Quantitative imaging in medicine and surgery · 2025
Enhancing prediction of cervical lymph node metastasis in papillary thyroid carcinoma through nodule-oriented quantification: combined S-Detect and ultrasound elastography.
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Xu Ze-Lin, Hou Ji-Xue, Zheng Zhen-Hao, Deng Ya-Qian, Zeng Guan-Ming, Wang Si-Rui, Zhu Pei-Shan, Kang Yan-Fei, Du Ting-Ting, Dong Jian, Liu Wen, Li Jun, Cui Xin-Wu
Аннотация
BACKGROUND: Papillary thyroid carcinoma (PTC) frequently metastasizes to cervical lymph nodes (LNs), with metastasis rates of 20-90%, significantly impacting patient prognosis. Although ultrasound (US) is the primary preoperative assessment tool, its accuracy (Acc) in detecting LN metastasis (LNM) remains insufficient, with conventional US detecting only 50% of confirmed cases. This study aimed to improve the prediction of cervical LNM in PTC by combining quantitative nodule orientation parameters with multi-modal US techniques.
METHODS: Data were retrospectively collected from 117 patients (141 nodules: 85 non-metastasis and 56 metastasis) who underwent PTC resection and cervical LN dissection from September 2023 to May 2024. All patients underwent US, US elastography (UE), and S-Detect examinations before surgery. For each nodule, the angle between the nodule's maximum diameter and the skin was measured. Logistic regression analysis assessed the correlation between each variable and cervical LNM, identified significant predictive factors, and a predictive model presented as a nomogram was constructed.
RESULTS: Univariate analysis showed significant differences between non-metastasis and metastasis groups in orientation quantification [-9.3° (-35.2°, 17.2°) vs. 13.9° (-1.6°, 54.0°), P<0.001], age (P=0.002), maximum nodule diameter (P=0.017), boundary (P=0.021), microcalcifications on S-Detect (P=0.014), microcalcifications (P=0.036), and ECI scores (P=0.043). Multivariate analysis identified seven independent predictors for cervical LNM, with S-Detect-detected microcalcifications showing the highest odds ratio (OR) [OR =4.159; 95% confidence interval (CI): 1.545-11.199]. The combined predictive model incorporating conventional US, UE, S-Detect, and orientation quantification demonstrated superior diagnostic performance [area under the curve (AUC) =0.861; 95% CI: 0.803-0.919] compared to individual models (P<0.001), achieving sensitivity (Sen) of 0.911 and specificity (Spe) of 0.659. The nomogram showed good calibration with no significant deviation (χ2=3.271; P=0.926).
CONCLUSIONS: S-Detect accurately identifies the direction of the maximum diameter of thyroid nodules, and quantification of the longitudinal section orientation can be used as an independent predictor for LNM in PTC.
METHODS: Data were retrospectively collected from 117 patients (141 nodules: 85 non-metastasis and 56 metastasis) who underwent PTC resection and cervical LN dissection from September 2023 to May 2024. All patients underwent US, US elastography (UE), and S-Detect examinations before surgery. For each nodule, the angle between the nodule's maximum diameter and the skin was measured. Logistic regression analysis assessed the correlation between each variable and cervical LNM, identified significant predictive factors, and a predictive model presented as a nomogram was constructed.
RESULTS: Univariate analysis showed significant differences between non-metastasis and metastasis groups in orientation quantification [-9.3° (-35.2°, 17.2°) vs. 13.9° (-1.6°, 54.0°), P<0.001], age (P=0.002), maximum nodule diameter (P=0.017), boundary (P=0.021), microcalcifications on S-Detect (P=0.014), microcalcifications (P=0.036), and ECI scores (P=0.043). Multivariate analysis identified seven independent predictors for cervical LNM, with S-Detect-detected microcalcifications showing the highest odds ratio (OR) [OR =4.159; 95% confidence interval (CI): 1.545-11.199]. The combined predictive model incorporating conventional US, UE, S-Detect, and orientation quantification demonstrated superior diagnostic performance [area under the curve (AUC) =0.861; 95% CI: 0.803-0.919] compared to individual models (P<0.001), achieving sensitivity (Sen) of 0.911 and specificity (Spe) of 0.659. The nomogram showed good calibration with no significant deviation (χ2=3.271; P=0.926).
CONCLUSIONS: S-Detect accurately identifies the direction of the maximum diameter of thyroid nodules, and quantification of the longitudinal section orientation can be used as an independent predictor for LNM in PTC.