Mahar Alyson L, Compton Carolyn, McShane Lisa M, Halabi Susan, Asamura Hisao, Rami-Porta Ramon, Groome Patti A
*Division of Cancer Care and Epidemiology, Cancer Research Institute, Queen's University, ON; Canada; †Arizona State University, Phoenix, Arizona; ‡Laboratory Medicine and Pathology, Mayo Clinic School of Medicine, Rochester, Minnesota; §Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland; ‖Department of Biostatistics and Bioinformatics, Duke University and Alliance Statistics and Data Center, Durham, North Carolina; ¶Division of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan; and #Department of Thoracic Surgery, Hospital Universitari Mutua Terrassa, Barcelona, Spain.
J Thorac Oncol. 2015 Nov;10(11):1576-89. doi: 10.1097/JTO.0000000000000652.
Accurate, individualized prognostication for lung cancer patients requires the integration of standard patient and pathologic factors, biological, genetic, and other molecular characteristics of the tumor. Clinical prognostic tools aim to aggregate information on an individual patient to predict disease outcomes such as overall survival, but little is known about their clinical utility and accuracy in lung cancer.
A systematic search of the scientific literature for clinical prognostic tools in lung cancer published from January 1, 1996 to January 27, 2015 was performed. In addition, web-based resources were searched. A priori criteria determined by the Molecular Modellers Working Group of the American Joint Committee on Cancer were used to investigate the quality and usefulness of tools. Criteria included clinical presentation, model development approaches, validation strategies, and performance metrics.
Thirty-two prognostic tools were identified. Patients with metastases were the most frequently considered population in non-small-cell lung cancer. All tools for small-cell lung cancer covered that entire patient population. Included prognostic factors varied considerably across tools. Internal validity was not formally evaluated for most tools and only 11 were evaluated for external validity. Two key considerations were highlighted for tool development: identification of an explicit purpose related to a relevant clinical population and clear decision points and prioritized inclusion of established prognostic factors over emerging factors.
Prognostic tools will contribute more meaningfully to the practice of personalized medicine if better study design and analysis approaches are used in their development and validation.
肺癌患者准确的个体化预后评估需要整合标准的患者和病理因素、肿瘤的生物学、遗传学及其他分子特征。临床预后工具旨在汇总个体患者的信息以预测疾病转归，如总生存期，但对于它们在肺癌中的临床实用性和准确性知之甚少。
对1996年1月1日至2015年1月27日发表的关于肺癌临床预后工具的科学文献进行系统检索。此外，还检索了基于网络的资源。采用美国癌症联合委员会分子建模工作组确定的先验标准来研究工具的质量和实用性。标准包括临床表现、模型开发方法、验证策略和性能指标。
共识别出32种预后工具。在非小细胞肺癌中，有转移的患者是最常被纳入研究的人群。所有小细胞肺癌的工具涵盖了整个患者群体。不同工具所包含的预后因素差异很大。大多数工具未对内部效度进行正式评估，仅11种工具评估了外部效度。工具开发突出了两个关键考虑因素：确定与相关临床人群相关的明确目的以及明确的决策点，并优先纳入已确立的预后因素而非新出现的因素。
如果在预后工具的开发和验证中采用更好的研究设计和分析方法，它们将对个性化医疗实践做出更有意义的贡献。
