Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy.
Postgraduation School in Radiodiagnostics, University of Rome Tor Vergata, Viale Oxford 81, 00133 Rome, Italy.
Curr Oncol. 2023 Feb 22;30(3):2673-2701. doi: 10.3390/curroncol30030203.
The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages of the diagnostic and therapeutic process. Although the histological investigation will remain difficult to replace, in the near future the radiomic approach will allow a complementary, repeatable and non-invasive characterization of the lesion, assisting oncologists and neurosurgeons in selecting the best therapeutic option and the correct molecular target in chemotherapy. AI-driven tools are already playing an important role in surgical planning, delimiting the extent of the lesion (segmentation) and its relationships with the brain structures, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life. Finally, AI-assisted models allow the prediction of complications, recurrences and therapeutic response, suggesting the most appropriate follow-up. Looking to the future, AI-powered models promise to integrate biochemical and clinical data to stratify risk and direct patients to personalized screening protocols.
人工智能（AI）的应用正在加速向个体化脑肿瘤管理的范式转变，为每个个体实现最佳的肿瘤功能平衡。基于 AI 的模型可以积极影响诊断和治疗过程的不同阶段。虽然组织学研究仍难以替代，但在不久的将来，放射组学方法将允许对病变进行补充、可重复和非侵入性的特征描述，帮助肿瘤学家和神经外科医生选择最佳的治疗方案和化疗中的正确分子靶点。人工智能驱动的工具已经在手术计划中发挥着重要作用，划定病变的范围（分割）及其与脑结构的关系，从而允许进行尽可能激进的精确脑部手术，以保留生活质量。最后，人工智能辅助模型可以预测并发症、复发和治疗反应，从而为最合适的随访提供建议。展望未来，人工智能驱动的模型有望整合生化和临床数据，对风险进行分层，并指导患者进行个性化的筛查方案。
