正电子发射断层显像/磁共振成像用于预测头颈癌的结外侵犯

PET/MR for predicting extranodal extension of head and neck cancer.

作者信息

Sanchez Vanessa, Pizzuto Daniele A, Maurer Alexander, Muehlematter Urs J, Sah Bert-Ram, Husmann Lars, Skawran Stephan, Mader Caecilia E, Morand Gregoire B, Mueller Simon A, Meerwein Christian, Rupp Niels J, Freiberger Sandra, Lanzer Martin, Messerli Michael, Huellner Martin W

机构信息

Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Nuclear Medicine Unit, GSTeP Radiopharmacy, Fondazone Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.

出版信息

Neuroradiology. 2025 May 21. doi: 10.1007/s00234-025-03635-9.

DOI:10.1007/s00234-025-03635-9

PMID:40397135

Abstract

PURPOSE

To analyze the diagnostic accuracy of multiparametric FDG-PET/MR in identifying pathologic extranodal extension (pENE) of lymph node metastases (LNM) in head and neck squamous cell carcinoma (HNSCC) patients.

METHODS AND MATERIALS

Retrospective analysis of 57 HNSCC patients who underwent preoperative FDG-PET/MR imaging. PET parameters of LNM SUVmax and MTV, lymph node size as well as MR parameters flare sign, shaggy margin sign and vanishing border sign were analyzed. Histopathological assessment of neck dissection specimens served as standard of reference.

RESULTS

A logistic regression model consisting of lymph node size (p = 0.029), shaggy margin sign (p = 0.031) and MTV (p = 0.035) proved that all three parameters significantly contributed to the prediction of pENE (χ²(3) = 54.23, p < 0.001). A second model without the reader-dependent parameter shaggy margin sign yielded similar results (χ²(2) = 45.36, p < 0.001), with every increase in lymph node size (p = 0.006) by 1 mm increasing the likelihood of pENE by a factor of 1.41 (95%-CI[1.11, 1.81]), and every increase in MTV (p = 0.023) by 1 cm3 increasing the likelihood of pENE by a factor of 1.64 (95%-CI[1.07, 2.50]). This model yielded an accuracy of 94.7% (95%-CI [85.4, 98.9]) for predicting pENE, with a specificity of 97.3% (95%-CI [85.8, 99.9]) and a sensitivity of 90.0% (95%-CI [68.3, 98.8]). Internal validation using a test dataset confirmed high accuracy of this model.

CONCLUSION

PET/MR-based multivariate binomial logistic regression models consisting of MTV, lymph node size and/or shaggy lymph node margins predict pENE with high accuracy.

摘要

目的

分析多参数FDG-PET/MR在识别头颈部鳞状细胞癌（HNSCC）患者淋巴结转移（LNM）的病理结外扩展（pENE）中的诊断准确性。

方法和材料

对57例接受术前FDG-PET/MR成像的HNSCC患者进行回顾性分析。分析LNM的PET参数SUVmax和MTV、淋巴结大小以及MR参数耀斑征、边缘粗糙征和边界消失征。颈部清扫标本的组织病理学评估作为参考标准。

结果

由淋巴结大小（p = 0.029）、边缘粗糙征（p = 0.031）和MTV（p = 0.035）组成的逻辑回归模型证明，这三个参数均对pENE的预测有显著贡献（χ²(3)=54.23，p < 0.001）。第二个不包含依赖阅片者的参数边缘粗糙征的模型产生了相似的结果（χ²(2)=45.36，p < 0.001），淋巴结大小每增加1毫米（p = 0.006），pENE的可能性增加1.41倍（95%置信区间[1.11, 1.81]），MTV每增加1立方厘米（p = 0.023），pENE的可能性增加1.64倍（95%置信区间[1.07, 2.50]）。该模型预测pENE的准确率为94.7%（95%置信区间[85.4, 98.9]），特异性为97.3%（95%置信区间[85.8, 99.9]），敏感性为90.0%（95%置信区间[68.3, 98.8]）。使用测试数据集进行内部验证证实了该模型的高准确性。