基于多参数磁共振成像的影像组学列线图预测早期宫颈癌淋巴结转移
Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer.
作者信息
Xiao Meiling, Ma Fenghua, Li Ying, Li Yongai, Li Mengdie, Zhang Guofu, Qiang Jinwei
机构信息
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Department of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China.
出版信息
J Magn Reson Imaging. 2020 Sep;52(3):885-896. doi: 10.1002/jmri.27101. Epub 2020 Feb 25.
BACKGROUND
Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early-stage cervical cancer.
PURPOSE
To establish a multiparametric MRI (mpMRI)-based radiomics nomogram for preoperatively predicting LNM status.
STUDY TYPE
Retrospective.
POPULATION
Among 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort.
FIELD STRENGTH
Radiomic features were extracted from a 1.5T mpMRI scan (T -weighted imaging [T WI], fat-saturated T -weighted imaging [FS-T WI], contrast-enhanced [CE], diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps).
ASSESSMENT
The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated.
STATISTICAL TESTS
The least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively.
RESULTS
The radiomic signature allowed a good discrimination between the LNM and non-LNM groups, with a C-index of 0.856 (95% confidence interval [CI], 0.794-0.918) in the primary cohort and 0.883 (95% CI, 0.809-0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C-index, 0.882 [95% CI, 0.827-0.937], C-index, 0.893 [95% CI, 0.822-0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.
DATA CONCLUSION
A radiomics nomogram was developed by incorporating the radiomics signature with the MRI-reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early-stage cervical cancer.
LEVEL OF EVIDENCE
3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:885-896.
背景
淋巴结转移（LNM）是影响早期宫颈癌患者治疗策略和预后的关键危险因素。
目的
建立基于多参数MRI（mpMRI）的影像组学列线图，用于术前预测LNM状态。
研究类型
回顾性研究。
研究对象
在233例连续患者中，155例患者被随机分配到主要队列，78例患者被分配到验证队列。
场强
从1.5T mpMRI扫描（T加权成像[T WI]、脂肪饱和T加权成像[FS-T WI]、对比增强[CE]、扩散加权成像[DWI]和表观扩散系数[ADC]图）中提取影像组学特征。
评估
评估列线图的校准、鉴别能力和临床实用性。还计算了受试者操作特征曲线下面积（ROC AUC）、准确性、敏感性和特异性。
统计检验
采用最小绝对收缩和选择算子（LASSO）方法进行降维、特征选择和影像组学特征构建。采用多变量逻辑回归分析建立影像组学列线图。分别采用独立样本t检验和卡方检验比较连续变量和分类变量的差异。
结果
影像组学特征在LNM组和非LNM组之间具有良好的鉴别能力，主要队列中的C指数为0.856（95%置信区间[CI]，0.794-0.918），验证队列中的C指数为0.883（95%CI，0.809-0.957）。此外，影像组学列线图在主要队列和验证队列中也具有良好的鉴别性能和校准效果（C指数分别为0.882[95%CI，0.827-0.937]和0.893[95%CI，0.822-0.964]）。决策曲线分析表明，影像组学列线图具有临床实用性。
数据结论
通过将影像组学特征与MRI报告的LN状态和国际妇产科联盟（FIGO）分期相结合，开发了一种影像组学列线图。该列线图可用于促进早期宫颈癌患者LNM的个体化预测。
证据水平
3级 技术效能阶段：2级 《磁共振成像杂志》2020年；52：885-896。
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