Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
Am Soc Clin Oncol Educ Book. 2021 Mar;41:1-12. doi: 10.1200/EDBK_320593.
Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.
组织分析方法、图像分析、高通量分子分析和计算工具的进步使我们越来越能够捕捉和量化影响癌症风险、预后和治疗反应的个体间差异。整合来自多个来源（如家族史、人口统计学、种系变异、影像学特征）的患者特定信息的统计模型可以提供个体化的癌症风险预测，从而指导筛查和预防策略。通过计算机辅助解决方案，诊断成像的精确性、质量和标准化正在提高，并且多种基因预后和预测测试提高了对各种癌症类型的预后和治疗反应的预测。这些进展的一个共同主题是，将个体中等信息量的变量组合成更准确的多变量预测模型。机器学习的进步和大型数据集的可用性推动了该领域的快速发展。癌症基因组的分子剖析在临床上已经成为现实，并且现在常规地对分子靶标进行分析以选择各种靶向治疗的患者。这些由技术驱动的、越来越精确和定量的获益与风险估计，使患者和医生能够制定与患者价值观和期望相匹配的治疗策略。
