Adewole Maruf, Rudie Jeffrey D, Gbdamosi Anu, Toyobo Oluyemisi, Raymond Confidence, Zhang Dong, Omidiji Olubukola, Akinola Rachel, Suwaid Mohammad Abba, Emegoakor Adaobi, Ojo Nancy, Aguh Kenneth, Kalaiwo Chinasa, Babatunde Gabriel, Ogunleye Afolabi, Gbadamosi Yewande, Iorpagher Kator, Calabrese Evan, Aboian Mariam, Linguraru Marius, Albrecht Jake, Wiestler Benedikt, Kofler Florian, Janas Anastasia, LaBella Dominic, Kzerooni Anahita Fathi, Li Hongwei Bran, Iglesias Juan Eugenio, Farahani Keyvan, Eddy James, Bergquist Timothy, Chung Verena, Shinohara Russell Takeshi, Wiggins Walter, Reitman Zachary, Wang Chunhao, Liu Xinyang, Jiang Zhifan, Familiar Ariana, Van Leemput Koen, Bukas Christina, Piraud Maire, Conte Gian-Marco, Johansson Elaine, Meier Zeke, Menze Bjoern H, Baid Ujjwal, Bakas Spyridon, Dako Farouk, Fatade Abiodun, Anazodo Udunna C
Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Department of Radiation Biology, Radiotherapy and Radiodiagnosis, University of Lagos, Lagos, Nigeria.
ArXiv. 2023 May 30:arXiv:2305.19369v1.
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.
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，诊断后的生存率不到两年。神经胶质瘤诊断具有挑战性，难以治疗，并且对传统疗法具有内在抗性。多年来为改善神经胶质瘤的诊断和治疗进行的广泛研究降低了全球北方地区的死亡率，而低收入和中等收入国家（LMIC）人群的生存机会保持不变，在撒哈拉以南非洲（SSA）人群中情况则更糟。神经胶质瘤的长期生存与在脑部MRI上识别适当的病理特征并经组织病理学确认有关。自2012年以来，脑肿瘤分割（BraTS）挑战赛一直在评估用于检测、表征和分类神经胶质瘤的最先进机器学习方法。然而，鉴于低质量MRI技术的广泛使用，目前尚不清楚这些最先进的方法能否在SSA广泛实施，低质量MRI技术会产生较差的图像对比度和分辨率，更重要的是，疾病在晚期出现的倾向以及SSA神经胶质瘤的独特特征（即疑似较高的大脑胶质瘤病发生率）。因此，BraTS-非洲挑战赛提供了一个独特的机会，通过BraTS挑战赛将来自SSA的脑部MRI神经胶质瘤病例纳入全球努力，以开发和评估用于在资源有限环境中检测和表征神经胶质瘤的计算机辅助诊断（CAD）方法，在这种环境中CAD工具更有可能改变医疗保健状况。
