Li Cheng, Luo Yong, Jiang Yan, Li Qi
Department of Thyroid Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, China.
Department of Ultrasound, Ningbo Medical Center Lihuili Hospital, Ningbo, China.
Sci Rep. 2025 Jan 13;15(1):1860. doi: 10.1038/s41598-024-84716-2.
Management of thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) cytology is challenging because of uncertain malignancy risk. Intraoperative frozen section pathology provides real-time diagnosis for AUS/FLUS nodules undergoing surgery, but its accuracy is limited. This study aimed to develop an integrated predictive model combining clinical, ultrasound and IOFS features to improve intraoperative malignancy risk assessment. A retrospective cohort study was conducted on patients with AUS/FLUS cytology and negative BRAF mutation who underwent thyroid surgery. The cohort was randomly divided into training and validation sets. Clinical, ultrasound, and pathological features were extracted for analysis. Three models were developed: an IOFS model with IOFS results as sole predictor, a clinical model integrating clinical and ultrasound features, and an integrated model combining all features. Model performance was evaluated using comprehensive metrics in both sets. The superior model was visualized as a nomogram. Among 531 included patients, the integrated model demonstrated superior diagnostic ability, predictive performance, calibration, and clinical utility compared to other models. It exhibited AUC values of 0.92 in the training set and 0.95 in the validation set. The nomogram provides a practical tool for estimating malignancy probability intraoperatively. This study developed an innovative integrated predictive model for intraoperative malignancy risk assessment of AUS/FLUS nodules. By combining clinical, ultrasound, and IOFS features, the model enhances IOFS diagnostic sensitivity, providing a reliable decision-support tool for optimizing surgical strategies.
对具有意义不明确的非典型性/意义不明确的滤泡性病变（AUS/FLUS）细胞学特征的甲状腺结节进行管理具有挑战性，因为其恶性风险不确定。术中冰冻切片病理检查可为接受手术的AUS/FLUS结节提供实时诊断，但其准确性有限。本研究旨在开发一种综合预测模型，结合临床、超声和术中冰冻切片（IOFS）特征，以改善术中恶性风险评估。对接受甲状腺手术且AUS/FLUS细胞学检查结果为阴性且BRAF基因突变阴性的患者进行了一项回顾性队列研究。该队列被随机分为训练集和验证集。提取临床、超声和病理特征进行分析。开发了三个模型：一个以IOFS结果作为唯一预测指标的IOFS模型、一个整合临床和超声特征的临床模型以及一个结合所有特征的综合模型。在两个数据集中使用综合指标评估模型性能。将性能最佳的模型制作成列线图。在纳入的531例患者中，与其他模型相比，综合模型表现出更优的诊断能力、预测性能、校准度和临床实用性。其在训练集中的AUC值为0.92，在验证集中为0.95。该列线图为术中估计恶性概率提供了一个实用工具。本研究开发了一种创新的综合预测模型，用于评估AUS/FLUS结节的术中恶性风险。通过结合临床、超声和IOFS特征，该模型提高了IOFS诊断敏感性，为优化手术策略提供了可靠的决策支持工具。
