Camps Jordi, Jiménez-Franco Andrea, García-Pablo Raquel, Joven Jorge, Arenas Meritxell
Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Dr. Josep Laporte 2, 43204 Reus, Catalonia, Spain.
Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Dr. Josep Laporte 2, 43204 Reus, Catalonia, Spain.
Biochim Biophys Acta Mol Basis Dis. 2025 Aug;1871(6):167841. doi: 10.1016/j.bbadis.2025.167841. Epub 2025 Apr 9.
Despite advances in cancer diagnosis and treatment, the disease remains a major health challenge. Integrating multi-omics, radiomics, and artificial intelligence has improved detection, prognosis, and treatment monitoring. Molecular multi-omics provides insights into tumor biology, while radiomics extracts imaging features for outcome prediction. Liquid biopsy and circulating tumor DNA aid early detection and personalized therapy. Artificial intelligence-driven models integrate data to identify biomarkers and guide precision oncology. Despite challenges like cost and data integration, future advancements aim to enhance resolution, scalability, and non-invasive diagnostics. This mini-review explores these methodologies, their clinical impact, and their potential in personalized cancer treatment.
尽管癌症诊断和治疗取得了进展，但该疾病仍然是一项重大的健康挑战。整合多组学、放射组学和人工智能已改善了检测、预后和治疗监测。分子多组学提供了对肿瘤生物学的见解，而放射组学提取成像特征用于结果预测。液体活检和循环肿瘤DNA有助于早期检测和个性化治疗。人工智能驱动的模型整合数据以识别生物标志物并指导精准肿瘤学。尽管存在成本和数据整合等挑战，但未来的进展旨在提高分辨率、可扩展性和非侵入性诊断。本综述探讨了这些方法、它们的临床影响以及它们在个性化癌症治疗中的潜力。
