摘要
目的探讨基于多参数MR的影像组学融合模型术前预测宫颈鳞癌脉管间隙浸润(LVSI)的应用价值。方法回顾性研究。纳入2016年6月—2019年3月山西省肿瘤医院宫颈鳞癌患者168例。患者年龄22~76(52.0±10.1)岁,临床分期为国际妇产联盟(FIGO)ⅠB期127例、ⅡA期41例。所有患者术前行多参数盆腔MR扫描,均接受根治性子宫切除术联合盆腔淋巴结清扫术治疗。收集其临床病理资料和多参数MRI数据,以7∶3的比例按照随机抽样法分为训练集117例和验证集51例。在T2加权像(T2WI)、表观弥散系数[ADC,由2个b值的弥散加权成像数据自动生成]及增强T1加权像(cT1WI)3个序列的MRI上,对病灶进行手动分割勾画肿瘤轮廓感兴趣区(ROI),得到三维感兴趣区(VOI)并提取特征,通过以最大相关最小冗余和最小绝对收缩与选择算子回归为主的三步降维法筛选特征并构建影像组学模型。多因素logistic回归分析筛选临床特征并联合影像组学模型建立融合模型,制作列线图。受试者操作特征曲线(ROC曲线)、校正曲线、决策分析曲线评估列线图的效能及临床效益。结果术后病理检查确诊LVSI阳性42例,阴性126例。训练集与验证集患者的年龄、FIGO分期、肿瘤最大径、肿瘤分化程度、LVSI状态等临床病理特征比较,差异均无统计学意义(P值均>0.05)。基于T2WI、ADC及cT1WI多参数MRI提取的影像组学特征,经特征筛选后得到7个关键特征,均与宫颈癌LVSI相关(P值均<0.05),并构建影像组学模型。训练集T2WI、ADC及cT1WI 3个序列独立构建的影像组学模型预测宫颈癌LVSI的ROC曲线下面积(AUC)分别为0.630[95%可信区间(CI)0.557~0.698]、0.686(95%CI 0.563~0.694)、0.761(95%CI 0.702~0.818),3个序列共同构建的联合影像组学模型对应的AUC为0.887(95%CI 0.842~0.925),诊断效能最优,并在验证集中得到验证。联合影像组学模型与肿瘤分化程度构建的融合模型列线图预测宫颈�
Objective This study aims to investigate the application value of a combined model based on the multiparameter magnetic resonance imaging(MRI)for preoperative prediction of lymph-vascular space invasion(LVSI)in cervical cancer.Methods A total of 168 patients with cervical squamous carcinoma who were pathologically diagnosed at the Shanxi Cancer Hospital from June 2016 to March 2019 were retrospectively enrolled in this study,with an average age of 22-76(52.0±10.1)years old,including 127 FIGOⅠB cases and 41 FIGOⅡA cases.All patients underwent a multiparametric pelvic MRI scan before the surgery and a radical hysterectomy combined with pelvic lymph node dissection was performed.Patients were divided into two groups,the training group(n=117)and the validation group(n=51)according to the random ratio of 7∶3.Volume regions of interest(VOIs)were manually delineated slice by slice on the T2 weighted images(T2WI),apparent diffusion coefficient(ADC),and enhanced T1 weighted images(cT1WI)images of each patient.Radiomics features were extracted from each patient.A three-step dimensionality reduction method based on the maximum relevance minimum redundancy(MRMR)and the least absolute shrinkage and selection operator(LASSO)regression methods were used for feature selection and radiomics signature building.The combined radiomics model,including the clinical risk factors and the abovementioned radiomics signature,was constructed via the multivariate logistic regression method,and the corresponding nomogram was constructed.The prediction performance was determined by the calibration,discrimination,and clinical usefulness.Results Postoperative pathological examination confirmed LVSI positive in 42 patients and negative in 126 patients.No significant differences were found for the general clinical information between the training and validation groups(all P values>0.05).Seven key radiomics features were obtained after feature selection based on the T2WI,ADC,and cT1WI,all of which were significantly associated with lymph-vas
作者
贾亚菊
杨晓棠
崔艳芬
全帅
侯丽娜
Jia Yaju;Yang Xiaotang;Cui Yanfen;Quan Shuai;Hou Lina(Department of Radiology,Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital,Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University Shanxi Province Tumor Hospital,Taiyuan 030000,China;Department of Medical Imaging,Shanxi Medical University,Taiyuan 030000,China;GE Healthcare China,Shanghai 200000,China)
出处
《中华解剖与临床杂志》
2022年第11期737-744,共8页
Chinese Journal of Anatomy and Clinics
基金
国家自然科学基金(82001789、82171923)。
关键词
宫颈肿瘤
脉管浸润
磁共振成像
影像组学
Uterine cervical neoplasms
Vascular invasion
Magnetic resonance imaging
Radiomics
