Background: Biopsy remains the gold standard for characterizing breast cancer, but it is invasive, costly, and may not fully capture tumor heterogeneity. Advances in artificial intelligence (AI) now allow for the extraction of biological and clinical information from medical images, raising the possibility of using imaging as a non-invasive alternative.Methods: A semi-systematic review was conducted to identify AI-based approaches applied to mammography (MM) and breast tomosynthesis (BT) for tumor subtyping, staging, and prognosis. A PubMed search retrieved 1091 articles, of which 81 studies met inclusion criteria (63 MM, 18 BT). Studies were analyzed by clinical target, modality, AI pipeline, number of cases, dataset type, and performance metrics (AUC, accuracy, or C-index).Results: Most studies focused on tumor subtyping, particularly receptor status and molecular classification. Contrast-enhanced spectral mammography (CESM) was frequently used in radiomics pipelines, while end-to-end deep learning (DL) approaches were increasingly applied to MM. Deep models achieved strong performance for ER/PR and HER2 status prediction, especially in large datasets. Fewer studies addressed staging or prognosis, but promising results were obtained for axillary lymph node (ALN) metastasis and pathological complete response (pCR). Multimodal and longitudinal approaches—especially those combining MM or BT with MRI or ultrasound—show improved accuracy but remain rare. Public datasets were used in only a minority of studies, limiting reproducibility.Conclusions: AI models can predict key tumor characteristics directly from MM and BT, showing promise as non-invasive tools to complement or even replace biopsy. However, challenges remain in terms of generalizability, external validation, and clinical integration. Future work should prioritize standardized annotations, larger multicentric datasets, and integration of histological or transcriptomic validation to ensure robustness and real-world applicability.
背景：活检仍是乳腺癌特征分析的金标准，但其具有侵入性、成本高昂且可能无法完全捕捉肿瘤异质性。人工智能（AI）的进步使得从医学影像中提取生物学和临床信息成为可能，这为将影像学作为非侵入性替代方法提供了潜在途径。 方法：本研究通过半系统性综述，识别应用于乳腺X线摄影（MM）和乳腺断层合成摄影（BT）的AI方法，以进行肿瘤亚型分型、分期和预后评估。通过PubMed检索获得1091篇文章，其中81项研究符合纳入标准（63项MM研究，18项BT研究）。研究按临床目标、成像模态、AI流程、病例数量、数据集类型和性能指标（AUC、准确率或C指数）进行分析。 结果：大多数研究集中于肿瘤亚型分型，特别是受体状态和分子分型。对比增强能谱乳腺X线摄影（CESM）常用于影像组学流程，而端到端深度学习（DL）方法越来越多地应用于MM。深度学习模型在预测ER/PR和HER2状态方面表现出色，尤其是在大型数据集中。针对分期或预后的研究较少，但在腋窝淋巴结（ALN）转移和病理完全缓解（pCR）预测方面已取得有前景的结果。多模态和纵向研究方法——特别是将MM或BT与MRI或超声相结合的方法——显示出更高的准确性，但仍较为罕见。仅少数研究使用了公共数据集，限制了结果的可重复性。 结论：AI模型可直接从MM和BT图像预测关键肿瘤特征，显示出作为非侵入性工具以补充甚至替代活检的潜力。然而，在泛化能力、外部验证和临床整合方面仍存在挑战。未来工作应优先考虑标准化标注、更大规模的多中心数据集，以及整合组织学或转录组学验证，以确保模型的稳健性和实际应用价值。
肿瘤(癌症)患者之家