上海交通大学学报(医学版) ›› 2025, Vol. 45 ›› Issue (7): 900-909.doi: 10.3969/j.issn.1674-8115.2025.07.012

• 论著 · 临床研究 • 上一篇    下一篇

合成磁共振成像在口腔癌颈部淋巴结转移诊断中的应用价值

王蕊, 袁瑛, 陶晓峰()   

  1. 上海交通大学医学院附属第九人民医院放射科,上海 200011
  • 收稿日期:2024-12-30 接受日期:2025-04-08 出版日期:2025-07-28 发布日期:2025-07-28
  • 通讯作者: 陶晓峰,主任医师,博士;电子信箱:cjr.taoxiaofeng@vip.163.com

Application value of synthetic magnetic resonance imaging in predicting cervical lymph node metastasis of oral cancer

WANG Rui, YUAN Ying, TAO Xiaofeng()   

  1. Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
  • Received:2024-12-30 Accepted:2025-04-08 Online:2025-07-28 Published:2025-07-28
  • Contact: TAO Xiaofeng, E-mail: cjr.taoxiaofeng@vip.163.com.

摘要:

目的·探究合成磁共振成像(synthetic magnetic resonance imaging,SyMRI)技术在口腔癌患者颈部淋巴结转移诊断中的价值。方法·对上海交通大学医学院附属第九人民医院于2023年11月—2024年4月期间收治的、经病理确诊为口腔癌且明确淋巴结转移状况的患者,进行回顾性分析。收集这些患者的术前颌面部磁共振图像,从SyMRI生成的定量图[包括合成T1 map、T2 map及质子密度(proton density,PD)map]、表观弥散系数(apparent diffusion coefficient,ADC)图以及对比增强图像的感兴趣体积区域(volume of interest,VOI)中,提取并筛选直方图特征。通过比较不同瘤周区域的直方图参数,确定最佳范围。在此基础上,进一步结合定量图与ADC图开展生境分析,提取肿瘤侵袭性亚区的生境特征,从而构建预测模型。运用受试者工作特征曲线(receiver operator characteristic curve,ROC曲线)、净重新分类指数(net reclassification improvement,NRI)、综合判别改善指数(integrated discrimination improvement,IDI)以及决策曲线分析(decision curve analysis,DCA),对模型性能进行综合评估。结果·研究共纳入61例口腔癌患者。基于SyMRI提取的瘤内直方图特征对于颈部淋巴结转移的预测曲线下面积(area under the curve,AUC)值为0.798(95%CI 0.673~0.924)。结合ADC图可提高AUC值到0.818(95%CI 0.635~0.861)。通过结合瘤周12 mm的直方图特征和生境特征,预测淋巴结转移的AUC值可进一步提升至0.907(95%CI 0.812~0.993)。NRI、IDI和DCA的分析结果均显示,该模型的预测性能优于临床诊断。结论·基于SyMRI,结合瘤内与瘤周的直方图特征以及生境特征,在口腔癌淋巴结转移预测中展现出较高性能,为无造影剂条件下短时间内成像预测转移淋巴结提供了可行途径。

关键词: 口腔癌, 合成磁共振成像, 淋巴结转移, 直方图特征, 瘤周

Abstract:

Objective ·To explore the value of synthetic magnetic resonance imaging (SyMRI) technology in the diagnosis of cervical lymph node metastasis in patients with oral cancer. Methods ·A retrospective analysis was conducted on patients admitted to Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine, from November 2023 to April 2024, who were pathologically diagnosed with oral cancer and had a clear lymph node metastasis status. Pre-operative maxillofacial magnetic resonance images of these patients were collected. Histogram features were extracted and screened from the quantitative maps generated by SyMRI, including synthetic T1, T2, and proton density (PD) maps, apparent diffusion coefficient (ADC) maps, and volumes of interest (VOIs) from contrast-enhanced images. The optimal range was determined by comparing the histogram parameters of different peritumoral regions. On this basis, habitat analysis was further carried out by combining the quantitative maps and ADC maps, and the habitat features of the tumor invasive sub-regions were extracted to construct a prediction model. The performance of the model was comprehensively evaluated using receiver operator characteristic curves (ROC curves), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results ·A total of 61 patients with oral cancer were included in the study. The area under the curve (AUC) value for predicting cervical lymph node metastasis based on the intratumoral histogram features extracted from SyMRI was 0.798 (95%CI 0.673‒0.924). Adding the ADC map could increase the AUC value to 0.818 (95%CI 0.635‒0.861). By combining the histogram features from the 12-mm peritumoral region with habitat features, the AUC value for predicting lymph node metastasis could be further increased to 0.907 (95%CI 0.812‒0.993). The analysis results of NRI, IDI, and DCA all showed that the predictive performance of the model was better than that of clinical diagnosis. Conclusion ·Based on SyMRI, combining the histogram features from the intratumoral and peritumoral regions with habitat features shows high performance in the prediction of lymph node metastasis in oral cancer, providing a feasible, contrast agent-free approach for rapid imaging and prediction of metastatic lymph nodes.

Key words: oral cancer, synthetic magnetic resonance imaging, lymph node metastasis, histogram features, peritumor

中图分类号: