[Application of Explainable Deep Learning in Differentiating Benign from Malignant Pulmonary Space-occupying Lesions and Classifying Pathological Subtypes of Lung Cancer].

内容摘要

The discrimination between benign and malignant pulmonary space-occupying lesions and the classification of pathological subtypes of lung cancer are critical for clinical decision-making. However, conventional methods often suffer from insufficient utilization of multi-source clinical data and poor interpretability of deep learning models. This study investigates the performance of interpretable deep learning algorithms in diagnosing benign versus malignant pulmonary space-occupying lesions and classifying pathological subtypes of lung cancer, using a hybrid architecture based on Tab-Transformer-designed for tabular data and Residual Multi-Layer Perceptron (ResMLP), referred to as TT-ResMLP. Data including radiological characteristics, medical history, and laboratory findings from 345 patients with pathologically confirmed pulmonary space-occupying lesions were collected. The dataset was randomly split into a development set and a test set at an 8:2 ratio. Stable features were selected using the Spearman correlation test and the Least Absolute Shrinkage and Selection Operator (LASSO). The Synthetic Minority Over-sampling Technique (SMOTE) was employed to balance the samples, and 10-fold cross-validation was used to enhance model generalizability. Models were constructed using the Tab-Transformer algorithm, the ResMLP algorithm, and the TT-ResMLP hybrid. Model performance was evaluated using receiver operating characteristic (ROC) curves, the area under the curve (AUC), accuracy, specificity, sensitivity, and micro-averaged ROC (micro-ROC). SHapley Additive exPlanations (SHAP) analysis was performed based on the optimal model. In the benign vs malignant diagnosis task, all three models performed well. The Tab-Transformer model demonstrated the best performance on the test set, followed by TT-ResMLP and ResMLP. SHAP analysis of the top-performing Tab-Transformer model revealed that the feature importance ranking was: age, pleural indentation, thrombin time, mean density, and ground-glass opacity. Pleural indentation contributed substantially to malignant diagnosis, and its contribution was further enhanced with increasing age and decreasing thrombin time. In the lung cancer subtype classification task, all three models exhibited excellent performance, with the TT-ResMLP hybrid showing the best overall performance. SHAP analysis further revealed that the Lung Imaging Reporting and Data System (Lung-RADS) category held high importance across all three pathological subtypes. Male gender was positively associated with the prediction of squamous cell carcinoma. Neuron-specific enolase (NSE) played a significant role in predicting small cell carcinoma. For adenocarcinoma, the diagnostic probability was positively correlated with the Lung-RADS category, a relationship more pronounced at lower prothrombin time (PT) values. In contrast, a negative correlation was observed in the squamous cell carcinoma and small cell carcinoma subgroups, although gender and NSE levels could enhance their contributory risk prediction. Analysis of feature decision boundaries indicated that the Lung-RADS grade possessed high discriminative power for identifying adenocarcinoma, whereas NSE demonstrated stronger discriminative ability for identifying small cell carcinoma. The TT-ResMLP hybrid architecture is effective for diagnosing the benign or malignant nature of pulmonary space-occupying lesions and classifying pathological subtypes of lung cancer. The model possesses good interpretability, aiding in the identification of key predictive features and unravelling their interactive mechanisms, thereby providing an effective tool for a deeper understanding of lung cancer biology and clinical decision support. 【中文题目：可解释性深度学习算法在肺占位性病变
良恶性诊断及肺癌病理亚型分类中的运用】 【中文摘要：背景与目的 肺占位性病变的良恶性鉴别与肺癌病理亚型分类是临床决策的关键，但传统方法存在多源临床数据利用不足及深度学习模型可解释性差的问题。本研究基于针对表格化数据设计的Transformer（Tab-Transformer）与残差多层感知器（Residual Multi-Layer Perceptron, ResMLP）的混合架构（TT-ResMLP），探讨可解释性深度学习算法在肺占位性病变良恶性诊断及肺癌病理亚型分类中的性能。方法 收集345例经病理证实的肺占位性病变患者的影像学特征、病史资料及实验室检查等数据，按8:2随机分为训练集和测试集。采用Spearman检验与最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）筛选稳定特征，使用合成少数类过采样技术（Synthetic Minority Over-sampling Technique, SMOTE）平衡样本，采用10折交叉验证提高模型泛化能力，选用Tab-Transformer算法、ResMLP算法、TT-ResMLP构建模型，通过受试者工作特征（receiver operating characteristic, ROC）曲线、曲线下面积（area under the curve, AUC）、准确率、特异性、敏感性和微平均ROC（micro-averaged ROC, micro-ROC）曲线评估模型性能，并基于最优模型进行SHAP（SHapley Additive exPlanations）特征分析。结果 良恶性诊断模型中，3种模型均表现良好，其中Tab-Transformer在测试集表现最优，TT-ResMLP和ResMLP次之；SHAP分析显示，表现最优的Tab-Transformer模型特征重要性依次是年龄、胸膜凹陷征、凝血酶时间、平均密度、磨玻璃样改变等，其中胸膜凹陷征有较高的恶性诊断贡献，且随年龄增长、凝血酶时间缩短，其贡献度进一步增强。在肺癌亚型分类任务中，3种模型均表现出优异性能，其中TT-ResMLP综合表现最优。SHAP分析进一步揭示，肺部影像报告和数据系统评分（Lung Imaging Reporting and Data System, Lung-RADS）在3种病理亚型中均具较高重要性；男性与鳞癌预测呈正相关；神经元特异性烯醇化酶（neuron-specific enolase, NSE）在小细胞癌预测中起重要作用。在腺癌中，诊断概率与Lung-RADS分级呈正相关，且在低凝血酶原时间值时更显著；而在鳞癌与小细胞癌亚组中呈负相关，但性别和NSE水平可增强其风险预测的贡献。特征决策边界分析显示，Lung-RADS分级在腺癌识别中具有较高的区分能力，而NSE在小细胞癌识别中展现出更强的区分能力。结论 TT-ResMLP混合架构能达到肺占位性病变的良恶性诊断及肺癌病理亚型分类的目的，模型具备良好的可解释性，有助于识别关键预测特征并揭示其交互机制，为深入理解肺癌生物学行为及临床辅助决策提供了有效工具。
】 【中文关键词：肺肿瘤；肺占位性病变；机器学习；特征诠释；良恶性诊断；深度学习】.