DataSheet_1_CT radiomics nomogram predicts pathological response after induced chemotherapy and overall survival in patients with advanced laryngeal cancer: A single-center retrospective study.docx

PurposeThis study aimed to develop a radiomics nomogram to predict pathological response (PR) after induction chemotherapy (IC) and overall survival (OS) in patients with advanced laryngeal cancer (LC).
MethodsThis retrospective study included patients with LC (n = 114) who had undergone contrast computerized tomography (CT); patients were randomly assigned to training (n = 81) and validation cohorts (n = 33). Potential radiomics scores were calculated to establish a model for predicting the PR status using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression analyses were performed to select significant variables for predicting PR status. Kaplan–Meier analysis was performed to assess the risk stratification ability of PR and radiomics score (rad-score) for predicting OS. A prognostic nomogram was developed by integrating radiomics features and clinicopathological characteristics using multivariate Cox regression. All LC patients were stratified as low- and high-risk by the median CT radiomic score, C-index, calibration curve. Additionally, decision curve analysis (DCA) of the nomogram was performed to test model performance and clinical usefulness.
ResultsOverall, PR rates were 45.6% (37/81) and 39.3% (13/33) in the training and validation cohorts, respectively. Eight features were optimally selected to build a rad-score model, which was significantly associated with PR and OS. The median OS in the PR group was significantly shorter than that in the non-PR group in both cohorts. Multivariate Cox analysis revealed that volume [hazard ratio, (HR) = 1.43], N stage (HR = 1.46), and rad-score (HR = 2.65) were independent risk factors associated with OS. The above four variables were applied to develop a nomogram for predicting OS, and the DCAs indicated that the predictive performance of the nomogram was better than that of the clinical model.
ConclusionFor patients with advanced LC, CT radiomics score was an independent biomarker for estimating PR after IC. Moreover, the nomogram that incorporated radiomics features and clinicopathological factors performed better for individualized OS estimation.

研究目的：本研究旨在构建放射组学列线图，用于预测晚期喉癌（laryngeal cancer, LC）患者诱导化疗（induction chemotherapy, IC）后的病理应答（pathological response, PR）以及总生存期（overall survival, OS）。

研究方法：本回顾性研究纳入了114例接受过增强计算机断层扫描（contrast computerized tomography, CT）的喉癌患者，将其随机分为训练队列（n=81）与验证队列（n=33）。采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归计算潜在放射组学评分，以构建预测病理应答状态的模型。通过多变量logistic回归分析筛选出预测病理应答状态的显著变量。采用Kaplan-Meier分析评估病理应答与放射组学评分（rad-score）对总生存期的风险分层能力。通过整合放射组学特征与临床病理特征，采用多变量Cox回归构建预后列线图。依据CT放射组学评分的中位数、C指数与校准曲线，将所有喉癌患者分为低风险组与高风险组。此外，对该列线图进行决策曲线分析（decision curve analysis, DCA），以检验模型的预测性能与临床实用性。

研究结果：训练队列与验证队列的病理应答率分别为45.6%（37/81）与39.3%（13/33）。最终筛选出8个特征以构建放射组学评分模型，该模型与病理应答及总生存期均显著相关。在两个队列中，病理应答组的中位总生存期均显著短于非病理应答组。多变量Cox分析显示，肿瘤体积[风险比（hazard ratio, HR）=1.43]、N分期（HR=1.46）以及放射组学评分（HR=2.65）是与总生存期相关的独立风险因素。基于上述四个变量构建了用于预测总生存期的列线图，决策曲线分析结果显示，该列线图的预测性能优于临床模型。

研究结论：对于晚期喉癌患者，CT放射组学评分是预测诱导化疗后病理应答的独立生物标志物。此外，整合了放射组学特征与临床病理因素的列线图在个体化总生存期预测方面表现更优。