放射组学分析医学成像的定量特征，已迅速成为转化肿瘤学的新兴领域。放射组学已在多种肿瘤恶性肿瘤中进行了研究，因为它可能允许非侵入性肿瘤表征以及识别预测和预后生物标志物。在过去的几年里，关于机器学习在癌症患者历史的许多关键时刻的潜在临床应用的证据不断积累。然而，放射组学纳入临床决策过程仍然受到数据重复性低和研究变异性的限制。此外，对前瞻性验证和标准化的需求正在出现。在这篇叙述性综述中，我们总结了有关放射组学在高发病率癌症（乳腺癌和肺癌）筛查、诊断、分期、治疗选择、反应和临床结果评估中的应用的当前证据。我们还讨论了放射组学方法的利弊，针对可能使放射组学研究无效的关键问题提出了可能的解决方案，并提出了未来的观点。版权所有 © 2024。由 Elsevier B.V. 出版。

Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging. In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.Copyright © 2024. Published by Elsevier B.V.