Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China.
Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, People's Republic of China.
BMC Cancer. 2022 Mar 11;22(1):258. doi: 10.1186/s12885-022-09352-3.
Accurate prognosis assessment is essential for surgically resected intrahepatic cholangiocarcinoma (ICC) while published prognostic tools are limited by modest performance. We therefore aimed to establish a novel model to predict survival in resected ICC based on readily-available clinical parameters using machine learning technique.
A gradient boosting machine (GBM) was trained and validated to predict the likelihood of cancer-specific survival (CSS) on data from a Chinese hospital-based database using nested cross-validation, and then tested on the Surveillance, Epidemiology, and End Results (SEER) database. The performance of GBM model was compared with that of proposed prognostic score and staging system.
A total of 1050 ICC patients (401 from China and 649 from SEER) treated with resection were included. Seven covariates were identified and entered into the GBM model: age, tumor size, tumor number, vascular invasion, number of regional lymph node metastasis, histological grade, and type of surgery. The GBM model predicted CSS with C-Statistics ≥ 0.72 and outperformed proposed prognostic score or system across study cohorts, even in sub-cohort with missing data. Calibration plots of predicted probabilities against observed survival rates indicated excellent concordance. Decision curve analysis demonstrated that the model had high clinical utility. The GBM model was able to stratify 5-year CSS ranging from over 54% in low-risk subset to 0% in high-risk subset.
We trained and validated a GBM model that allows a more accurate estimation of patient survival after resection compared with other prognostic indices. Such a model is readily integrated into a decision-support electronic health record system, and may improve therapeutic strategies for patients with resected ICC.
准确的预后评估对于手术切除的肝内胆管癌（ICC）至关重要，而已发表的预后工具的性能有限。因此，我们旨在使用机器学习技术，基于易于获得的临床参数建立一种新的模型来预测可切除 ICC 的生存情况。
使用嵌套交叉验证，在基于中国医院数据库的数据上训练和验证梯度提升机（GBM），以预测癌症特异性生存（CSS）的可能性，并在监测、流行病学和最终结果（SEER）数据库上进行测试。GBM 模型的性能与提出的预后评分和分期系统进行了比较。
共纳入 1050 例接受切除术治疗的 ICC 患者（中国 401 例，SEER 649 例）。确定了 7 个协变量并输入 GBM 模型：年龄、肿瘤大小、肿瘤数量、血管侵犯、区域淋巴结转移数量、组织学分级和手术类型。GBM 模型预测 CSS 的 C-统计量≥0.72，在研究队列中表现优于提出的预后评分或系统，甚至在缺失数据的亚组中也是如此。预测概率与观察生存率的校准图表明高度一致。决策曲线分析表明该模型具有较高的临床实用性。GBM 模型能够分层 5 年 CSS，从低风险亚组的 54%以上到高风险亚组的 0%。
我们训练和验证了一种 GBM 模型，与其他预后指标相比，该模型能够更准确地估计切除术后患者的生存情况。这种模型可以很容易地集成到决策支持电子病历系统中，并可能改善接受切除的 ICC 患者的治疗策略。
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