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1. Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Author

LaBella, Dominic, Baid, Ujjwal, Khanna, Omaditya, McBurney-Lin, Shan, McLean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel Hoda, Altes, Talissa, Bhalerao, Radhika, Dhemesh, Yaseen, Godfrey, Devon, Hilal, Fathi, Floyd, Scott, Janas, Anastasia, Kazerooni, Anahita Fathi, Kirkpatrick, John, Kent, Collin, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anwar, Syed Muhammad, Bergquist, Timothy, Chen, Sully Francis, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Khalili, Nastaran, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Liu, Xinyang, Mahtabfar, Aria, Meier, Zeke, Moawad, Ahmed W., Mongan, John, Piraud, Marie, Shinohara, Russell Takeshi, Wiggins, Walter F., Abayazeed, Aly H., Akinola, Rachel, Jakab, András, Bilello, Michel, de Verdier, Maria Correia, Crivellaro, Priscila, Davatzikos, Christos, Farahani, Keyvan, Freymann, John, Hess, Christopher, Huang, Raymond, Lohmann, Philipp, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I., Hussain, Ali, Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, de Oliveira, Eduardo Portela, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D'Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam E., Wiestler, Benedikt, Bakas, Spyridon, and Calabrese, Evan

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

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

Published
2024

2. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Author

Li, Hongwei Bran, Conte, Gian Marco, Anwar, Syed Muhammad, Kofler, Florian, Ezhov, Ivan, van Leemput, Koen, Piraud, Marie, Diaz, Maria, Cole, Byrone, Calabrese, Evan, Rudie, Jeff, Meissen, Felix, Adewole, Maruf, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W., Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M., Wiest, Roland, Kirschke, Jan, Colen, Rivka R., Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva, Javier, Colak, Meyer Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Yu, Thomas, Baid, Ujjwal, Bakas, Spyridon, Linguraru, Marius George, Menze, Bjoern, Iglesias, Juan Eugenio, and Wiestler, Benedikt

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

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions., Comment: Technical report of BraSyn

Published
2023

3. The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Ehrlich, Stefan K, Reinke, Annika, Oswald, Eva, Waldmannstetter, Diana, Hoelzl, Florian, Horvath, Izabela, Turgut, Oezguen, Shit, Suprosanna, Bukas, Christina, Yang, Kaiyuan, Paetzold, Johannes C., de da Rosa, Ezequiel, Mekki, Isra, Vinayahalingam, Shankeeth, Kassem, Hasan, Zhang, Juexin, Chen, Ke, Weng, Ying, Durrer, Alicia, Cattin, Philippe C., Wolleb, Julia, Sadique, M. S., Rahman, M. M., Farzana, W., Temtam, A., Iftekharuddin, K. M., Adewole, Maruf, Anwar, Syed Muhammad, Baid, Ujjwal, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Li, Hongwei Bran, Moawad, Ahmed W, Conte, Gian-Marco, Farahani, Keyvan, Eddy, James, Sheller, Micah, Pati, Sarthak, Karagyris, Alexandros, Aristizabal, Alejandro, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Johanson, Elaine, Meier, Zeke, Familiar, Ariana, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Fatade, Abiodun, Omidiji, Olubukola, Lagos, Rachel Akinola, Olatunji, O O, Khanna, Goldey, Kirkpatrick, John, Alonso-Basanta, Michelle, Rashid, Arif, Bornhorst, Miriam, Nabavizadeh, Ali, Lepore, Natasha, Palmer, Joshua, Porras, Antonio, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Calabrese, Evan, Rudie, Jeffrey David, Linguraru, Marius George, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern H

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

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge., Comment: 14 pages, 6 figures

Published
2023

4. 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

5. Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging

Author

Salehinejad, Hojjat, Ho, Edward, Lin, Hui-Ming, Crivellaro, Priscila, Samorodova, Oleksandra, Arciniegas, Monica Tafur, Merali, Zamir, Suthiphosuwan, Suradech, Bharatha, Aditya, Yeom, Kristen, Mamdani, Muhammad, Wilson, Jefferson, and Colak, Errol

