Analysis of features of papillary thyroid carcinoma on color Doppler ultrasound images: implications for lymph node metastasis - Lu Cao; Ying Cao; Xiangru Wang; Xinxin Lu; Fangxi Zhao; Lei Sun; Hua Wang; Xiaopeng Li (BMC Medical Imaging)
Abstract Background This study aimed to describe the color Doppler flow features of papillary thyroid carcinoma (PTC) and to further investigate the associations between these features and lymph node metastasis (LNM). Methods A retrospective analysis of the clinical data of 287 PTC patients confirmed by postoperative pathology at the Second Affiliated Hospital of Xi’an Jiaotong University from January 2022 to April 2023 was conducted. The Adler grading system and novel blood flow patterns were used to analyze the vascularity of the PTC lesions on color Doppler images. Univariate and multivariate logistic regression analyses were conducted to evaluate the independent effects of blood flow characteristics on LNM, and a logistic regression model was established to assess their predictive value for PTC-related LNM. Results In all, 287 PTC lesions were analyzed using color Doppler ultrasonography, which identified five main reference patterns: avascular (26.13%), dot-line (24.74%), branching (14.29%), garland (11.50%), and rich-disorganized (23.34%). The Adler blood flow grading was as follows: 0 (32.75%), I (18.82%), II (19.16%), and III (29.27%). A univariate analysis revealed that the Adler grade was not significantly associated with LNM (P > 0.05), whereas the garland pattern was significantly associated with LNM (P < 0.05). A multivariate analysis revealed that the garland pattern was an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156–0.893]). The incorporation of the garland pattern into the model improved the predictive accuracy for LNM in PTC patients, and the AUC increased from 0.727 [95% CI: 0.669–0.786] to 0.767 [95% CI: 0.731–0.821]. Conclusions This study classifies PTC into five types on the basis of color Doppler flow features and highlights the garland pattern as a potential predictor of LNM risk.