摘要
目的探讨基于多中心T1WI影像组学列线图治疗前预测骨肉瘤1年内复发的价值。方法回顾性分析2009年1月至2017年10月来自6个中心的107例接受新辅助化疗和手术切除后经组织学证实的骨肉瘤患者临床及影像资料,根据入组顺序,将来自先入组的4个中心(n=75)的患者作为训练组,另外2个中心(n=32)的患者作为验证组。基于治疗前T1WI提取的影像组学特征,于训练组中使用最小绝对收缩与选择算子算法(LASSO)进行降维后建立预测骨肉瘤手术后1年内复发的影像组学标签。使用单因素logistic回归筛选独立临床危险因素,使用多变量logistic回归纳入影像组学标签构建列线图预测骨肉瘤复发;使用受试者操作特征(ROC)曲线评估列线图和影像组学标签在训练组中的准确性,并通过验证组进行验证。采用校正曲线评估列线图预测和实际观察复发风险的一致性,采用决策曲线评估列线图的临床实用性。结果基于多中心的T1WI,提取了2个与骨肉瘤术后1年内复发相关的影像组学特征构成影像组学标签,包括1个灰度共生矩阵特征(L_G_1.0_GLCM_homogeneity1,权重值3.122)和1个灰度游程矩阵特征(GLRLM_RP,权重值-2.474)。融合影像组学标签和常规影像学特征(关节侵犯和血管周围浸润)构建列线图,在训练组和验证组中预测骨肉瘤术后1年内复发的ROC曲线下面积分别为0.884和0.821,校正曲线显示列线图在预测和实际观察之间具有良好的一致性。决策曲线分析表明,当风险阈值大于21%时,影像组学列线图具有较大的临床应用价值。结论基于T1WI影像组学列线图可作为非侵入性量化工具,于治疗前预测骨肉瘤在1年内的复发情况,为骨肉瘤的临床决策提供支持。
Objective To explore the value of a radiomics nomogram based on T1WI for prediction of the relapse of osteosarcoma after surgery within 1 year from multicenter data.Methods The imaging and clinical data of 107 patients with pathologica1ly confirmed osteosarcoma who received neoadjuvant chemotherapy before surgery from 6 hospitals from January 2009 to October 2017 were retrospectively analyzed.A training cohort consisted of 75 patients from firstly enrolled 4 hospitals and an independent validation cohort of 32 patients from other 2 hospitals.Pretreatment T1WI was used to extract radiomics features.Least absolute shrinkage and selection operator(LASSO)regression was applied to reduce the dimension and then the radiomics signature was constructed to predict the relapse of osteosarcoma after surgery within 1 year in training cohort.Independent clinical risk factors were screened using one-way logistic regression,and then a radiomics nomogram incorporated the radiomics signature and MRI characteristics was developed by multivariate logistic regression.The predictive nomogram was evaluated using receiver operating characteristic(ROC)curve in the training cohort,and validated in the independent validation cohort.The calibration curve was used to evaluate the agreement between prediction and actual observation and the decision curve was used to demonstrate the clinical usefulness.Results Based on T1WI from multicenter institutions,the radiomics signature was built using 2 valuable selected features that were significantly associated with relapse within 1 year.Two selected features included 1 gray-level co-occurrence matrices(GLCM)feature(L_G_1.0_GLCM_homogeneity1,LASSO coefficient 3.122)and 1 gray-level run length matrix(GLRLM)feature(GLRLM_RP,LASSO coefficient-2.474).The prediction nomogram including radiomics signature and MRI characteristics(joint invasion and perivascular involvement)showed good discrimination with the area under the ROC curve of 0.884 and 0.821 in the training and validation cohorts,respectively.Th
作者
陈海妹
刘金
程梓轩
全显跃
王晓红
邓宇
陆明
周全
阳维
向之明
李绍林
刘再毅
赵英华
Chen Haimei;Liu Jin;Cheng Zixuan;Quan Xianyue;Wang Xiaohong;Deng Yu;Lu Ming;Zhou Quan;Yang Wei;Xiang Zhiming;Li Shaolin;Liu Zaiyi;Zhao Yinghua(Department of Radiology,the Third Affiliated Hospital of Southern Medical University,Academy of Orthopedics Guangdong Province,Guangzhou 510630,China;Department of Radiology,Guangdong Provincial People′s Hospital,Guangdong Academy of Medical Sciences,Guangzhou 510080,China;Department of Radiology,Zhujiang Hospital of Southern Medical University,Guangzhou 510282,China;Department of Radiology,the Third Affiliated Hospital of Sun Yat-Sen University,Guangzhou 510630,China;Department of Radiology,the First Affiliated Hospital of Guangzhou Medical University,Guangzhou 510120,China;Department of Bone Oncology,the Third Affiliated Hospital of Southern Medical University,Academy of Orthopedics Guangdong Province,Guangzhou 510630,China;Guangdong Provincial Key Laboratory of Medical Image Processing,School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Department of Radiology,Panyu Central Hospital of Guangzhou,Guangzhou 511400,China;Department of Radiology,the Fifth Affiliated Hospital of Sun Yat-Sen University,Zhuhai 519000,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2020年第9期874-881,共8页
Chinese Journal of Radiology
基金
国家自然科学基金(81871510,81771912,81771916,61471187,81671853)
国家重点研究发展计划项目(2017YFC1309100)
广州市科学技术计划项目(201903010032)。
关键词
骨肉瘤
复发
磁共振成像
影像组学
列线图
Osteosarcoma
Recurrence
Magnetic resonance imaging
Radiomics
Nomogram
