术前对比增强CT预测口咽鳞状细胞癌结外扩张。

IF 5.6 Q1 ONCOLOGY
Ryan T Hughes, Christopher M Lack, Jeffrey R Sachs, Kevin D Hiatt, Sydney Smith, Cole R Steber, Fatima Z Aly, Ralph B D'Agostino, Paul M Bunch
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引用次数: 0

摘要

目的建立一种实用、易于实施的基于术前对比增强CT (CECT)淋巴结特征的风险分层模型,以预测口咽鳞状细胞癌(OPSCC)患者病理性结外延伸(pENE)的概率。材料和方法在2012年10月至2020年10月期间接受手术切除的连续OPSCC患者的术前CECT研究由四名神经放射科医生进行,对病理结果不知情,以了解pENE的影像学特征。查询病理报告是否存在pENE。采用成本-复杂性剪枝的决策树分析来确定临床实用的pENE预测模型。结果共162例患者,中位年龄60岁[IQR, 54-67岁];134例男性,28例女性),208例剖腹。大多数患者的原发性OPSCC部位为扁桃体(67%,162例中109例)或舌根(31%,162例中50例)。大多数患者为早期疾病(American Joint Committee on Cancer分期手册第八版分类T0-T2, 93% [151 / 162];N0-N1, 90%[145 / 162])。病理证实的pENE患者占28%(162例中45例)。在单变量分析中,与pENE显著相关的CECT特征包括大小、坏死、多刺、结周搁浅和邻近结构浸润。决策树分析确定了一种预测模型,包括毛刺或不规则边缘、簇状节点和邻近结构的浸润。该模型预测pENE的敏感性为41%(46 / 19),特异性为96%(162 / 157)。结论利用术前CECT特征预测OPSCC患者pENE的模型是实用的,且对OPSCC患者具有较高的特异性。需要进一步的前瞻性研究来确定对临床管理和结果的影响。关键词:头颈部,CT,放射治疗/肿瘤,原发肿瘤,肿瘤,决策分析,观察者表现本文有补充材料。©rsna, 2025。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Extranodal Extension with Preoperative Contrast-enhanced CT in Patients with Oropharyngeal Squamous Cell Carcinoma.

Purpose To develop a practical, easily implementable risk stratification model based on preoperative contrast-enhanced CT (CECT) nodal features to predict the probability of pathologic extranodal extension (pENE) in patients with oropharyngeal squamous cell carcinoma (OPSCC). Materials and Methods Preoperative CECT studies in consecutive patients with OPSCC who underwent surgical resection between October 2012 and October 2020 were examined by four neuroradiologists, blinded to the pathologic outcome, for imaging features of pENE. The pathology report was queried for the presence of pENE. Decision tree analysis with cost-complexity pruning was performed to identify a clinically pragmatic model to predict pENE. Results A total of 162 patients (median age, 60 years [IQR, 54-67 years]; 134 male, 28 female) with 208 dissected heminecks were included. The primary OPSCC site for most patients was tonsil (67%, 109 of 162) or base of tongue (31%, 50 of 162). Most patients had early-stage disease (American Joint Committee on Cancer Staging Manual eighth edition category T0-T2, 93% [151 of 162]; N0-N1, 90% [145 of 162]). Pathologically confirmed pENE was reported in 28% (45 of 162) of patients. CECT features that were significantly associated with pENE on univariable analysis included size, necrosis, spiculation, perinodal stranding, and infiltration of adjacent structures. Decision tree analysis identified a predictive model including spiculation or irregular margins, matted nodes, and infiltration of adjacent structures. The model had a sensitivity of 41% (19 of 46) and specificity of 96% (157 of 162) for predicting pENE. Conclusion The developed model for predicting pENE using preoperative CECT features is practical and had high specificity in patients with OPSCC. Further prospective study is warranted to determine impact on clinical management and outcomes. Keywords: Head/Neck, CT, Radiation Therapy/Oncology, Neoplasms-Primary, Oncology, Decision Analysis, Observer Performance Supplemental material is available for this article. © RSNA, 2025.

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