目的:本研究的目的是分析慢性非细菌性骨髓炎(CNO)骨病变的质地特征,确定为短tau反转恢复(STIR)序列上信号强度改变的区域,并通过机器学习(ML)和深度学习(DL)分析将它们与骨髓生长相关的变化区分开。
方法:我们纳入了一组66例确诊为CNO的患者和一组28例疑似骨骼外系统疾病的患者。所有检查均在1.5TMRI扫描仪上进行。使用开源3D切片器软件版本4.10.2,对CNO病变和红骨髓的ROI进行采样。使用Pyradiacomics进行纹理分析(TA)。我们在九个经典ML分类器和深度学习(DL)神经网络(NN)上应用了优化搜索网格算法。使用准确性(ACC)评估模型的性能,AUC-ROC曲线,F1分数,正预测值(PPV),平均绝对误差(MAE)和均方根误差(RMSE)。此外,我们使用Shapley加性解释来深入了解预测模型的行为。
结果:大多数预测特征是通过Boruta算法为ROI序列的每种组合选择的,用于表征和分类两种类型的信号超强度。总体最佳分类结果是通过NN获得的,ACC=0.91,AUC=0.93,95%CI为0.91-0.94,F1评分=0.94,PPV=93.8%。在经典的ML方法之间,集合学习者表现出很高的模型性能;具体地说,表现最好的分类器是Stack(ST),ACC=0.85,AUC=0.81,95%CI为0.8-0.84,F1评分=0.9,PPV=90%.
结论:我们的结果显示ML方法在识别水肿样病变方面的潜力,特别是通过区分儿科人群中的CNO病变和造血骨髓变化。神经网络显示出总体最好的结果,而堆叠分类器,基于梯度提升和随机森林作为主估计,Logistic回归作为最终估计,在其他ML方法之间取得了最好的结果。
OBJECTIVE: The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis.
METHODS: We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model\'s performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model.
RESULTS: Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91-0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8-0.84, F1-score = 0.9, PPV = 90%.
CONCLUSIONS: Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.