Yu Bing, He Huijuan, Zheng Qiao, Ai Yao, Yu Xianwen, Li Sunjun, Zhang Ji, Jin Juebin, Jin Xiance, Yu Wenliang
Purchasing Center Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, People's Republic of China.
Radiation and Medical Oncology Department, Wenzhou Medical University First Affiliated Hospital, Wenzhou, 325000, People's Republic of China.
Cancer Manag Res. 2025 Jul 8;17:1339-1349. doi: 10.2147/CMAR.S507943. eCollection 2025.
The feasibility and accuracy of ultrasound-based radiomics, deep learning, and combined deep learning radiomics models were investigated in the differentiation of papillary thyroid carcinoma and papillary thyroid microcarcinoma to decrease the risk of overtreatment of papillary thyroid microcarcinoma.
A total of 549 patients with confirmed 180 papillary thyroid carcinoma and 436 papillary thyroid microcarcinoma nodules from Hospital One were enrolled and randomly divided into training and validation cohorts at a ratio of 8:2 with 56 patients left as independent testing set 1. Fifty patients from Hospital Two were enrolled as independent testing set 2. Radiomics signature and five deep learning networks, such as visual geometry group 13 (VGG13), VGG16, VGG19, AlexNet, and EfficientNet, were generated for papillary thyroid carcinoma and papillary thyroid microcarcinoma differentiation. Combined deep learning and radiomics models were constructed to further improve the differentiation ability.
An area under curves of 0.826 and 0.822 was achieved with radiomics model for papillary thyroid carcinoma and papillary thyroid microcarcinoma differentiation in the independent testing set 1 and set 2, respectively. VGG19 achieved the best area under curves of 0.890 and EfficientNet achieved the best accuracy of 0.867. The best accuracy and area under curves of 0.904, 0.900, and 0.931, 0.946 were achieved with the combination of VGG + radiomics (R_V_Combined) and EffiecientNet + radiomics (R_E_Combined) in the independent testing set 1 and set 2, respectively.
Deep learning and radiomics combination models are promising in the noninvasively preoperative differentiation of papillary thyroid microcarcinoma and papillary thyroid carcinoma to decrease the overtreatment of patients with papillary thyroid microcarcinoma and to minimize the complications caused by overtreatment.
研究基于超声的放射组学、深度学习以及联合深度学习放射组学模型在鉴别甲状腺乳头状癌和甲状腺微小乳头状癌中的可行性和准确性，以降低甲状腺微小乳头状癌过度治疗的风险。
共纳入来自第一医院的549例确诊为180例甲状腺乳头状癌和436例甲状腺微小乳头状癌结节的患者，并按8:2的比例随机分为训练组和验证组，56例患者留作独立测试集1。来自第二医院的50例患者作为独立测试集2。生成放射组学特征以及五个深度学习网络，如视觉几何组13（VGG13）、VGG16、VGG19、AlexNet和EfficientNet，用于甲状腺乳头状癌和甲状腺微小乳头状癌的鉴别。构建联合深度学习和放射组学模型以进一步提高鉴别能力。
在独立测试集1和测试集2中，放射组学模型鉴别甲状腺乳头状癌和甲状腺微小乳头状癌的曲线下面积分别为0.826和0.822。VGG19的曲线下面积最佳，为0.890，EfficientNet的准确率最佳，为0.867。在独立测试集1和测试集2中，VGG+放射组学（R_V_Combined）和EfficientNet+放射组学（R_E_Combined）的组合分别取得了最佳准确率0.904、0.900以及曲线下面积0.931、0.946。
深度学习和放射组学联合模型在甲状腺微小乳头状癌和甲状腺乳头状癌的非侵入性术前鉴别中具有前景，可减少甲状腺微小乳头状癌患者的过度治疗，并将过度治疗引起的并发症降至最低。
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