文章：

基于机器学习的头颈部鳞状细胞癌颈部淋巴结分类：采用特征选择优化的影像组学方法

Machine Learning-Based Classification of Cervical Lymph Nodes in HNSCC: A Radiomics Approach with Feature Selection Optimization

原文发布日期：20 August 2025

DOI: 10.3390/cancers17162711

类型: Article

开放获取: 是

英文摘要：

Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) diagnosis and treatment rely heavily on computed tomography (CT) imaging to evaluate tumor characteristics and lymph node (LN) involvement, crucial for staging, prognosis, and therapy planning. Conventional LN evaluation methods based on morphological criteria such as size, shape, and anatomical location often lack sensitivity for early metastasis detection. This study leverages radiomics to extract quantitative features from CT images, addressing the limitations of subjective assessment and aiming to enhance LN classification accuracy while potentially reducing reliance on invasive histopathology. Methods: We analyzed 234 LNs from 27 HNSCC patients, deriving 120 features per node, resulting in over 25,000 data points classified into reactive, pathologic, and pathologic with extracapsular spread classes. Considering the challenges of high dimensionality and limited dataset size, more than 44,000 experiments systematically optimized machine learning models, feature selection methods, and hyperparameters, including ensemble approaches to strengthen classification robustness. A Pareto front strategy was employed to balance diagnostic accuracy with significant feature reduction. Results: The optimal model, validated via 5-fold cross-validation, achieved a balanced accuracy of 0.90, an area under the curve (AUC) of 0.93, and an F1-score of 0.88 using only five radiomics features. Conclusions: This interpretable approach aligns well with clinical radiology practice, demonstrating strong potential for clinical application in noninvasive LN classification in HNSCC.

摘要翻译： 

背景/目的：头颈部鳞状细胞癌（HNSCC）的诊断与治疗高度依赖计算机断层扫描（CT）成像来评估肿瘤特征及淋巴结（LN）受累情况，这对分期、预后及治疗规划至关重要。传统的淋巴结评估方法基于大小、形态和解剖位置等形态学标准，常对早期转移检测缺乏敏感性。本研究利用影像组学从CT图像中提取定量特征，以解决主观评估的局限性，旨在提高淋巴结分类准确性，同时可能减少对侵入性组织病理学的依赖。方法：我们分析了27例HNSCC患者的234个淋巴结，每个节点提取120个特征，生成超过25,000个数据点，并将其分类为反应性、病理性及伴包膜外扩散的病理性淋巴结。针对高维数据与有限样本量的挑战，通过超过44,000次实验系统优化了机器学习模型、特征选择方法和超参数，包括采用集成学习增强分类稳健性。采用帕累托前沿策略平衡诊断准确性与特征精简度。结果：经5折交叉验证的最优模型仅使用五个影像组学特征，即达到0.90的平衡准确率、0.93的曲线下面积（AUC）和0.88的F1分数。结论：这种可解释的方法与临床放射学实践高度契合，展现了在HNSCC无创淋巴结分类中良好的临床应用潜力。

原文链接：

Machine Learning-Based Classification of Cervical Lymph Nodes in HNSCC: A Radiomics Approach with Feature Selection Optimization