Wang Jing, Zhu Yueqiang, Li Qian, Wang Lining, Bian Haiman, Lu Xiaomei, Ye Zhaoxiang
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Eur Radiol. 2024 Dec 27. doi: 10.1007/s00330-024-11294-2.
To establish a spectral CT-based nomogram for predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced esophageal squamous cell carcinoma (ESCC).
This retrospective study included 172 patients with ESCC who underwent spectral CT scans before NAC followed by resection. Based on postoperative tumor regression grades (TRG), 34% (58) of patients were responsive (TRG1) and 66% (114) were non-responsive (TRG2-3). The data was divided into a primary set of 120 and a validation set of 52, maintaining a 7:3 random ratio. Measurements included iodine concentration (IC), normalized iodine concentration (nIC), CT, CT, spectral attenuation curve slope (λHU), and effective atomic number (Zeff) during non-contrast and venous phases (VP). Clinicopathologic characteristics were collected. Univariable and multivariable logistic regressions identified independent predictors of NAC response. The model was visualized using nomograms, and its efficacy was assessed via receiver operating characteristic (ROC) curves.
Multivariable logistic regression analysis identified the neutrophil-to-lymphocyte ratio (NLR), clinical stage, ZeffVP, and nICVP as independent predictors of NAC response. The nomogram incorporating all four independent predictors, outperformed spectral CT and the clinical model with the highest AUCs of 0.825 (95% CI: 0.746-0.895) for the primary set and 0.794 (95% CI: 0.635-0.918) for the validation set (DeLong test: all p < 0.05).
The spectral CT and clinical models were useful in predicting NAC response in ESCC patients. Combining spectral CT imaging parameters and clinicopathologic characteristics in a nomogram improved predictive accuracy.
Question Developing a non-invasive, practical tool to predict ESCC's response to chemotherapy is crucial and has not yet been done. Findings This nomogram, incorporating clinicopathologic characteristics and spectral CT-derived parameters, predicted NAC response in ESCC patients. Clinical relevance This spectral CT-based nomogram is a non-invasive and easily obtainable tool for accurately predicting ESCC response to NAC, aiding clinicians in personalized treatment planning.
建立基于光谱CT的列线图，用于预测局部晚期食管鳞状细胞癌（ESCC）患者对新辅助化疗（NAC）的反应。
这项回顾性研究纳入了172例ESCC患者，这些患者在接受NAC治疗前进行了光谱CT扫描，随后接受了手术切除。根据术后肿瘤退缩分级（TRG），34%（58例）患者有反应（TRG1），66%（114例）患者无反应（TRG2 - 3）。数据被分为120例的主要数据集和52例的验证集，保持7:3的随机比例。测量指标包括非增强期和静脉期（VP）的碘浓度（IC）、归一化碘浓度（nIC）、CT、CT、光谱衰减曲线斜率（λHU）和有效原子序数（Zeff）。收集临床病理特征。单变量和多变量逻辑回归分析确定了NAC反应的独立预测因素。使用列线图对模型进行可视化，并通过受试者操作特征（ROC）曲线评估其效能。
多变量逻辑回归分析确定中性粒细胞与淋巴细胞比值（NLR）、临床分期、ZeffVP和nICVP为NAC反应的独立预测因素。纳入所有四个独立预测因素的列线图，在主要数据集中AUC最高为0.825（95%CI：0.746 - 0.895），在验证集中为0.794（95%CI：0.635 - 0.918），优于光谱CT和临床模型（DeLong检验：所有p < 0.05）。
光谱CT和临床模型有助于预测ESCC患者对NAC的反应。将光谱CT成像参数和临床病理特征结合在列线图中可提高预测准确性。
问题开发一种非侵入性、实用的工具来预测ESCC对化疗的反应至关重要且尚未实现。发现该列线图纳入临床病理特征和光谱CT衍生参数，可预测ESCC患者对NAC的反应。临床意义这种基于光谱CT的列线图是一种非侵入性且易于获得的工具，可准确预测ESCC对NAC的反应，有助于临床医生进行个性化治疗规划。
