Lee Dong-Hwa, Choi Jee-Woo, Kim Geun-Hyeong, Park Seung, Jeon Hyun Jeong
Department of Internal Medicine, Chungbuk National University College of Medicine and Chungbuk National University Hospital, Cheongju, South Korea.
Medical AI Research Team, Chungbuk National University Hospital, Cheongju, South Korea.
Int J Gen Med. 2024 Dec 31;17:6585-6594. doi: 10.2147/IJGM.S486189. eCollection 2024.
Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy. Although its mortality rate is low, some patients experience cancer recurrence during follow-up. In this study, we investigated the accuracy of a novel multimodal model by simultaneously analyzing numeric and time-series data to predict recurrence in patients with PTC after thyroidectomy.
We analyzed patients with thyroid carcinoma who underwent thyroidectomy at the Chungbuk National University Hospital between January 2006 and December 2021. The proposed model used numerical data, including clinical information at the time of surgery, and time-series data, including postoperative thyroid function test results. For the model training with unbalanced data, we employed weighted binary cross-entropy with weights of 0.8 for the positive (recurrence) group and 0.2 for the negative (nonrecurrence) group. We performed four-fold cross-validation of the dataset to evaluate the model performance.
Our dataset comprised 1613 patients who underwent thyroidectomy, including 1550 and 63 patients with nonrecurrent and recurrent PTC, respectively. Patients with recurrence had a larger tumor size, more tumor multiplicity, and a higher male-to-female ratio than those without recurrence. The proposed model achieved an average area under the curve of 0.9622, F1-score of 0.4603, sensitivity of 0.9042, and specificity of 0.9077.
When applying our proposed model, the experimental results showed that it could predict recurrence at least 1 year before occurrence. The multimodal model for predicting PTC recurrence after thyroidectomy showed good performance. In clinical practice, it may help with the early detection of recurrence during the follow-up of patients with PTC after thyroidectomy.
甲状腺乳头状癌（PTC）是最常见的甲状腺恶性肿瘤。尽管其死亡率较低，但部分患者在随访期间会出现癌症复发。在本研究中，我们通过同时分析数值和时间序列数据，研究了一种新型多模态模型预测甲状腺切除术后PTC患者复发的准确性。
我们分析了2006年1月至2021年12月在忠北国立大学医院接受甲状腺切除术的甲状腺癌患者。所提出的模型使用数值数据（包括手术时的临床信息）和时间序列数据（包括术后甲状腺功能测试结果）。对于不平衡数据的模型训练，我们采用加权二元交叉熵，阳性（复发）组权重为0.8，阴性（未复发）组权重为0.2。我们对数据集进行了四倍交叉验证以评估模型性能。
我们的数据集包括1613例接受甲状腺切除术的患者，其中分别有1550例和63例未复发和复发的PTC患者。复发患者的肿瘤大小更大、肿瘤多灶性更多且男女比例更高。所提出的模型的曲线下平均面积为0.9622，F1分数为0.4603，灵敏度为0.9042，特异性为0.9077。
应用我们提出的模型时，实验结果表明它可以在复发发生前至少1年进行预测。甲状腺切除术后预测PTC复发的多模态模型表现良好。在临床实践中，它可能有助于在甲状腺切除术后PTC患者的随访期间早期发现复发。
