甲状腺结节
医学
放射性武器
甲状腺
不确定
急诊分诊台
回顾性队列研究
危险分层
结核(地质)
分级(工程)
放射科
内科学
超声波
算法
数学
纯数学
急诊医学
古生物学
土木工程
工程类
生物
作者
Matti L. Gild,Mico Chan,Jay Gajera,Brett Lurie,Ziba Gandomkar,Roderick Clifton‐Bligh
摘要
Indeterminate thyroid nodules (Bethesda III) are challenging to characterize without diagnostic surgery. Auxiliary strategies including molecular analysis, machine learning models, and ultrasound grading with Thyroid Imaging, Reporting and Data System (TI-RADS) can help to triage accordingly, but further refinement is needed to prevent unnecessary surgeries and increase positive predictive values.Retrospective review of 88 patients with Bethesda III nodules who had diagnostic surgery with final pathological diagnosis.Each nodule was retrospectively scored through TI-RADS. Two deep learning models were tested, one previously developed and trained on another data set, mainly containing determinate cases and then validated on our data set while the other one trained and tested on our data set (indeterminate cases).The mean TI-RADS score was 3 for benign and 4 for malignant nodules (p = .0022). Radiological high risk (TI-RADS 4,5) and low risk (TI-RADS 2,3) categories were established. The PPV for the high radiological risk category in those with >10 mm nodules was 85% (CI: 70%-93%). The NPV for low radiological risk in patients >60 years (mean age was 100% (CI: 83%-100%). The area under the curve (AUC) value of our novel classifier was 0.75 (CI: 0.62-0.84) and differed significantly from the chance-level (p < .00001).Novel radiomic and radiologic strategies can be employed to assist with preoperative diagnosis of indeterminate thyroid nodules.
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