Background/Objectives: Accurate diagnosis is essential to avoid unnecessary procedures for thyroid incidentalomas (TIs). Advances in radiomics and machine learning applied to medical imaging offer promise for assessing thyroid nodules. This study utilized radiomics analysis on F-18 FDG PET/CT to improve preoperative differential diagnosis of TIs. Methods: A total of 152 patient cases were retrospectively analyzed and split into training and validation sets (7:3) using stratification and randomization. Results: The least absolute shrinkage and selection operator (LASSO) algorithm identified nine radiomics features from 960 candidates to construct a radiomics signature predictive of malignancy. Performance of the radiomics score was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). In the training set, the radiomics score achieved an AUC of 0.794 (95% CI: 0.703–0.885,p< 0.001). Validation was performed on internal and external datasets, yielding AUCs of 0.702 (95% CI: 0.547–0.858,p= 0.011) and 0.668 (95% CI: 0.500–0.838,p= 0.043), respectively. Conclusions: These results demonstrate that the selected nine radiomics features effectively differentiate malignant thyroid nodules. Overall, the radiomics model shows potential as a valuable predictive tool for thyroid cancer in patients with TIs, supporting improved preoperative decision-making.
背景/目的：准确诊断对于避免对甲状腺偶发瘤（TIs）进行不必要的操作至关重要。应用于医学影像的放射组学和机器学习技术的进步为评估甲状腺结节提供了新希望。本研究利用F-18 FDG PET/CT的放射组学分析，旨在提升甲状腺偶发瘤的术前鉴别诊断能力。方法：回顾性分析了152例患者病例，通过分层随机化方法按7:3比例划分为训练集和验证集。结果：采用最小绝对收缩与选择算子（LASSO）算法从960个候选特征中筛选出9个放射组学特征，构建了可预测恶性肿瘤的放射组学特征模型。通过受试者工作特征（ROC）分析和曲线下面积（AUC）评估放射组学评分的性能。在训练集中，放射组学评分的AUC为0.794（95% CI: 0.703–0.885, p<0.001）。在内部和外部验证集上的AUC分别为0.702（95% CI: 0.547–0.858, p=0.011）和0.668（95% CI: 0.500–0.838, p=0.043）。结论：研究结果表明，所选9个放射组学特征能有效区分恶性甲状腺结节。总体而言，该放射组学模型展现出作为甲状腺偶发瘤患者癌症预测工具的潜力，有助于优化术前决策。
肿瘤(癌症)患者之家