Vahdati Sanaz, Khosravi Bardia, Robinson Kathryn A, Rouzrokh Pouria, Moassefi Mana, Akkus Zeynettin, Erickson Bradley J
Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55905, USA.
Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55905, USA.
Bioengineering (Basel). 2024 Jun 25;11(7):648. doi: 10.3390/bioengineering11070648.
Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75-0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (-value = 0.003). Our proposed work demonstrated the potential possibility of a deep learning model to achieve diagnostic performance for thyroid nodule evaluation.
甲状腺超声（US）是评估甲状腺结节的主要方法。深度学习（DL）在评估甲状腺癌方面发挥着重要作用。我们提出了一种基于深度学习的流程，依靠超声成像的两个视图来检测甲状腺结节并将其分为良性或恶性组。回顾性收集了983例患者甲状腺结节的横向和纵向超声图像。81例作为测试集保留，其余数据用于五折交叉验证（CV）。训练了两个You Look Only Once（YOLO）v5模型来检测结节并对其进行分类。对于每个视图，在交叉验证期间开发了五个模型，通过使用非极大值抑制（NMS）进行集成以提高其集体泛化能力。在两个视图的集成模型输出上训练一个极端梯度提升（XGBoost）模型，以对每个结节产生恶性的最终预测。由一名专家放射科医生使用美国放射学会甲状腺影像报告和数据系统（ACR-TIRADS）对测试集进行评估。每个视图的集成模型横向的平均精度均值（mAP0.5）为0.797，纵向为0.716。整个流程的曲线下面积（AUROC）为0.84（95%置信区间：0.75 - 0.91），敏感性和特异性分别为84%和63%，而对同一组的ACR-TIRADS评估的敏感性为76%，特异性为34%（P值 = 0.003）。我们提出的工作证明了深度学习模型在实现甲状腺结节评估诊断性能方面的潜在可能性。
