Gliomas are the most prevalent and aggressive form of primary brain tumors. The clinical challenge in managing patients with this disease revolves around the difficulty of diagnosis, both at onset and during treatment, and the scarcity of prognostic outcome indicators. Radiomics involves the extraction of quantitative features from medical images with the help of artificial intelligence, positioning it as a promising tool to be integrated into the care of glioma patients. Using data from 52 studies and 12,482 patients over two years, this review explores how radiomics can enhance the initial diagnosis of gliomas, especially helping to differentiate treatment stages that may be difficult for the human eye to do otherwise. Radiomics has also been able to identify patient-specific tumor molecular signatures for targeted treatments without the need for invasive surgical biopsy. Such an approach could lead to earlier interventions and more precise individualized therapies that are tailored to each patient. Additionally, it could be integrated into clinical practice to improve longitudinal diagnosis during treatment and predict tumor recurrence. Finally, radiomics has the potential to predict clinical outcomes, helping both patients and providers set realistic expectations. While this field is continuously evolving, future research should conduct such studies in larger, multi-institutional cohorts to enhance generalizability and applicability in clinical practice and focus on combining radiomics with other modalities to improve its predictive accuracy and clinical utility.
胶质瘤是最常见且最具侵袭性的原发性脑肿瘤。临床管理此类患者的主要挑战在于，无论是发病初期还是治疗过程中都难以准确诊断，且缺乏有效的预后结果指标。影像组学借助人工智能从医学影像中提取定量特征，使其成为有望整合到胶质瘤患者诊疗体系中的有力工具。本综述基于两年间52项研究、涉及12,482名患者的数据，探讨了影像组学如何提升胶质瘤的初诊水平，特别是在区分治疗阶段方面——这些细微差异往往难以通过人眼识别。影像组学还能在不进行侵入性手术活检的情况下，识别患者特异性肿瘤分子特征，为靶向治疗提供依据。这种方法有助于实现更早期的干预和更精准的个体化治疗方案。此外，影像组学可整合到临床实践中，以改善治疗期间的纵向诊断并预测肿瘤复发。最后，影像组学在预测临床结局方面具有潜力，能帮助医患双方建立合理的治疗预期。尽管该领域持续发展，未来研究应在更大规模的多机构队列中开展，以增强临床实践的普适性与适用性，并着重探索影像组学与其他模态技术的融合，从而提升其预测准确性与临床效用。
肿瘤(癌症)患者之家