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.

本研究旨在开发一种基于影像组学的预测图，以预测晚期喉癌（LC）患者在接受诱导化疗（IC）后的病理反应（PR）及总生存期（OS）。研究方法为回顾性分析，纳入了114名经过对比计算机断层扫描（CT）的LC患者；患者被随机分配至训练组（n=81）和验证组（n=33）。通过计算潜在的影像组学评分，利用最小绝对收缩和选择算子（LASSO）回归建立预测PR状态的模型。通过多变量逻辑回归分析，选择与PR状态相关的显著变量。采用Kaplan-Meier方法评估PR状态和影像组学评分（rad-score）对OS的风险分层能力。通过多变量Cox回归，结合影像组学特征和临床病理学特征，构建了预后预测图。所有LC患者均根据中位CT影像组学评分、C指数和校准曲线划分为低风险和高风险组。此外，还对预测图进行了决策曲线分析（DCA），以检验模型的性能和临床实用性。结果显示，在训练组和验证组中，PR率分别为45.6%（37/81）和39.3%（13/33）。通过优化选取了8个特征来构建rad-score模型，该模型与PR和OS显著相关。在两个队列中，PR组的平均OS均显著短于非PR组。多变量Cox分析显示，体积[风险比（HR）=1.43]、N期（HR=1.46）和rad-score（HR=2.65）是与OS相关的独立风险因素。以上四个变量被用于开发预测OS的预测图，决策曲线分析（DCA）表明，该预测图的预测性能优于临床模型。结论：对于晚期LC患者，CT影像组学评分是预测IC后PR的独立生物标志物。此外，结合影像组学特征和临床病理学因素的预测图在个性化OS估计方面表现更为优异。