Ibrahim Muhammad, Muhammad Quratulain, Zamarud Aroosa, Eiman Hadia, Fazal Faizan
Department of Medicine, Rawalpindi Medical University, Rawalpindi, PAK.
Department of Neurosurgery, Stanford Health Care, Palo Alto, USA.
Cureus. 2023 Aug 27;15(8):e44214. doi: 10.7759/cureus.44214. eCollection 2023 Aug.
Glioblastoma multiforme (GBM), an aggressive brain tumor with high recurrence rates and limited survival, presents a pressing need for accurate and timely diagnosis. The interpretation of MRI can be complex and subjective. Artificial Intelligence (AI) has emerged as a promising solution, leveraging its potential to revolutionize diagnostic imaging. Radiomics treats images as numerical data and extracts intricate features from images, including subtle patterns that elude human observation. By integrating radiomics with genetics through radiogenomics, AI aids in tumor classification, identifying specific mutations and genetic traits. Furthermore, AI's impact extends to treatment planning. GBM's heterogeneity and infiltrative growth complicate delineation for treatment purposes. AI-driven segmentation techniques provide accurate 2D and 3D delineations, optimizing surgical and radiotherapeutic planning. Predictive features like angiogenesis and tumor volumes enable AI models to anticipate postop complications and survival rates. It can also aid in distinguishing posttreatment radiation effects from tumor recurrence. Despite these merits, concerns linger. The quality of medical data, transparency of AI techniques, and ethical considerations require thorough addressing. Collaborative efforts between neurosurgeons, data scientists, ethicists, and regulatory bodies are imperative for AI's ethical development and implementation. Transparent communication and patient consent are vital, fostering trust and understanding in AI-augmented medical care. In conclusion, AI holds immense promise in diagnosing and managing aggressive brain tumors like GBM. Its ability to analyze complex radiological data, integrate genetics, and aid in treatment planning underscores its potential to transform patient care. However, carefully considering ethical, technical, and regulatory aspects is crucial for realizing AI's full potential in oncology.
多形性胶质母细胞瘤（GBM）是一种侵袭性脑肿瘤，复发率高且生存期有限，因此迫切需要准确、及时的诊断。MRI的解读可能复杂且主观。人工智能（AI）已成为一种有前景的解决方案，利用其潜力彻底改变诊断成像。放射组学将图像视为数值数据，并从图像中提取复杂特征，包括人类难以观察到的细微模式。通过放射基因组学将放射组学与遗传学相结合，AI有助于肿瘤分类，识别特定突变和遗传特征。此外，AI的影响还扩展到治疗计划。GBM的异质性和浸润性生长使治疗目的的轮廓划定变得复杂。AI驱动的分割技术提供准确的二维和三维轮廓，优化手术和放射治疗计划。血管生成和肿瘤体积等预测特征使AI模型能够预测术后并发症和生存率。它还可以帮助区分治疗后放射效应与肿瘤复发。尽管有这些优点，但担忧仍然存在。医疗数据的质量、AI技术的透明度以及伦理考量都需要全面解决。神经外科医生、数据科学家、伦理学家和监管机构之间的合作努力对于AI的伦理发展和实施至关重要。透明的沟通和患者同意至关重要，有助于在AI辅助医疗中建立信任和理解。总之，AI在诊断和管理GBM等侵袭性脑肿瘤方面具有巨大潜力。它分析复杂放射学数据、整合遗传学以及辅助治疗计划的能力突显了其改变患者护理的潜力。然而，仔细考虑伦理、技术和监管方面对于在肿瘤学中充分发挥AI的潜力至关重要。
