Cao Jiashi, Li Qiong, Zhang Huili, Wu Yanyan, Wang Xiang, Ding Saisai, Chen Song, Xu Shaochun, Duan Guangwen, Qiu Defu, Sun Jiuyi, Shi Jun, Liu Shiyuan
Department of Orthopedics, Navy Medical Center, the Navy Medical University, No. 338 Huaihai West Road, Shanghai 200052, China.
Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center/Cancer Hospital, No. 651 Dongfeng East Road, Guangzhou 510060, China.
J Bone Oncol. 2024 Mar 28;45:100599. doi: 10.1016/j.jbo.2024.100599. eCollection 2024 Apr.
Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them.
Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score.
Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively.
The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.
脊柱多发性骨髓瘤（MM）和转移瘤是两种具有相似影像学特征的常见癌症类型，需要进行鉴别诊断以确保精准治疗。本研究旨在建立用于有效区分它们的放射组学模型。
本研究纳入了来自两家医疗机构的263例患者，其中127例为脊柱MM患者，136例为脊柱转移瘤患者。其中，将来自机构I的210例患者作为内部训练队列，将来自机构II的53例患者作为外部验证队列。收集并回顾了对比增强T1加权成像（CET1）和T2加权成像（T2WI）序列。基于从CET1和T2WI图像中提取的1037个放射组学特征，构建了逻辑回归（LR）、自适应增强（AB）、支持向量机（SVM）、随机森林（RF）以及基于多核学习的SVM（MKL-SVM）。通过五折交叉验证对超参数进行调整。通过准确度（ACC）、灵敏度（SEN）、特异度（SPE）、ROC曲线下面积（AUC）、YI、阳性预测值（PPV）、阴性预测值（NPV）和F1分数比较不同放射组学模型的诊断效率。
基于单序列，RF模型优于所有其他模型。所有基于T2WI图像的模型表现均优于基于CET1的模型。通过合并CET1和T2WI序列提高了所有模型的效率，MKL-SVM模型表现最佳，ACC、AUC和F1分数分别为0.862、0.870和0.874。
基于MRI构建的放射组学模型在区分脊柱MM和转移瘤方面取得了令人满意的诊断性能，显示出在个体化诊断和治疗方面的广阔应用前景。
