Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for the prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumour histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumour morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumour subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumour-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumour segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumour classification may inform prognosis and treatment response for precision medicine.

放射组学是指从放射扫描中高通量提取定量特征，广泛用于寻找成像生物标志物以预测临床结果。当前的放射组学特征的再现性和普遍性有限，因为大多数特征依赖于成像模式和肿瘤组织学，使得它们对扫描协议的变化敏感。在这里，我们提出了新的放射学特征，这些特征是专门为确保不同组织和成像对比度的兼容性而设计的。这些特征提供了肿瘤形态和空间异质性的系统表征。在一项针对 1,682 名患者的国际多机构研究中，我们发现并验证了三种恶性肿瘤和两种主要成像方式的四种统一成像亚型。这些肿瘤亚型在常规治疗后表现出不同的分子特征和预后。在接受免疫疗法治疗的晚期肺癌中，与其他亚型相比，一种亚型与生存率的提高和肿瘤浸润淋巴细胞的增加有关。深度学习能够实现自动肿瘤分割和可重复的亚型识别，这有助于实际实施。统一的放射学肿瘤分类可以为精准医学的预后和治疗反应提供信息。