Radiomics from Primary Tumor on Dual-Energy CT Derived Iodine Maps can Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer

列线图 医学 无线电技术 淋巴结 放射科 逻辑回归 甲状腺癌 曲线下面积 甲状腺乳突癌 肿瘤科 内科学 癌症
作者
Yan Zhou,Guo‐Yi Su,Hao Hu,Xinwei Tao,Yingqian Ge,Yan Si,Meiping Shen,Xiao‐Quan Xu,Fei-Yun Wu
出处
期刊:Academic Radiology [Elsevier BV]
卷期号:29: S222-S231 被引量:26
标识
DOI:10.1016/j.acra.2021.06.014
摘要

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.
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