Department of Medical Ultrasound, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China.
Ultrasound Med Biol. 2024 Oct;50(10):1506-1514. doi: 10.1016/j.ultrasmedbio.2024.05.026. Epub 2024 Jul 25.
To develop and validate a machine learning (ML) model based on high-frequency ultrasound (HFUS) images with the aim to identify the functional status of parathyroid glands (PTGs) in secondary hyper-parathyroidism (SHPT) patients.
This retrospective study enrolled 60 SHPT patients (27 female, 33 male; mean age: 51.2 years) with 184 PTGs detected from February 2016 to June 2022. All enrollments underwent single-photon emission computed tomography/computed tomography and contrast-enhanced ultrasound examinations. The PTGs were randomly divided into training (n = 147) and testing datasets (n = 37). Four effective ML classifiers were used and combined models incorporating multi-modal HFUS visual signs and radiomics features was constructed based on the optimal classifier. Model performance was compared in terms of discrimination, calibration and clinical utility. The Shapley additive explanation method was used to explain and visualize the main predictors of the optimal model.
This model, using a random forest classifier algorithm, outperformed other classifiers. Based on optimal classifier features, the model constructed from ultrasound visual and ML features achieved a favorable performance in the prediction of hyper-functioning PTGs. Compared with the traditional visual model, the ultrasound-based ML model achieved significant (p = 0.03) improvement (area under the curve: 0.859 vs. 0.629) and higher sensitivity (100.0% vs. 94.1%) and accuracy (86.5% vs. 67.6%). Among the predictors attributed to model development, large size and high echogenic heterogeneity of PTGs in ultrasonographic images were more often associated with high risk of hyper-functioning PTGs.
The ultrasound-based ML model for identifying hyper-functioning PTGs in SHPT patients showed good performance and interpretability using high-frequency ultrasonographic images, which may facilitate clinical management.
开发和验证一种基于高频超声（HFUS）图像的机器学习（ML）模型，旨在识别继发性甲状旁腺功能亢进症（SHPT）患者甲状旁腺（PTG）的功能状态。
本回顾性研究纳入了 2016 年 2 月至 2022 年 6 月期间 60 例 SHPT 患者（27 名女性，33 名男性；平均年龄：51.2 岁），共检测到 184 个 PTG。所有患者均接受单光子发射计算机断层扫描/计算机断层扫描和对比增强超声检查。PTG 随机分为训练集（n=147）和测试集（n=37）。使用了四种有效的 ML 分类器，并基于最优分类器构建了结合多模态 HFUS 视觉特征和放射组学特征的组合模型。从区分度、校准度和临床实用性方面比较了模型性能。采用 Shapley 加性解释方法解释和可视化最优模型的主要预测因子。
该模型使用随机森林分类器算法，优于其他分类器。基于最优分类器特征，基于超声视觉和 ML 特征构建的模型在预测高功能 PTG 方面表现出良好的性能。与传统的视觉模型相比，基于超声的 ML 模型在预测中取得了显著改善（曲线下面积：0.859 比 0.629，p=0.03）和更高的敏感性（100.0%比 94.1%）和准确性（86.5%比 67.6%）。在归因于模型开发的预测因子中，超声图像中 PTG 较大的大小和高回声异质性与高功能 PTG 的高风险更相关。
使用高频超声图像，基于超声的 ML 模型在识别 SHPT 患者高功能 PTG 方面表现出良好的性能和可解释性，可能有助于临床管理。