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

Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death. Accurate diagnosis in patients with suspected fractures by computed tomography (CT) is critical to patient management. In this paper, we propose a deep convolutional neural network (DCNN) with a bidirectional long-short term memory (BLSTM) layer for the automated detection of cervical spine fractures in CT axial images. We used an annotated dataset of 3,666 CT scans (729 positive and 2,937 negative cases) to train and validate the model. The validation results show a classification accuracy of 70.92% and 79.18% on the balanced (104 positive and 104 negative cases) and imbalanced (104 positive and 419 negative cases) test datasets, respectively., Comment: This paper is accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI) 2021

Published
2020

8. Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography

Author

Hamghalam, Mohammad, primary, Moreland, Robert, additional, Gomez, David, additional, Simpson, Amber, additional, Lin, Hui Ming, additional, Jandaghi, Ali Babaei, additional, Tafur, Monica, additional, Vlachou, Paraskevi A., additional, Wu, Matthew, additional, Brassil, Michael, additional, Crivellaro, Priscila, additional, Mathur, Shobhit, additional, Hosseinpour, Shahob, additional, and Colak, Errol, additional

Published
2024
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9. Current Practice Patterns, Challenges, and Need for Education in Performing and Reporting Advanced Pelvic US and MRI to Investigate Endometriosis: A Survey by the Canadian Association of Radiologists Endometriosis Working Group.

Author

Crivellaro, Priscila Sacilotto, Rehman, Iffat, Chang, Silvia, Duigenan, Shauna, Holmes, Signy, Hurrell, Casey, Kielar, Ania Z., Leonardi, Mathew, Pang, Emily, Shergill, Arvind, and Al-Arnawoot, Basma

Subjects
*ALLIED health education, *DIAGNOSIS of endometriosis, *ENDOMETRIOSIS, *WORK experience (Employment), *ULTRASONIC imaging, *PROFESSIONS, *PROFESSIONAL employee training, *MAGNETIC resonance imaging, *SURVEYS, *MEDICAL protocols, *SCALE analysis (Psychology), *DESCRIPTIVE statistics, *PHYSICIAN practice patterns, *PELVIS
Abstract

Purpose: The Canadian Association of Radiologists (CAR) Endometriosis Working Group developed a national survey to evaluate current practice patterns associated with imaging endometriosis using advanced pelvic ultrasound and MRI to inform forthcoming clinical practice guidelines for endometriosis imaging. Methods: The anonymous survey consisted of 36 questions and was distributed electronically to CAR members. The survey contained a mix of multiple choice, Likert scale and open-ended questions intended to collect information about training and certification, current practices and protocols associated with imaging endometriosis, opportunities for quality improvement and continuing professional development. Descriptive statistics were used to summarize the results. Results: Canadian radiologists were surveyed about their experience with imaging endometriosis. A total of 89 responses were obtained, mostly from Ontario and Quebec. Most respondents were community radiologists, and almost 33% were in their first five years of practice. Approximately 38% of respondents reported that they or their institution performed advanced pelvic ultrasound for endometriosis, with most having done so for less than 5 years, and most having received training during residency or fellowship. 70% of respondents stated they currently interpret pelvic endometriosis MRI, with most having 1-5 years of experience. Conclusion: Many radiologists in Canada do not perform dedicated imaging for endometriosis. This may be due to a lack of understanding of the benefits and limited access to training. However, dedicated imaging can improve patient outcomes and decrease repeated surgeries. The results highlight the importance of developing guidelines for these imaging techniques and promoting a multidisciplinary approach to endometriosis management. [ABSTRACT FROM AUTHOR]

Published
2024
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10. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, Menze, Bjoern, Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern

Abstract

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada., Comment: 5 pages, 1 figure

Published
2023

11. Standardized Reporting on the Preoperative CT Assessment of Potential Living Renal Transplant Donors: Can We Create a Universal Report Standard to Meet the Needs of Transplant Urologists?

