amit K., Cambron, Sword C., Silbergleit, Richard, Dusoi, Alexandru, Postma, Alida A., Letourneau-Guillon, Laurent, Perez-Carrillo, Gloria J. Guzman, Saha, Atin, Soni, Neetu, Zaharchuk, Greg, Zohrabian, Vahe M., Chen, Yingming, Cekic, Milos M., Rahman, Akm, Small, Juan E., Sethi, Varun, Davatzikos, Christos, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Freymann, John B., Kirby, Justin S., Wiestler, Benedikt, Crivellaro, Priscila, Colen, Rivka R., Kotrotsou, Aikaterini, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Fathallah-Shaykh, Hassan, Wiest, Roland, Jakab, Andras, Weber, Marc-Andre, Mahajan, Abhishek, Menze, Bjoern, Flanders, Adam E., and Bakas, Spyridon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of $60,000 collectively., Comment: 19 pages, 2 figures, 1 table

Published
2021

3. Feasibility and Cost Analysis of Portable MRI Implementation in a Remote Setting in Canada

Author

DesRoche, Chloe N., Johnson, Ana P., Hore, Elizabeth B., Innes, Elaine, Silver, Ian, Tampieri, Donatella, Kwan, Benjamin Y.M., Jimenez, Johanna Ortiz, Boyd, J. Gordon, and Islam, Omar

Abstract

ABSTRACTObjective:To conduct feasibility and cost analysis of portable MRI implementation in a remote setting where MRI access is otherwise unavailable.Methods:Portable MRI (ultra-low field, 0.064T) was installed in Weeneebayko General Hospital, Moose Factory, Ontario. Adult patients, presenting with any indication for neuroimaging, were eligible for study inclusion. Scanning period was from November 14, 2021, to September 6, 2022. Images were sent via a secure PACS network for Neuroradiologist interpretation, available 24/7. Clinical indications, image quality, and report turnaround time were recorded. A cost analysis was conducted from a healthcare system’s perspective in 2022 Canadian dollars, comparing cost of portable MRI implementation to transporting patients to a center with fixed MRI.Results:Portable MRI was successfully implemented in a remote Canadian location. Twenty-five patients received a portable MRI scan. All studies were of diagnostic quality. No clinically significant pathologies were identified on any of the studies. However, based on clinical presentation and limitations of portable MRI resolution, it is estimated that 11 (44%) of patients would require transfer to a center with fixed MRI for further imaging workup. Cost savings were $854,841 based on 50 patients receiving portable MRI over 1 year. Five-year budget impact analysis showed nearly $8 million dollars saved.Conclusions:Portable MRI implementation in a remote setting is feasible, with significant cost savings compared to fixed MRI. This study may serve as a model to democratize MRI access, offer timely care and improved triaging in remote areas where conventional MRI is unavailable.

Published
2024
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4. Anatomical Variations of the Vertebral Artery in the Upper Cervical Spine: Clinical Relevance for Procedures Targeting the C1/C2 and C2/C3 Joints.

Author

Elgueta, Maria Francisca, Jimenez, Johanna Ortiz, Wang, Nina Nan, Lara, Almudena Pérez, Chankowsky, Jeffrey, Charghi, Roshanak, Tran, De Q., Finlayson, Roderick J., Ortiz Jimenez, Johanna, and Pérez Lara, Almudena

Abstract

Background and Objectives: Accidental breach of the vertebral artery (VA) during the performance of cervical pain blocks can result in significant morbidity. Whereas anatomical variations have been described for the foraminal (V2) segment of the VA, those involving its V3 portion (between the C2 transverse process and dura) have not been investigated and may be of importance for procedures targeting the third occipital nerve or the lateral atlantoaxial joint.Methods: Five hundred computed tomography angiograms of the neck performed in patients older than 50 years for the management of cerebrovascular accident or cervical trauma (between January 2010 and May 2016) were retrospectively and independently reviewed by 2 neuroradiologists. Courses of the VA in relation to the lateral aspect of the C2/C3 joint and the posterior surface of the C1/C2 joint were examined. For the latter, any medial encroachment of the VA (or one of its branches) was noted. The presence of a VA loop between C1 and C2 and its distance from the upper border of the superior articular process (SAP) of C3 were also recorded. If the VA loop coursed posteriorly, its position in relation to 6 fields found on the lateral aspects of the articular pillars of C2 and C3 was tabulated.Results: At the C1/C2 level, the VA coursed medially over the lateral quarter of the dorsal joint surface in 1% of subjects (0.6% and 0.4% on the left and right sides, respectively; P = 0.998). A VA loop originating between C1 and C2 was found to travel posteroinferiorly over the anterolateral aspect of the inferior articular pillar of C2 in 55.5% of patients on the left and 41.9% on the right side (P < 0.001), as well as over the SAP of C3 in 0.4% of subjects. When present in the quadrant immediately cephalad to the C3 SAP, VA loops coursed within 2.0 ± 1.5 and 3.3 ± 2.5 mm on the left and right sides, respectively, of its superior aspect (P < 0.001).Conclusions: The VA commonly travels adjacent to areas targeted by third occipital nerve procedures and more rarely over the access point for lateral atlantoaxial joint injections. Modifications to existing techniques may reduce the risk of accidental VA breach. [ABSTRACT FROM AUTHOR]

Published
2018
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5. CT-based Radiomics Model with Machine Learning for Predicting Primary Treatment Failure in Diffuse Large B-cell Lymphoma

Author

Santiago, Raoul, Jimenez, Johanna Ortiz, Forghani, Reza, Muthukrishnan, Nikesh, Corpo, Olivier Del, Karthigesu, Shairabi, Haider, Muhammad Yahya, Reinhold, Caroline, and Assouline, Sarit

Abstract

•CT-based radiomics with machine learning classifier is able to accurately predict primary refractory diffuse large B cell lymphomas (DLBCL).•The radiomics model exhibits a better discrimination for refractory DLBCL identification compared to available standard clinical criteria.

Published
2021
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