Liang Meiheng, Wang Fei, Yang Yan, Wen Li, Wang Shunan, Zhang Dong
Department of Radiology, Xinqiao Hospital, Army Medical University, Chongqing, China.
Department of Radiology, Chongqing Shapingba Maternity & Child Healthcare Hospital, Chongqing, China.
Neuroradiology. 2025 Jul 9. doi: 10.1007/s00234-025-03698-8.
To establish an interpretable and non-invasive machine learning (ML) model using clinicoradiological predictors and magnetic resonance imaging (MRI) radiomics features to predict the consistency of pituitary macroadenomas (PMAs) preoperatively.
Total 350 patients with PMA (272 from Xinqiao Hospital of Army Medical University and 78 from Daping Hospital of Army Medical University) were stratified and randomly divided into training and test cohorts in a 7:3 ratio. The tumor consistency was classified as soft or firm. Clinicoradiological predictors were examined utilizing univariate and multivariate regression analyses. Radiomics features were selected employing the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. Logistic regression (LR) and random forest (RF) classifiers were applied to construct the models. Receiver operating characteristic (ROC) curves and decision curve analyses (DCA) were performed to compare and validate the predictive capacities of the models. A comparative study of the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) was performed. The Shapley additive explanation (SHAP) was applied to investigate the optimal model's interpretability.
The combined model predicted the PMAs' consistency more effectively than the clinicoradiological and radiomics models. Specifically, the LR-combined model displayed optimal prediction performance (test cohort: AUC = 0.913; ACC = 0.840). The SHAP-based explanation of the LR-combined model suggests that the wavelet-transformed and Laplacian of Gaussian (LoG) filter features extracted from TWI and CE-TWI occupy a dominant position. Meanwhile, the skewness of the original first-order features extracted from TWI (TWI_original_first-order_Skewness) demonstrated the most substantial contribution.
An interpretable machine learning model incorporating clinicoradiological predictors and multiparametric MRI (mpMRI)-based radiomics features may predict PMAs consistency, enabling tailored and precise therapies for patients with PMA.
利用临床放射学预测指标和磁共振成像（MRI）影像组学特征建立一种可解释的非侵入性机器学习（ML）模型，以术前预测垂体大腺瘤（PMA）的质地。
总共350例PMA患者（陆军军医大学新桥医院272例，陆军军医大学大坪医院78例）按7:3的比例分层并随机分为训练组和测试组。肿瘤质地分为软质或硬质。采用单因素和多因素回归分析来研究临床放射学预测指标。利用最小冗余最大相关（mRMR）和最小绝对收缩和选择算子（LASSO）算法选择影像组学特征。应用逻辑回归（LR）和随机森林（RF）分类器构建模型。进行受试者操作特征（ROC）曲线和决策曲线分析（DCA）以比较和验证模型的预测能力。对曲线下面积（AUC）、准确率（ACC）、灵敏度（SEN）和特异度（SPE）进行比较研究。应用Shapley加性解释（SHAP）来研究最优模型的可解释性。
联合模型比临床放射学模型和影像组学模型更有效地预测了PMA的质地。具体而言，LR联合模型表现出最佳的预测性能（测试组：AUC = 0.913；ACC = 0.840）。基于SHAP对LR联合模型的解释表明，从TWI和CE-TWI提取的小波变换和高斯拉普拉斯（LoG）滤波器特征占据主导地位。同时，从TWI提取的原始一阶特征的偏度（TWI_original_first-order_Skewness）贡献最大。
结合临床放射学预测指标和基于多参数MRI（mpMRI）的影像组学特征的可解释机器学习模型可以预测PMA的质地，为PMA患者提供个性化的精准治疗。
