College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.
College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.
Crit Rev Oncol Hematol. 2020 Apr;148:102908. doi: 10.1016/j.critrevonc.2020.102908. Epub 2020 Feb 17.
Despite advances in the breast cancer treatment, significant variability in patient outcomes remain. This results in significant stress to patients and clinicians. Treatment-specific clinical prediction models allow patients to be matched against historical outcomes of patients with similar characteristics; thereby reducing uncertainty by providing personalised estimates of benefits, harms, and prognosis. To achieve this objective, models need to be clinical-grade with evidence of accuracy, reproducibility, generalizability, and be user-friendly. A structured search was undertaken to identify treatment-specific clinical prediction models for therapeutic or adverse outcomes in breast cancer using clinicopathological data. Significant gaps in the presence of validated models for available treatments was identified, along with gaps in prediction of therapeutic and adverse outcomes. Most models did not have user-friendly tools available. With the aim being to facilitate the selection of the best medicine for a specific patient and shared-decision making, future research will need to address these gaps.
尽管乳腺癌治疗取得了进展，但患者结局仍存在显著差异。这给患者和临床医生带来了巨大的压力。针对特定治疗的临床预测模型可将患者与具有相似特征的患者的历史结局进行匹配；从而通过提供对获益、危害和预后的个性化估计来减少不确定性。为了实现这一目标，模型需要具有临床水平的准确性、可重复性、可推广性，并易于使用。使用临床病理数据，我们进行了一项有针对性的搜索，以确定针对乳腺癌治疗或不良结局的特定治疗的临床预测模型。研究发现，针对现有治疗方法的验证模型存在显著差距，对治疗和不良结局的预测也存在差距。大多数模型没有可用的易于使用的工具。目标是为特定患者选择最佳药物并进行共同决策，未来的研究需要解决这些差距。
