Wang Xian, Agyekum Enock Adjei, Ren Yongzhen, Zhang Jin, Zhang Qing, Sun Hui, Zhang Guoliang, Xu Feiju, Bo Xiangshu, Lv Wenzhi, Hu Shudong, Qian Xiaoqin
Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China.
School of Medicine, Jiangsu University, Zhenjiang, China.
Front Oncol. 2021 Mar 4;11:625646. doi: 10.3389/fonc.2021.625646. eCollection 2021.
To construct a sequence diagram based on radiological and clinical factors for the evaluation of extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC).
Between January 2016 and January 2020, 161 patients with PTC who underwent preoperative ultrasound examination in the Affiliated People's Hospital of Jiangsu University were enrolled in this retrospective study. According to the pathology results, the enrolled patients were divided into a non-ETE group and an ETE group. All patients were randomly divided into a training cohort (n = 97) and a validation cohort (n = 64). A total of 479 image features of lesion areas in ultrasonic images were extracted. The radiomic signature was developed using least absolute shrinkage and selection operator algorithms after feature selection using the minimum redundancy maximum relevance method. The radiomic nomogram model was established by multivariable logistic regression analysis based on the radiomic signature and clinical risk factors. The discrimination, calibration, and clinical usefulness of the nomogram model were evaluated in the training and validation cohorts.
The radiomic signature consisted of six radiomic features determined in ultrasound images. The radiomic nomogram included the parameters tumor location, radiological ETE diagnosis, and the radiomic signature. Area under the curve (AUC) values confirmed good discrimination of this nomogram in the training cohort [AUC, 0.837; 95% confidence interval (CI), 0.756-0.919] and the validation cohort (AUC, 0.824; 95% CI, 0.723-0.925). The decision curve analysis showed that the radiomic nomogram has good clinical application value.
The newly developed radiomic nomogram model is a noninvasive and reliable tool with high accuracy to predict ETE in patients with PTC.
构建基于放射学和临床因素的序列图，用于评估甲状腺乳头状癌（PTC）患者的甲状腺外侵犯（ETE）情况。
2016年1月至2020年1月期间，在江苏大学附属人民医院接受术前超声检查的161例PTC患者纳入本回顾性研究。根据病理结果，将纳入患者分为非ETE组和ETE组。所有患者随机分为训练队列（n = 97）和验证队列（n = 64）。提取超声图像中病变区域共479个图像特征。采用最小冗余最大相关法进行特征选择后，使用最小绝对收缩和选择算子算法生成放射组学特征。基于放射组学特征和临床危险因素，通过多变量逻辑回归分析建立放射组学列线图模型。在训练队列和验证队列中评估列线图模型的辨别力、校准度和临床实用性。
放射组学特征由超声图像中确定的6个放射组学特征组成。放射组学列线图包括肿瘤位置、放射学ETE诊断和放射组学特征等参数。曲线下面积（AUC）值证实该列线图在训练队列（AUC，0.837；95%置信区间[CI]，0.756 - 0.919）和验证队列（AUC，0.824；95%CI，0.723 - 0.925）中具有良好的辨别力。决策曲线分析表明，放射组学列线图具有良好的临床应用价值。
新开发的放射组学列线图模型是一种无创且可靠的工具，对预测PTC患者的ETE具有较高准确性。