Author

Benhabib, Hadas, Crivellaro, Priscila Sacilotto, Osman, Heba, Gunaseelan, Senthujan, Chung, Andrew, Lee, Jason Y., Colak, Errol, Leung, Vincent, O'Sullivan, Joseph, Walsh, Cynthia, and Kielar, Ania

Subjects
*KIDNEY radiography, *PREOPERATIVE care, *RESEARCH, *REPORT writing, *UROLOGISTS, *COMPARATIVE studies, *QUALITY assurance, *DESCRIPTIVE statistics, *COMPUTED tomography, *ORGAN donors
Abstract

Purpose: Determine whether standardized template reporting for the preoperative assessment of potential living renal transplant donors improves the comprehensiveness of radiology reports to meet the needs of urologists performing renal transplants. Methods: Urologist and radiologist stakeholders from renal transplant centres in our province ratified a standardized reporting template for evaluation of potential renal donors. Three centres (A, B, and C) were designated "intervention" groups. Centre D was the control group, given employment of a site-specific standardized template prior to study commencement. Up to 100 consecutive CT scan reports per centre, pre- and post-implementation of standardized reporting, were evaluated for reporting specific outcome measures. Results: At baseline, all intervention groups demonstrated poor reporting of urologist-desired outcome measures. Centre A discussed 5/13 variables (38%), Centre B discussed 6/13 variables (46%), and Centre C only discussed 1/13 variables (8%) with ≥90% reliability. The control group exhibited consistent reporting, with 11/13 variables (85%) reported at ≥90% reliability. All institutions in the intervention group exhibited excellent compliance to structured reporting post-template implementation (Centres A = 95%, B = 100%, and C = 77%, respectively). Additionally, all intervention centres demonstrated a significant improvement in the comprehensiveness of reports post-template implementation, with statistically significant increases in the reporting of all variables under-reported at baseline (P >.01). Conclusion: Standardized templates across our province for CT scans of potential renal donors promote completeness of reports. Radiologists can reliably provide our surgical colleagues with needed preoperative anatomy and incidental findings, helping to determine suitable transplant donors and reduce potential complications associated with organ retrieval. Visual Abstract [ABSTRACT FROM AUTHOR]

Published
2023
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12. Supplemental Material - Current Practice Patterns, Challenges, and Need for Education in Performing and Reporting Advanced Pelvic US and MRI to Investigate Endometriosis: A Survey by the Canadian Association of Radiologists Endometriosis Working Group

Author

Crivellaro, Priscila Sacilotto, Rehman, Iffat, Chang, Silvia, Duigenan, Shauna, Holmes, Signy, Hurrell, Casey, Kielar, Ania Z., Leonardi, Mathew, Pang, Emily, Shergill, Arvind, and Al-Arnawoot, Basma

Subjects
110320 Radiology and Organ Imaging, FOS: Clinical medicine
Abstract

Supplemental Material for Current Practice Patterns, Challenges, and Need for Education in Performing and Reporting Advanced Pelvic US and MRI to Investigate Endometriosis: A Survey by the Canadian Association of Radiologists Endometriosis Working Group by Priscila Sacilotto Crivellaro, Iffat Rehman, Silvia Chang, Shauna Duigenan, Signy Holmes, Casey Hurrell, Ania Z. Kielar, Mathew Leonardi, Emily Pang, Arvind Shergill, and Basma Al-Arnawoot in Canadian Association of Radiologists Journal

Published
2023
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13. Supplemental Material - Standardized Reporting on the Preoperative CT Assessment of Potential Living Renal Transplant Donors: Can We Create a Universal Report Standard to Meet the Needs of Transplant Urologists?

