Peng M, Wang M, Yang X, Wang Y, Xie L, An W, Ge F, Yang C, Wang K
PET-CT/MRI Department, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, China.
Scientific Research Center Department, Beijing General Electric Company, No.2 Yongchang North Road, Yizhuang Economic and Technological Development Zone, Daxing District, Beijing, 102200, China.
Clin Radiol. 2025 Jul;86:106915. doi: 10.1016/j.crad.2025.106915. Epub 2025 Apr 2.
To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients and estimating progression-free survival (PFS) and overall survival (OS) in PD-L1-positive patients undergoing first-line immunotherapy.
We retrospectively analysed 143 NSCLC patients who underwent pretreatment F-fluorodeoxyglucose (F-FDG) PET/CT scans, of whom 86 were PD-L1-positive. Clinical data collected included gender, age, smoking history, Tumor-Node-Metastases (TNM) staging system, pathologic types, laboratory parameters, and PET metabolic parameters. Four machine learning algorithms-Bayes, logistic, random forest, and Supportsupport vector machine (SVM)-were used to build models. The predictive performance was validated using receiver operating characteristic (ROC) curves. Univariate and multivariate Cox analyses identified independent predictors of OS and PFS in PD-L1-positive expression patients undergoing immunotherapy, and a nomogram was created to predict OS.
A total of 20 models were built for predicting PD-L1 expression. The clinical combined PET/CT radiomics model based on the SVM algorithm performed best (area under curve for training and test sets: 0.914 and 0.877, respectively). The Cox analyses showed that smoking history independently predicted PFS. SUVmean, monocyte percentage and white blood cell count were independent predictors of OS, and the nomogram was created to predict 1-year, 2-year, and 3-year OS based on these three factors.
We developed PET/CT-based machine learning models to help predict PD-L1 expression in NSCLC patients and identified independent predictors of PFS and OS in PD-L1-positive patients receiving immunotherapy, thereby aiding precision treatment.
开发一种基于正电子发射断层扫描/计算机断层扫描（PET/CT）的放射组学模型，用于预测非小细胞肺癌（NSCLC）患者程序性细胞死亡配体1（PD-L1）的表达，并估计接受一线免疫治疗的PD-L1阳性患者的无进展生存期（PFS）和总生存期（OS）。
我们回顾性分析了143例接受过治疗前氟脱氧葡萄糖（F-FDG）PET/CT扫描的NSCLC患者，其中86例为PD-L1阳性。收集的临床数据包括性别、年龄、吸烟史、肿瘤-淋巴结-转移（TNM）分期系统、病理类型、实验室参数和PET代谢参数。使用四种机器学习算法——贝叶斯、逻辑回归、随机森林和支持向量机（SVM）——构建模型。使用受试者操作特征（ROC）曲线验证预测性能。单因素和多因素Cox分析确定了接受免疫治疗的PD-L1阳性表达患者OS和PFS的独立预测因素，并创建了一个列线图来预测OS。
共构建了20个预测PD-L1表达的模型。基于SVM算法的临床联合PET/CT放射组学模型表现最佳（训练集和测试集的曲线下面积分别为0.914和0.877）。Cox分析表明，吸烟史独立预测PFS。SUVmean、单核细胞百分比和白细胞计数是OS的独立预测因素，并基于这三个因素创建了列线图来预测1年、2年和3年的OS。
我们开发了基于PET/CT的机器学习模型，以帮助预测NSCLC患者的PD-L1表达，并确定接受免疫治疗的PD-L1阳性患者PFS和OS的独立预测因素，从而有助于精准治疗。
