基于LASSO-Logistic回归的垂体ACTH腺瘤术后预后不良的风险预测模型构建及验证-现代医学

基于LASSO-Logistic回归的垂体ACTH腺瘤术后预后不良的风险预测模型构建及验证

单位：南京大学医学院附属鼓楼医院神经外科, 江苏南京 210008

分类号：R739.41;R195.1

出版年·卷·期（页码）：2026·54·第二期（227-234）

摘要：

目的： 基于最小绝对收缩与选择算子回归(LASSO)结合Logistic回归分析，筛选垂体促肾上腺皮质激素(ACTH)腺瘤患者预后不良的危险因素。方法： 回顾性分析南京鼓楼医院神经外科2013年至2023年间收治的205例ACTH腺瘤患者。系统收集患者一般临床基线(年龄、性别等)、术前内分泌学指标(ACTH、皮质醇、尿游离皮质醇等)、肿瘤影像学(肿瘤大小、侵袭性、视神经受压等)及病理特征(Ki-67指数、p53表达、骨质疏松等)。将术后预后二分类为预后良好与预后不良。经单因素分析筛选候选变量，通过LASSO-Logistic回归结合10折交叉验证(10-fold cross-validation)筛选独立危险因素并构建多变量预测模型。采用受试者工作特征曲线下面积及准确率评估模型判别能力，通过校准曲线和Brier评分评价模型校准度，并结合决策曲线分析(DCA)评估模型的临床净获益；同时利用沙普利可解释方法(SHAP)方法解释模型特征重要性。结果： 术前ACTH、Ki-67、肿瘤大小、年龄、尿游离皮质醇和术前皮质醇与肿瘤复发相关。临床变量(发病年龄小、肿瘤最大径、腺瘤侵袭性、骨质疏松、ACTH及Ki-67升高)可以准确预测ACTH腺瘤患者的临床预后。Lasso回归分析结果表明术前ACTH是最具预测价值的因素。结论： 结合ACTH腺瘤的临床标志物及临床资料能够准确预测患者的临床预后。风险评估模型的建立为ACTH腺瘤患者的临床评估和随访提供了有价值的工具。

Objective: To identify risk factors associated with poor prognosis in patients with adrenocorticotropic hormone(ACTH) adenomas using least absolute shrinkage and selection operator(LASSO) regression combined with logistic regression, and to develop a clinical prediction model. Methods: A retrospective analysis was conducted on 205 patients with ACTH adenoma in the Department of Neurosurgery of Nanjing Drum Tower Hospital from 2013 to 2023. Clinical baseline characteristics(age, sex), preoperative endocrine parameters [ACTH, serum cortisol, urinary free cortisol(UFC)], radiological features(maximum tumor diameter, invasiveness, optic chiasm compression), and pathological variables(Ki-67 proliferation index, p53 expression, osteoporosis) were collected. The postoperative prognosis is dichotomized as good prognosis and poor prognosis. Univariate analysis is conducted using independent sample t-test and χ² test to screen candidate variables. Independent risk factors are selected through LASSO-Logistic regression combined with 10-fold cross-validation, and a multivariate prediction model is constructed; the discriminatory ability of the model is evaluated using are under the receiver operating characteristic curve, accuracy rate, etc., the calibration is verified using calibration curves, Brier score, etc., the clinical net benefit is assessed using decision curve analysis(DCA), and the importance of model features is analyzed using shapley additive explanations(SHAP) method. Results: Preoperative ACTH, Ki-67, tumor size, age, urine free cortisol and preoperative cortisol are associated with tumor recurrence. The comprehensive model of clinical variables(younger age of onset, maximum diameter of tumor, invasiveness of adenoma, osteoporosis, ACTH and Ki-67) can accurately predict the clinical prognosis of patients with ACTH adenoma. The analysis results of LASSO regression and machine learning models indicate that preoperative ACTH is the most predictive factor. Conclusion: Combining the clinical markers and clinical data of ACTH adenoma can accurately predict the clinical prognosis of patients. The establishment of the risk assessment model provides a valuable tool for the clinical evaluation and follow-up of patients with ACTH adenoma.

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