Forestieri Marta, Napolitano Antonio, Tomà Paolo, Bascetta Stefano, Cirillo Marco, Tagliente Emanuela, Fracassi Donatella, D'Angelo Paola, Casazza Ines
Imaging Department, Bambino Gesù Children's Hospital, IRCCS, 00165 Rome, Italy.
Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, 00165 Rome, Italy.
Diagnostics (Basel). 2023 Dec 27;14(1):61. doi: 10.3390/diagnostics14010061.
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.
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.
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%.
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.
本研究旨在分析慢性非细菌性骨髓炎（CNO）骨病变的纹理特征，这些病变在短tau反转恢复（STIR）序列上表现为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来。
我们纳入了一组66例确诊为CNO的患者和一组28例疑似骨骼外系统性疾病的患者。所有检查均在1.5T MRI扫描仪上进行。使用开源的3D Slicer软件版本4.10.2，在CNO病变和红骨髓上采样感兴趣区域（ROI）。使用Pyradiomics进行纹理分析（TA）。我们在九个经典ML分类器和一个深度学习（DL）神经网络（NN）上应用了优化搜索网格算法。使用准确率（ACC）、AUC-ROC曲线、F1分数、阳性预测值（PPV）、平均绝对误差（MAE）和均方根误差（RMSE）评估模型的性能。此外，我们使用Shapley加法解释来深入了解预测模型的行为。
对于两种类型的信号高增强的表征和分类，Boruta算法为每个ROI序列组合选择了大多数预测特征。NN获得了总体最佳分类结果，ACC = 0.91，AUC = 0.93，95%CI为0.91 - 0.94，F1分数 = 0.94，PPV = 93.8%。在经典ML方法中，集成学习器显示出较高的模型性能；具体而言，表现最佳的分类器是Stack（ST），ACC = 0.85，AUC = 0.81，95%CI为0.8 - 0.84，F1分数 = 0.9，PPV = 90%。
我们的结果显示了ML方法在辨别水肿样病变方面的潜力，特别是在区分儿科人群中CNO病变与造血骨髓变化方面。神经网络显示了总体最佳结果，而基于梯度提升和随机森林作为主要估计器以及逻辑回归作为最终估计器的堆叠分类器在其他ML方法中取得了最佳结果。