Author

Benhabib, Hadas, Crivellaro, Priscila Sacilotto, Osman, Heba, Gunaseelan, Senthujan, Chung, Andrew, Lee, Jason Y., Colak, Errol, Leung, Vincent, O’Sullivan, Joseph, Walsh, Cynthia, and Kielar, Ania

Subjects
110320 Radiology and Organ Imaging, FOS: Clinical medicine
Abstract

Supplemental Material for Standardized Reporting on the Preoperative CT Assessment of Potential Living Renal Transplant Donors: Can We Create a Universal Report Standard to Meet the Needs of Transplant Urologists? by Hadas Benhabib, Priscila Sacilotto Crivellaro, Heba Osman, Senthujan Gunaseelan, Andrew Chung, Jason Y. Lee, Errol Colak, Vincent Leung, Joseph O'Sullivan, Cynthia Walsh, and Ania Kielar in Canadian Association of Radiologists Journal

Published
2023
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14. Preparedness of Residents and Medical Students for the Transition to Competence by Design in Diagnostic Radiology Post-Graduate Medical Education

Author

Bentley, Helena, primary, Lee, Juvel, additional, Supersad, Alanna, additional, Yu, Hang, additional, Dobson, Jessica L., additional, Wong, Scott A., additional, Stewart, Matthew, additional, Vatturi, Sri Sannihita, additional, Lebel, Kiana, additional, Crivellaro, Priscila, additional, Khatchikian, Aline D., additional, Hague, Cameron J., additional, Taylor, Jana, additional, and Probyn, Linda, additional

Published
2022
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17. Deep Sequential Learning For Cervical Spine Fracture Detection On Computed Tomography Imaging

Author

Salehinejad, Hojjat, primary, Ho, Edward, additional, Lin, Hui-Ming, additional, Crivellaro, Priscila, additional, Samorodova, Oleksandra, additional, Arciniegas, Monica Tafur, additional, Merali, Zamir, additional, Suthiphosuwan, Suradech, additional, Bharatha, Aditya, additional, Yeom, Kristen, additional, Mamdani, Muhammad, additional, Wilson, Jefferson, additional, and Colak, Errol, additional

Published
2021
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18. Interrater Agreement of CT Grading of Blunt Splenic Injuries: Does the AAST Grading Need to Be Reimagined?

Author

Adams-McGavin, R. Chris, Tafur, Monica, Vlachou, Paraskevi A., Wu, Matthew, Brassil, Michael, Crivellaro, Priscila, Lin, Hui-Ming, Gomez, David, and Colak, Errol

Subjects
*STATISTICS, *RETROSPECTIVE studies, *DESCRIPTIVE statistics, *RESEARCH funding, *SPLENIC rupture, *COMPUTED tomography, *DATA analysis software
Abstract

Introduction: The Revised Organ Injury Scale (OIS) of the American Association for Surgery of Trauma (AAST) is the most widely accepted classification of splenic trauma. The objective of this study was to evaluate inter-rater agreement for CT grading of blunt splenic injuries. Methods: CT scans in adult patients with splenic injuries at a level 1 trauma centre were independently graded by 5 fellowship trained abdominal radiologists using the AAST OIS for splenic injuries – 2018 revision. The inter-rater agreement for AAST CT injury score, as well as low-grade (IIII) versus high-grade (IV-V) splenic injury was assessed. Disagreement in two key clinical scenarios (no injury versus injury, and high versus low grade) were qualitatively reviewed to identify possible sources of disagreement. Results: A total of 610 examinations were included. The inter-rater absolute agreement was low (Fleiss kappa statistic 0.38, P < 0.001), but improved when comparing agreement between low and high grade injuries (Fleiss kappa statistic of 0.77, P <.001). There were 34 cases (5.6%) of minimum two-rater disagreement about no injury vs injury (AAST grade ≥ I). There were 46 cases (7.5%) of minimum two-rater disagreement of low grade (AAST grade I-III) versus high grade (AAST grade IV-V) injuries. Likely sources of disagreement were interpretation of clefts versus lacerations, peri-splenic fluid versus subcapsular hematoma, application of adding multiple low grade injuries to higher grade injuries, and identification of subtle vascular injuries. Conclusion: There is low absolute agreement in grading of splenic injuries using the existing AAST OIS for splenic injuries. [ABSTRACT FROM AUTHOR]

Published
2024
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19. A National Survey of Canadian Diagnostic Radiology Program Directors on the Transition to Competence by Design in Diagnostic Radiology Post-Graduate Medical Education.

