Zhou Yan, Su Guo-Yi, Hu Hao, Tao Xin-Wei, Ge Ying-Qian, Si Yan, Shen Mei-Ping, Xu Xiao-Quan, Wu Fei-Yun
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing.
Siemens Healthineers, Shanghai.
Acad Radiol. 2022 Mar;29 Suppl 3:S222-S231. doi: 10.1016/j.acra.2021.06.014. Epub 2021 Aug 5.
To develop and validate 2 iodine maps based radiomics nomograms for preoperatively predicting cervical lymph node metastasis (LNM) and central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC).
A total of 346 patients with PTC were enrolled and allocated to training (242) and validation (104) sets. Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Aggregated machine-learning strategy was applied for features selection and construction of 2 radiomics scores (LN rad-score; CLN rad-score). Logistic regression model was employed to establish two radiomics nomograms (nomogram 1: predicting LNM; nomogram 2: predicting CLNM) after incorporating LN or CLN rad-score with clinical predictors. Nomograms performance was determined by discrimination, calibration and clinical usefulness.
Nomogram 1 incorporated LN rad-score, age (categorized by 55) and CT reported LN status; Nomogram 2 incorporated CLN rad-score, capsule contact >25% and CT reported CLN status. 2 nomograms both showed good discrimination and calibration in the training (AUC = 0.847; AUC = 0.837) and validation cohorts (AUC = 0.807; AUC = 0.795). Significant improved AUC, net reclassification index (NRI) and integrated discriminatory improvement (IDI) confirmed additional great predictive value of 2 rad-scores, compared with clinical models without radiomics. Decision curve analysis indicated clinical utility of nomograms. 2 nomograms both demonstrated favorable predictive efficacy in CT reported LN or CLN negative subgroup (AUC = 0.766; AUC = 0.744).
The presented 2 radiomics nomograms are useful tools for preoperative prediction of LNM and CLNM in PTC.
开发并验证基于2种碘图的影像组学列线图，用于术前预测甲状腺乳头状癌（PTC）的颈部淋巴结转移（LNM）和中央区淋巴结转移（CLNM）。
共纳入346例PTC患者，分为训练集（242例）和验证集（104例）。分别从动脉期和静脉期碘图中提取影像组学特征。采用聚合机器学习策略进行特征选择，并构建2种影像组学评分（LN放射学评分；CLN放射学评分）。在将LN或CLN放射学评分与临床预测指标相结合后，采用逻辑回归模型建立2种影像组学列线图（列线图1：预测LNM；列线图2：预测CLNM）。通过区分度、校准度和临床实用性来确定列线图的性能。
列线图1纳入了LN放射学评分、年龄（以55岁为界分类）和CT报告的LN状态；列线图2纳入了CLN放射学评分、包膜接触>25%和CT报告的CLN状态。2种列线图在训练队列（AUC = 0.847；AUC = 0.837）和验证队列（AUC = 0.807；AUC = 0.795）中均显示出良好的区分度和校准度。与无影像组学的临床模型相比，显著提高的AUC、净重新分类指数（NRI）和综合判别改善（IDI）证实了2种放射学评分具有额外的巨大预测价值。决策曲线分析表明列线图具有临床实用性。2种列线图在CT报告的LN或CLN阴性亚组中均显示出良好的预测效能（AUC = 0.766；AUC = 0.744）。
所呈现的2种影像组学列线图是术前预测PTC中LNM和CLNM的有用工具。
