颈部和头部鳞状细胞癌（HNSCC）中的淋巴结外生长（ENE）与恶劣预后和治疗策略密切相关。深度学习可以在预测HNSCC中的ENE方面取得有希望的表现，但缺乏透明度和可解释性。本研究提出了一种名为EL-ENE的进化学习方法，用于建立一个更可解释的ENE预测模型，以辅助临床诊断。共有364例接受颈部淋巴结（LN）切除术的HNSCC患者，使用术前增强型计算机断层扫描图像进行检查。将778个LN分为训练集和测试集，比例为8：2。EL-ENE使用可继承的双目标组合遗传算法来进行支持向量机的最佳特征选择和参数设置。使用独立测试数据集比较了ENE预测模型和放射科医生的诊断性能。EL-ENE模型在ENE检测方面取得了80.00%的测试准确率、81.13%的敏感性和79.44%的特异性。三名放射科医生的平均诊断准确率为70.4%、敏感性为75.6%、特异性为67.9%。灰度纹理和LN的三维形态特征在预测ENE方面起到了关键作用。EL-ENE方法提供了一个准确、可理解和稳健的模型，用于预测HNSCC中的ENE，并使用可解释的放射组学特征来扩展临床知识。所提出的透明预测模型更可靠，可能会增加在日常临床实践中的接受度。©2023年。国际癌症影像学会（ICIS）。

Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis.There were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets.The EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE.The EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.© 2023. International Cancer Imaging Society (ICIS).