Jin Zhicheng, Wang Yongqing, Wang Yizhen, Mao Yangting, Zhang Fang, Yu Jing
Department of Nuclear Medicine, Second Affiliated Hospital, Dalian Medical University, Dalian, China.
School of Geophysics and Information Technology, China University of Geosciences, Beijing, China.
Front Med (Lausanne). 2022 Apr 18;9:874847. doi: 10.3389/fmed.2022.874847. eCollection 2022.
The purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods.
This retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively.
A total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: = 0.01 and = 0.001; Validation: = 0.018 and = 0.033), and no statistical difference was observed between the CT model and human experts ( = 0.187 and = 0.229, respectively).
The radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.
本研究旨在探讨18F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F-FDG PET/CT）图像放射组学在脊柱多发性骨髓瘤（MM）和骨转移（BM）鉴别诊断中的应用，以及该方法与传统方法相比能否提高分类诊断性能。
本回顾性研究收集了2017年1月至2021年1月期间131例患者的184个病灶。所有图像由两名具有20年经验的医生通过双盲法独立进行视觉评估，同时记录每个病灶的最大标准化摄取值（SUVmax）。通过手动方法分别从每个病灶的CT和PET图像感兴趣区（ROI）提取总共279个放射组学特征。经过可靠性测试后，采用最小绝对收缩和选择算子（LASSO）回归及10倍交叉验证进行特征降维和筛选。分别从CT图像和PET图像中导出CT和PET两个分类模型，并使用多变量逻辑回归算法构建。此外，通过结合PET模型和传统参数SUVmax构建ComModel。分别对三种分类诊断模型以及人类专家和SUVmax的性能进行评估和比较。
分别从CT和PET图像中选择了8个和10个特征用于构建放射组学模型。三种放射组学模型在训练组和验证组均取得了满意的性能（训练组：AUC：CT：0.909，PET：0.949，ComModel：0.973；验证组：AUC：CT：0.897，PET：0.929，ComModel：0.948）。此外，与人类专家相比，PET模型和ComModel在两组间的诊断性能有显著提高（训练组：P = 0.01和P = 0.001；验证组：P = 0.018和P = 0.033），而CT模型与人类专家之间未观察到统计学差异（分别为P = 0.187和P = 0.229）。
基于18F-FDG PET/CT图像构建的放射组学模型在MM和骨转移分类诊断中取得了满意的诊断性能。此外，放射组学模型与人类专家和PET传统参数SUVmax相比，诊断性能有显著提高。
