Can Whole-Thyroid-Based CT Radiomics Model Achieve the Performance of Lesion-Based Model in Predicting the Thyroid Nodules Malignancy? - A Comparative Study.

Wenxia Yuan, Jiayang Wu, Wenfeng Mai, Hengguo Li, Zhenyu Li
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Abstract

Machine learning is now extensively implemented in medical imaging for preoperative risk stratification and post-therapeutic outcome assessment, enhancing clinical decision-making. Numerous studies have focused on predicting whether thyroid nodules are benign or malignant using a nodule-based approach, which is time-consuming, inefficient, and overlooks the impact of the peritumoral region. To evaluate the effectiveness of using the whole-thyroid as the region of interest in differentiating between benign and malignant thyroid nodules, exploring the potential application value of the entire thyroid. This study enrolled 1121 patients with thyroid nodules between February 2017 and May 2023. All participants underwent contrast-enhanced CT scans prior to surgical intervention. Radiomics features were extracted from arterial phase images, and feature dimensionality reduction was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Four machine learning models were trained on the selected features within the training cohort and subsequently evaluated on the independent validation cohort. The diagnostic performance of whole-thyroid versus nodule-based radiomics models was compared through receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) metrics. The nodule-based logistic regression model achieved an AUC of 0.81 in the validation set, with sensitivity, specificity, and accuracy of 78.6%, 69.4%, and 75.6%, respectively. The whole-thyroid-based random forest model attained an AUC of 0.80, with sensitivity, specificity, and accuracy of 90.0%, 51.9.%, and 80.1%, respectively. The AUC advantage ratios on the LR, DT, RF, and SVM models are approximately - 2.47%, 0.00%, - 4.76%, and - 4.94%, respectively. The Delong test showed no significant differences among the four machine learning models regarding the region of interest defined by either the thyroid primary lesion or the whole thyroid. There was no significant difference in distinguishing between benign and malignant thyroid nodules using either a nodule-based or whole-thyroid-based strategy for ROI outlining. We hypothesize that the whole-thyroid approach provides enhanced diagnostic capability for detecting papillary thyroid carcinomas (PTCs) with ill-defined margins.

基于全甲状腺的CT放射组学模型在预测甲状腺结节恶性肿瘤方面能否达到基于病变模型的效果?——比较研究。
机器学习目前已广泛应用于医学成像,用于术前风险分层和治疗后结果评估,增强临床决策。许多研究都集中在使用基于结节的方法来预测甲状腺结节是良性还是恶性,这种方法耗时,效率低,并且忽略了肿瘤周围区域的影响。评价全甲状腺作为感兴趣区域鉴别甲状腺结节良恶性的有效性,探讨全甲状腺的潜在应用价值。该研究在2017年2月至2023年5月期间招募了1121名甲状腺结节患者。所有参与者在手术前都接受了CT增强扫描。从动脉相图像中提取放射组学特征,并使用最小绝对收缩和选择算子(LASSO)算法进行特征降维。四个机器学习模型在训练队列中选定的特征上进行训练,随后在独立验证队列上进行评估。通过受试者工作特征(ROC)曲线分析和曲线下面积(AUC)指标,比较全甲状腺和基于结节的放射组学模型的诊断性能。基于结节的logistic回归模型在验证集中的AUC为0.81,敏感性、特异性和准确性分别为78.6%、69.4%和75.6%。基于全甲状腺的随机森林模型的AUC为0.80,灵敏度、特异性和准确性分别为90.0%和51.9。%, 80.1%。LR、DT、RF和SVM模型的AUC优势比分别约为- 2.47%、0.00%、- 4.76%和- 4.94%。Delong测试显示,在由甲状腺原发性病变或整个甲状腺定义的感兴趣区域方面,四种机器学习模型之间没有显着差异。使用基于结节或基于全甲状腺的ROI概述策略来区分良性和恶性甲状腺结节没有显著差异。我们假设全甲状腺入路对边缘不明确的甲状腺乳头状癌(ptc)提供了增强的诊断能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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