天津医药 ›› 2025, Vol. 53 ›› Issue (11): 1158-1164.doi: 10.11958/20252455

• 临床研究 • 上一篇    下一篇

超声AI联合BRAF V600E基因检测对甲状腺结节良恶性与侵袭性的诊断效能

吴梦琳(), 马芳, 杨亚菲   

  1. 合肥市第二人民医院超声科(邮编 230000)
  • 收稿日期:2025-07-10 修回日期:2025-08-13 出版日期:2025-11-15 发布日期:2025-11-19
  • 作者简介:吴梦琳(1995),女,医师,主要从事浅表及血管方向超声方面研究。E-mail:wumenglinwmllll@sina.com
  • 基金资助:
    合肥市应用医学研究项目(Hwk2021yb008)

Diagnostic efficacy of ultrasonic artificial intelligence combined with BRAF V600E gene testing in differentiating benign-malignant and invasive thyroid nodules

WU Menglin(), MA Fang, YANG Yafei   

  1. Department of Ultrasound, Hefei Second People's Hospital, Hefei 230000, China
  • Received:2025-07-10 Revised:2025-08-13 Published:2025-11-15 Online:2025-11-19

摘要:

目的 探讨超声人工智能(AI)与苏氨酸特异性激酶(BRAF)V600E基因的联合检测在甲状腺结节良恶性与侵袭性评估中的应用价值。方法 选取甲状腺恶性结节(恶性组)、甲状腺良性结节(良性组)患者各150例,根据恶性组的病理诊断是否涉及包膜、血管、神经侵犯或淋巴结转移分为侵袭组(66例)和未侵袭组(84例)。收集各组一般临床特征、超声AI指标及BRAF V600E基因检测结果。统计超声AI、BRAF V600E基因检测与甲状腺结节术后病理诊断的差异。受试者工作特征(ROC)曲线及Delong检验评估超声AI、BRAF V600E基因单项及联合检测的诊断效能。结果 恶性组结节最大直径>1 cm、实性结构、极低回声/低回声、微钙化、边界模糊、形态不规则、纵横比>1、内部/混合血流分布、Ⅲ—Ⅴ级血流丰富度的比例均大于良性组。侵袭组结节最大直径>1 cm、实性结构、极低回声/低回声、微钙化、边界模糊、形态不规则、内部/混合血流分布、Ⅲ—Ⅴ级血流丰富度的比例均大于未侵袭组(P<0.05)。超声AI诊断甲状腺恶性结节的敏感度为90.00%,特异度为80.67%;超声AI诊断甲状腺恶性结节合并侵袭性的敏感度为84.85%,特异度为83.33%。BRAF V600E基因诊断甲状腺恶性结节的敏感度为72.67%,特异度为90.00%;BRAF V600E基因诊断甲状腺恶性结节合并侵袭性的敏感度为74.24%,特异度为88.10%。受试者工作特征(ROC)曲线显示,超声AI、BRAF V600E基因单项及联合检测诊断甲状腺恶性结节的AUC(95%CI)分别为0.853(0.807~0.900)、0.813(0.762~0.864)、0.941(0.917~0.966),且联合检测的AUC大于超声AI、BRAF V600E基因单项检测(P<0.05);超声AI、BRAF V600E基因单项及联合检测诊断甲状腺恶性结节合并侵袭性的AUC(95%CI)分别为0.841(0.773~0.909)、0.812(0.737~0.886)、0.924(0.880~0.967),且联合检测的AUC大于超声AI、BRAF V600E基因单项检测(P<0.05)。结论 超声AI与BRAF V600E基因的联合检测显著优化了甲状腺结节良恶性与侵袭性的诊断效能。

关键词: 甲状腺结节, 人工智能, 超声检查, BRAF V600E, 良恶性, 侵袭性

Abstract:

Objective To investigate the application value of ultrasonic artificial intelligence (AI) combined with serine/threonine-protein kinase (BRAF) V600E gene testing in differentiating benign-malignant and invasive thyroid nodules. Methods A total of 150 patients with malignant thyroid nodules (the malignant group) and 150 patients with benign thyroid nodules (the benign group) were selected. According to whether the pathological diagnosis of the malignant group involved capsule, vascular, nerve invasion or lymph node metastasis, patients were divided into the invasive group (66 cases) and the non-invasive group (84 cases). General clinical characteristics, ultrasonic AI parameters and BRAF V600E gene testing results were compared between groups. Discrepancies between ultrasonic AI, BRAF V600E gene testing and postoperative pathological diagnoses were analyzed. ROC curves and Delong tests were usd to evaluate the diagnostic efficacy of ultrasonic AI, BRAF V600E gene and their joint inspection. Results The malignant group exhibited higher probabilities of nodule maximum diameter (>1 cm), solid structure, hypoechoic/very hypoechoic echogenicity, microcalcification, blurred margin, irregular shape, aspect ratio (>1), internal and mixed blood flow distribution and high blood flow richness (grades Ⅲ—Ⅴ) compared to those of the benign group (P<0.05). The invasive subgroup showed higher probabilities of nodule maximum diameter (>1 cm), solid structure, hypoechoic/very hypoechoic echogenicity, microcalcification, blurred margin, irregular shape, internal and mixed blood flow distribution, and high blood flow richness (grades Ⅲ—Ⅴ) than those of the non-invasive subgroup (P<0.05). For diagnosing malignant thyroid nodules, ultrasonic AI demonstrated a sensitivity of 90.00% and specificity of 80.67%. For invasive malignant nodules, sensitivity was 84.85% and specificity was 83.33%. BRAF V600E gene testing showed a sensitivity of 72.67%, specificity of 90.00% for malignant nodules. For invasive nodules, sensitivity was 74.24% and specificity was 88.10%. Receiver operating characteristic curve (ROC) analysis revealed that the AUCs (95% CI) for ultrasonic AI, BRAF V600E gene and their joint inspection in diagnosing malignant thyroid nodules were 0.853 (0.807-0.900), 0.813 (0.762-0.864) and 0.941 (0.917-0.966), with the joint inspection outperforming individual tests (P<0.05). For invasive malignant nodules, the AUCs were 0.841 (0.773-0.909), 0.812 (0.737-0.886) and 0.924 (0.880-0.967), respectively, with the joint inspection showing superior performance (P<0.05). Conclusion The joint inspection ultrasound AI with BRAF V600E gene significantly improve the diagnostic efficacy for differentiating benign and malignant thyroid nodules, and assessing their invasive potential.

Key words: thyroid nodule, artificial intelligence, ultrasonography, BRAF V600E, benign-malignant, invasiveness

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