Agyekum Enock Adjei, Wang Yu-Guo, Issaka Eliasu, Ren Yong-Zhen, Tan Gongxun, Shen Xiangjun, Qian Xiao-Qin
Department of Ultrasound, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, China.
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China.
BMC Med Inform Decis Mak. 2025 Apr 11;25(1):161. doi: 10.1186/s12911-025-02989-7.
Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultrasound-guided thermal ablation can shrink nodules and alleviate discomfort. Because the degree and rate of lesion absorption vary greatly between individuals, there is no reliable model for predicting the therapeutic efficacy of thermal ablation.
Five convolutional neural network models including VGG19, Resnet 50, EfficientNetB1, EfficientNetB0, and InceptionV3, pre-trained with ImageNet, were compared for predicting the efficacy of ultrasound-guided microwave ablation (MWA) for benign thyroid nodules using ultrasound data. The patients were randomly assigned to one of two data sets: training (70%) or validation (30%). Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) were all used to assess predictive performance.
In the validation set, fine-tuned EfficientNetB1 performed best, with an AUC of 0.85 and an ACC of 0.79.
The study found that our deep learning model accurately predicts nodules with VRR < 50% after a single MWA session. Indeed, when thermal therapies compete with surgery, anticipating which nodules will be poor responders provides useful information that may assist physicians and patients determine whether thermal ablation or surgery is the preferable option. This was a preliminary study of deep learning, with a gap in actual clinical applications. As a result, more in-depth study should be undertaken to develop deep-learning models that can better help clinics. Prospective studies are expected to generate high-quality evidence and improve clinical performance in subsequent research.
甲状腺结节在临床中很常见，成人甲状腺结节的诊断数量不断增加，部分患者出现症状。超声引导下热消融可缩小结节并缓解不适。由于个体间病变吸收的程度和速度差异很大，目前尚无可靠模型预测热消融的治疗效果。
比较5种预先在ImageNet上训练的卷积神经网络模型，即VGG19、Resnet 50、EfficientNetB1、EfficientNetB0和InceptionV3，利用超声数据预测超声引导下微波消融（MWA）治疗良性甲状腺结节的疗效。患者被随机分配到两个数据集之一：训练集（70%）或验证集（30%）。采用准确率、灵敏度、特异度、阳性预测值、阴性预测值和曲线下面积（AUC）评估预测性能。
在验证集中，微调后的EfficientNetB1表现最佳，AUC为0.85，ACC为0.79。
该研究发现，我们的深度学习模型能够准确预测单次MWA治疗后体积缩小率（VRR）<50%的结节。实际上，当热消融疗法与手术竞争时，预测哪些结节治疗效果不佳可提供有用信息，有助于医生和患者确定热消融或手术哪种是更优选择。这是深度学习的初步研究，在实际临床应用方面存在差距。因此，应开展更深入的研究以开发能更好帮助临床的深度学习模型。预计前瞻性研究将产生高质量证据并在后续研究中改善临床效果。