Author

Bentley, Helena, Lee, Juvel, Supersad, Alanna, Yu, Hang, Wong, Scott A., Stewart, Matthew, Vatturi, Sri Sannihita, Crivellaro, Priscila, Khatchikian, Aline D., Hague, Cameron J., Taylor, Jana, and Probyn, Linda

Subjects
*MEDICAL radiology, *STATISTICS, *PROFESSIONAL employee training, *DIAGNOSTIC imaging, *DESCRIPTIVE statistics, *ALLIED health personnel, *MEDICAL education
Published
2023
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20. Preparedness of Residents and Medical Students for the Transition to Competence by Design in Diagnostic Radiology Post-Graduate Medical Education.

Author

Bentley, Helena, Lee, Juvel, Supersad, Alanna, Yu, Hang, Dobson, Jessica L., Wong, Scott A., Stewart, Matthew, Vatturi, Sri Sannihita, Lebel, Kiana, Crivellaro, Priscila, Khatchikian, Aline D., Hague, Cameron J., Taylor, Jana, and Probyn, Linda

Subjects
*STATISTICS, *MEDICAL students, *HEALTH occupations students, *CROSS-sectional method, *INTERNSHIP programs, *CLINICAL competence, *GRADUATE education, *SCALE analysis (Psychology), *DESCRIPTIVE statistics, *OUTCOME-based education, *HOSPITAL radiological services, *MEDICAL education
Abstract

Introduction: This needs assessment evaluated residents' and medical students' knowledge of Competence by Design (CBD), perceived benefits of and challenges or barriers to the transition to CBD for residents, and perceived overall preparedness for the transition to CBD in diagnostic radiology. Materials and Methods: All diagnostic radiology residents and medical students in Canada were eligible to participate in this national cross-sectional, questionnaire-based needs assessment. Knowledge of CBD was evaluated through participants' self-reported rating of their knowledge of CBD on a 5-point Likert scale. Perceived benefits of and challenges or barriers to the transition to CBD for residents were rank ordered. Participants' overall self-reported preparedness for the transition to CBD was assessed on a 5-point Likert scale. Data were summarized by descriptive statistics and bivariate analyses were conducted as appropriate. Results: Ninety-four residents (n = 77) and medical students (n = 17) participated in this needs assessment. Participants' mean ± standard deviation self-reported rating of their overall knowledge of CBD was 2.86 ±.94. Provision of meaningful feedback to learners and learners' ability to identify their own educational needs were among the highest ranked perceived benefits of the transition to CBD, while demands on time and increased frequency of evaluation were among the highest ranked perceived challenges or barriers to the transition to CBD. Few participants reported being either "prepared" (4.7%) or "somewhat prepared" (14.0%) for the transition to CBD. Conclusion: Preparedness for the transition to CBD in diagnostic radiology may be improved. Targeted interventions to augment the preparedness of residents and medical students should be considered. [ABSTRACT FROM AUTHOR]

Published
2023
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22. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) .

Author

Li HB, Conte GM, Anwar SM, Kofler F, Ezhov I, van Leemput K, Piraud M, Diaz M, Cole B, Calabrese E, Rudie J, Meissen F, Adewole M, Janas A, Kazerooni AF, LaBella D, Moawad AW, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Johanson E, Meier Z, Davatzikos C, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh HM, Wiest R, Kirschke J, Colen RR, Kotrotsou A, Lamontagne P, Marcus D, Milchenko M, Nazeri A, Weber MA, Mahajan A, Mohan S, Mongan J, Hess C, Cha S, Villanueva-Meyer J, Colak E, Crivellaro P, Jakab A, Albrecht J, Anazodo U, Aboian M, Yu T, Chung V, Bergquist T, Eddy J, Albrecht J, Baid U, Bakas S, Linguraru MG, Menze B, Iglesias JE, and Wiestler B

Abstract

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Published
2023

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