The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
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2023 年脑肿瘤分割 (BraTS) 挑战：撒哈拉以南非洲患者群体的神经胶质瘤分割 (BraTS-Africa)
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
U. Anazodo
中科院分区：
文献类型：
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作者：
Maruf Adewole;J. Rudie;A. Gbadamosi;O. Toyobo;Confidence Raymond;Dong Zhang;O. Omidiji;Rachel Akinola;M. A. Suwaid;A. Emegoakor;Nancy Ojo;Kenneth Aguh;Chinasa Kalaiwo;G. Babatunde;A. Ogunleye;Yewande Gbadamosi;Kator P. Iorpagher;E. Calabrese;M. Aboian;M. Linguraru;Jake Albrecht;B. Wiestler;F. Kofler;A. Janas;D. Labella;Anahita Fathi Kzerooni;Hongwei Li;J. E. Iglesias;Keyvan Farahani;James A. Eddy;T. Bergquist;Verena Chung;R. Shinohara;Walter F. Wiggins;Zachary J. Reitman;C. Wang;Xinyang Liu;Zhifan Jiang;Ariana M. Familiar;K. V. Leemput;Christina Bukas;M. Piraud;G. Conte;E. Johansson;Zeke Meier;Bjoern H Menze;Ujjwal Baid;S. Bakas;Farouk Dako;A. Fatade;U. Anazodo
神经胶质瘤是最常见的原发性脑肿瘤类型。尽管神经胶质瘤相对罕见，但它们是最致命的癌症类型之一，诊断后生存率不到两年。神经胶质瘤诊断困难、治疗困难，并且对传统疗法具有固有的抵抗力。多年来为改善神经胶质瘤的诊断和治疗而进行的广泛研究降低了北半球的死亡率，而低收入和中等收入国家（LMIC）个体的生存机会保持不变，并且撒哈拉以南非洲（SSA）人群的生存机会明显更差。神经胶质瘤的长期生存与脑部 MRI 上适当病理特征的识别和组织病理学的确认有关。自 2012 年以来，脑肿瘤分割 (BraTS) 挑战赛评估了最先进的机器学习方法来检测、表征和分类神经胶质瘤。然而，目前尚不清楚最先进的方法是否可以在 SSA 中广泛实施，因为低质量 MRI 技术的广泛使用，产生的图像对比度和分辨率较差，更重要的是，晚期疾病的倾向以及 SSA 中胶质瘤的独特特征（即疑似脑胶质瘤病发生率较高）。因此，BraTS-非洲挑战赛提供了一个独特的机会，将 SSA 的脑 MRI 神经胶质瘤病例纳入全球努力中，通过 BraTS 挑战赛开发和评估计算机辅助诊断 (CAD) 方法，用于在资源有限的环境中检测和表征神经胶质瘤，在这些环境中 CAD 工具更有可能改变医疗保健的潜力。
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.