Roisman Laila C, Kian Waleed, Anoze Alaa, Fuchs Vered, Spector Maria, Steiner Roee, Kassel Levi, Rechnitzer Gilad, Fried Iris, Peled Nir, Bogot Naama R
The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
Ben-Gurion University of the Negev, Be'er Sheva, Israel.
NPJ Precis Oncol. 2023 Nov 21;7(1):125. doi: 10.1038/s41698-023-00473-x.
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
个性化医疗通过使治疗方法针对每个患者而彻底改变了肺癌领域的治疗方式。然而，鉴于临床方面的复杂性，如肿瘤分子特征、肿瘤微环境、预期不良事件、获得性或固有耐药机制、脑转移的发生、生物标志物的可用性有限以及联合治疗的选择，医生在为个体提供最佳治疗方案时面临着众多挑战。深度学习（机器学习的一个子集）和放射组学等创新的下一代技术的整合，有可能通过支持癌症治疗中的临床决策和精准治疗的实施，同时整合众多临床因素，来改变这一领域。在本综述中，我们简要解释了现有技术、在预测肺癌免疫治疗反应中使用这些技术的益处，以及在精准医学背景下预期的未来挑战。
