PET-CT radiomics features.

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

无创鉴别非小细胞肺癌（non-small cell lung cancer, NSCLC）的鳞状细胞癌（squamous cell carcinoma, SCC）与腺癌（adenocarcinoma, ADC）亚型，可为不适于有创诊断操作的患者带来获益。因此，本研究评估了基于PET/CT的放射组学模型的预测性能。本研究旨在区分肺腺癌与鳞状细胞癌的组织学亚型，采用四种不同的机器学习技术。研究共回顾性分析了255例非小细胞肺癌患者，并将其随机分为训练集（n=177）与验证集（n=78）。研究提取了放射组学特征，并采用最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）方法进行特征筛选。随后，基于四种不同的机器学习技术构建模型，并通过准确率、灵敏度、特异度以及曲线下面积（area under the curve, AUC）等评估指标确定性能最优的算法。采用德龙检验（DeLong test）对各模型的效能进行评估与比较。基于预测效能与临床实用性最优的模型构建列线图，并通过校准曲线进行验证。结果显示，在放射组学模型的验证队列中，逻辑回归分类器具有更优的预测能力。联合模型（AUC=0.870）的预测性能优于临床模型（AUC=0.848）与放射组学模型（AUC=0.774）。本研究发现，经逻辑回归分类器优化的联合模型在非小细胞肺癌组织学亚型分类中表现出最优效能。