Department of Medical Physics and Clinical Engineering, Royal Liverpool and Broadgreen University Hospitals NHS Trust, 1st Floor Duncan Building, L7 8XP, Liverpool, UK; Department of Physics, The Oliver Lodge, University of Liverpool, Oxford St, L69 7ZE, Liverpool, UK.
Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Crown Street, L69 3BX, Liverpool, UK.
Comput Biol Med. 2018 Nov 1;102:151-156. doi: 10.1016/j.compbiomed.2018.09.024. Epub 2018 Sep 25.
BACKGROUND/AIMS: Uveal melanoma is fatal in almost 50% of patients. We previously developed a prognostic model to predict all-cause mortality. The aim of this study was to improve our model by predicting metastatic death as a cause-specific event distinct from other causes of death.
Patients treated in Liverpool were included if they resided in England, Scotland or Wales and if their uveal melanoma involved the choroid. They were flagged at the National Health Service Cancer Registry, which automatically informed us of the date and cause of death of any deceased patients. A semiparametric Markov multi-state model was fitted. Two different baseline hazard rates were assumed, with state transition-specific covariates. For both failure types, age at treatment and sex were used. For the metastatic death case, these factors were added: anterior margin position, largest basal tumour diameter, tumour thickness, extra-ocular extension, presence of epithelioid melanoma cells, presence of closed connective tissue loops, increased mitotic count, chromosome 3 loss, and chromosome 8q gain. Missing data required a multiple-imputation procedure.
The cohort comprised 4161 patients, 893 of whom died of metastastic disease with another 772 dying of other causes. The optimism-corrected, bootstrapped C-index for metastatic death prediction was 0.86, denoting very good discriminative performance. Bootstrapped calibration curves at two and five years also showed very good performance.
Our improved model provides reliable, personalised metastatic death prognostication using clinical, histological and genetic information, and it can be used as a decision support tool to individualize patient care in a clinical environment.
背景/目的:葡萄膜黑色素瘤几乎导致 50%的患者死亡。我们之前开发了一种预测总死亡率的预后模型。本研究的目的是通过预测转移性死亡作为与其他死亡原因不同的特定原因事件来改进我们的模型。
如果患者居住在英格兰、苏格兰或威尔士,并且葡萄膜黑色素瘤涉及脉络膜,则将其纳入利物浦治疗的患者。他们在国家卫生服务癌症登记处被标记,该登记处会自动通知我们任何已故患者的死亡日期和原因。拟合了半参数马尔可夫多状态模型。假设了两种不同的基线风险率,并带有与状态转换相关的协变量。对于两种失败类型,均使用治疗时的年龄和性别。对于转移性死亡病例,还添加了以下因素:前边缘位置、最大基底肿瘤直径、肿瘤厚度、眼外扩展、上皮样黑色素瘤细胞存在、闭合结缔组织环存在、有丝分裂计数增加、3 号染色体缺失和 8q 染色体获得。缺失数据需要进行多次插补处理。
该队列包括 4161 名患者,其中 893 名死于转移性疾病,另有 772 名死于其他原因。经乐观校正的转移性死亡预测的 bootstrap 校正 C 指数为 0.86,表明具有非常好的判别性能。两年和五年的 bootstrap 校准曲线也表现出非常好的性能。
我们改进的模型使用临床、组织学和遗传信息提供可靠的、个性化的转移性死亡预测,并可在临床环境中作为决策支持工具用于个体化患者护理。