Table_1_Risk Stratification in Patients With Follicular Neoplasm on Cytology: Use of Quantitative Characteristics and Sonographic Patterns.docx

ObjectivesDifferentiating thyroid nodules with a cytological diagnosis of follicular neoplasm remains an issue. The goal of this study was to determine whether ultrasonographic (US) findings obtained preoperatively from the computer-aided detection (CAD) system are sufficient to further stratify the risk of malignancy for this diagnostic cytological category.
MethodsFrom September 2016 to September 2018 in our hospital, patients diagnosed with Bethesda category IV (follicular neoplasm or suspicion of follicular neoplasm) thyroid nodules and underwent surgical excisions were include in the study. Quantification and analysis of tumor features were performed using CAD software. The US findings of the region of interest, including index of composition, margin, echogenicity, texture, echogenic dots indicative of calcifications, tall and wide orientation, and margin were calculated into computerized values. The nodules were further classified into American Thyroid Association (ATA) and American College of Radiology Thyroid Imaging Reporting & Data System (TI-RADS) categories.
Results92 (10.1%) of 913 patients were diagnosed with Bethesda category IV thyroid nodules. In 65 patients, the histological type of the nodule was identified. The quantitative features between patients with benign and malignant conditions differed significantly. The presence of heterogeneous echotexture, blurred margins, or irregular margins was shown to have the highest diagnostic value. The risks of malignancy for nodules classified as having very low to intermediate suspicion ATA, non-ATA, and high suspicion ATA patterns were 9%, 35.7%, and 51.7%, respectively. Meanwhile, the risks of malignancy were 12.5%, 26.1%, and 53.8% for nodules classified as TIRADS 3, 4, and 5, respectively. When compared to human observers, among whom poor agreement was noticeable, the CAD software has shown a higher average accuracy.
ConclusionsFor patients with nodules diagnosed as Bethesda category IV, the software-based characterizations of US features, along with the associated ATA patterns and TIRADS system, were shown helpful in the risk stratification of malignancy.

研究背景与目的：细胞学诊断为滤泡性肿瘤的甲状腺结节鉴别诊断仍是临床待解难题。本研究旨在明确：术前通过计算机辅助检测（computer-aided detection, CAD）系统获取的超声（ultrasonographic, US）影像学特征，是否可对该细胞学分类病例的恶性风险进行进一步分层。
研究方法：本研究纳入2016年9月至2018年9月于本院就诊、经确诊为贝塞斯达（Bethesda）分类IV级（滤泡性肿瘤或疑似滤泡性肿瘤）甲状腺结节且接受手术切除的患者。采用CAD软件对肿瘤特征进行量化分析与提取。对感兴趣区域的超声影像学特征进行量化赋值，包括成分指数、边界特征、回声强度、内部质地、提示钙化的强回声光点、高宽比及边界特征（原文此处存在重复表述），并将上述特征转化为计算机可识别的量化数值。随后将结节进一步归类至美国甲状腺协会（American Thyroid Association, ATA）及美国放射学会甲状腺影像报告和数据系统（Thyroid Imaging Reporting & Data System, TI-RADS）分类标准中。
研究结果：913例患者中，92例（10.1%）被诊断为贝塞斯达分类IV级甲状腺结节，其中65例明确了结节的组织学类型。良、恶性结节患者的量化特征存在显著差异。其中，不均匀内部质地、边界模糊或边界不规则表现出最高的诊断价值。按ATA极低至中度可疑、非ATA分类及高度可疑模式分类的结节，其恶性风险分别为9.0%、35.7%及51.7%；而按TI-RADS 3类、4类及5类分类的结节，其恶性风险分别为12.5%、26.1%及53.8%。与观察者间一致性欠佳的人工阅片者相比，CAD软件展现出更高的平均诊断准确率。
研究结论：对于细胞学诊断为贝塞斯达分类IV级的甲状腺结节患者，基于CAD软件的超声特征量化分析，结合相关ATA分类标准及TI-RADS系统，可有效辅助其恶性风险分层。