Department of Nuclear Medicine, Recep Tayyip Erdogan University, Faculty of Medicine, Training and Research Hospital, Rize, Turkey.
Acta Otorhinolaryngol Ital. 2024 Aug;44(4):261-268. doi: 10.14639/0392-100X-N3029.
If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis.
Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models.
After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%).
ML models may be used to predict ER in patients with DTC without distant metastasis.
分化型甲状腺癌（DTC）患者在接受放射性碘（RAI）治疗后出现极好的反应（ER），则复发率较低。本研究旨在通过机器学习（ML）方法，预测无远处转移的 DTC 患者在 RAI 治疗后 6-24 个月的 ER，其中包括临床病理参数。
确定 151 例无远处转移且接受 RAI 治疗的 DTC 患者的治疗反应（ER/非 ER）。将甲状腺切除术±颈部解剖病理数据、RAI 治疗前后的实验室和影像学发现引入 ML 模型。
RAI 治疗后，118 例患者 ER，33 例患者非 ER。在 RAI 治疗前，ER 患者中有 29%的人 TgAb 阳性，而非 ER 患者中有 55%的人 TgAb 阳性（p = 0.007）。8 种 ML 模型预测 ER 的曲线下面积（AUC）值较高（>0.700）。具有最高 AUC 值的模型是极端梯度增强（AUC = 0.871），梯度增强的准确率最高（81%）。
ML 模型可用于预测无远处转移的 DTC 患者的 ER。
