Guiyang Maternal and Child Health Care Hospital, Guiyang Children's Hospital, Guiyang, China.
Department of General Surgery, The Forth People's Hospital of Guiyang, Guiyang, China.
Sci Rep. 2023 Sep 8;13(1):14827. doi: 10.1038/s41598-023-40779-1.
Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.
准确的预后预测对于肺乳头状腺癌 (LPADC) 的治疗决策至关重要。本研究旨在使用集成机器学习和经典 Cox 回归模型预测 LPADC 的癌症特异性生存。此外，还评估了模型，以便根据定量数据为 LPADC 的个性化治疗提供建议。从 Surveillance, Epidemiology, and End Results 数据库中提取了 2004-2018 年诊断为 LPADC 的患者数据。样本集按 7:3 的比例随机分为训练集和验证集。选择了三种集成模型，即梯度提升生存（GBS）、随机生存森林（RSF）和额外生存树（EST）。此外，还使用 Cox 比例风险（CoxPH）回归构建了预后模型。使用 Harrell 一致性指数（C-index）、综合 Brier 评分（IBS）和时间依赖性接收器操作特征曲线下面积（time-dependent AUC）评估预测模型的性能。提供了一个用户友好的网络访问面板，以便轻松评估模型对生存和治疗建议的预测。共 3615 名患者被随机分为训练组和验证组（n=2530 和 1085）。在训练组和验证组中，额外生存树、RSF、GBS 和 CoxPH 模型均表现出良好的区分能力和校准度（时间依赖性 AUC 的平均值：>0.84 和>0.82；C-index：>0.79 和>0.77；IBS：<0.16 和<0.17）。RSF 和 GBS 模型在预测长期生存方面比 CoxPH 模型更一致。我们将开发的模型实现为网络应用程序，以便部署到临床实践中（可通过 https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ 访问）。所有四种预后模型均表现出良好的区分能力和校准度。在预测 LPADC 患者的长期癌症特异性生存方面，RSF 和 GBS 模型在所有模型中表现出最高的有效性。这种方法可能有助于制定 LPADC 的个性化治疗计划和预测预后。
