Park Jee-Yeun, Yang Sung-Hee
Department of Radiological Science, Jangpalpal Internal Medicine Clinic, 369, Haeundae-ro, Haeundae-gu, Busan 48062, Republic of Korea.
Department of Radiological Science, College of Health Sciences, Catholic University of Pusan, Busan 46252, Republic of Korea.
Diagnostics (Basel). 2025 Aug 29;15(17):2191. doi: 10.3390/diagnostics15172191.
Elastography is a non-invasive imaging technique that assesses tissue stiffness and elasticity. This study aimed to evaluate the diagnostic performance and clinical utility of elastography and S-detect in distinguishing benign from malignant thyroid nodules. S-detect (RS85) is a deep learning-based computer-aided diagnosis (DL-CAD) software that analyzes grayscale ultrasound 2D images to evaluate the morphological characteristics of thyroid nodules, providing a visual guide to the likelihood of malignancy. This retrospective study included 159 patients (61 male and 98 female) aged 30-83 years (56.14 ± 11.35) who underwent thyroid ultrasonography between January 2023 and June 2024. All the patients underwent elastography, S-detect analysis, and fine needle aspiration cytology (FNAC). Malignancy status was determined based on the FNAC findings, and the diagnostic performance of the elasticity contrast index (ECI), S-detect, and evaluations by a radiologist were assessed. Based on the FNAC results, 101 patients (63.5%) had benign nodules and 58 patients (36.5%) had malignant nodules. Radiologist interpretation demonstrated the highest diagnostic accuracy (area under the curve 89%), with a sensitivity of 98.28%, specificity of 79.21%, positive predictive value (PPV) of 73.1%, and negative predictive value (NPV) of 98.8%. The elasticity contrast index showed an accuracy of 85%, sensitivity of 87.93%, specificity of 81.19%, PPV of 72.9%, and NPV of 92.1%. S-detect yielded the lowest accuracy at 78%, with a sensitivity of 87.93%, specificity of 68.32%, PPV of 61.4%, and NPV of 90.8%. These findings offer valuable insights into the comparative diagnostic utility of elastography and AI-based S-detect for thyroid nodules in clinical practice. Although limited by its single-center design and sample size, which potentially limits the generalization of the results, the controlled environment ensured consistency and minimized confounding variables.
弹性成像术是一种评估组织硬度和弹性的非侵入性成像技术。本研究旨在评估弹性成像术和S-detect在鉴别甲状腺良恶性结节方面的诊断性能和临床应用价值。S-detect（RS85）是一款基于深度学习的计算机辅助诊断（DL-CAD）软件，可分析灰阶超声二维图像以评估甲状腺结节的形态特征，为恶性可能性提供可视化指导。这项回顾性研究纳入了159例年龄在30至83岁（56.14±11.35）之间的患者（61例男性和98例女性），他们在2023年1月至2024年6月期间接受了甲状腺超声检查。所有患者均接受了弹性成像术、S-detect分析和细针穿刺细胞学检查（FNAC）。根据FNAC结果确定恶性状态，并评估弹性对比指数（ECI）、S-detect的诊断性能以及放射科医生的评估。根据FNAC结果，101例患者（63.5%）有良性结节，58例患者（36.5%）有恶性结节。放射科医生的解读显示出最高的诊断准确性（曲线下面积89%），敏感性为98.28%，特异性为79.21%，阳性预测值（PPV）为73.1%，阴性预测值（NPV）为98.8%。弹性对比指数的准确性为85%，敏感性为87.93%，特异性为81.19%，PPV为72.9%，NPV为92.1%。S-detect的准确性最低，为78%，敏感性为87.93%，特异性为68.32%，PPV为61.4%，NPV为90.8%。这些发现为弹性成像术和基于人工智能的S-detect在临床实践中对甲状腺结节的比较诊断效用提供了有价值的见解。尽管受到单中心设计和样本量的限制，这可能会限制结果的推广，但可控环境确保了一致性并最大限度地减少了混杂变量。
