CT imaging-based nomogram for predicting early-stage glottic cancer recurrence following transoral laser microsurgery.

Huanlei Zhang, Yuanyuan Li, Xuelin Zhu, Xiuli Zhao, Lin Cong
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Abstract

Objective: To explore the differences between clinical features and computed tomography (CT) findings of early-stage glottic cancer (EGC) with or without recurrence after transoral laser microsurgery (TLM) and to establish a preoperative nomogram to predict postoperative recurrence.

Methods: The clinical and CT features of 168 consecutive patients with EGC with or without recurrence were analyzed retrospectively. Multivariate logistic regression analysis was used to determine the independent predictors of recurrence. A nomogram was constructed to preoperatively predict recurrence. To assess the nomogram's performance, the C-index and calibration plot were used.

Results: EGCs with and without recurrence differed significantly in T-stage, depth, and normalized CT values in the arterial phase (NCTAP) and venous phase (NCTVP) (all P < 0.05). T-stage, depth, and NCTVP were independent predictors of recurrence in EGCs (all P < 0.05). The C-index (0.765, 95% confidence interval: 0.703-0.827) and calibration plot showed that the nomogram has good prediction accuracy. Nomograms based on T-stage and CT variables provided numerically predicted recurrence rates and were better than those based on only T-stage (C-index of 0.765 vs. 0.608).

Conclusions: Using clinical and CT variables, we developed a novel nomogram to predict the recurrence of EGC before TLM, which may be a potential noninvasive tool for guiding personalized treatment.

用于预测经口激光显微手术后早期声门癌复发的基于 CT 成像的提名图。
目的探讨早期声门癌(EGC)经口激光显微手术(TLM)后复发与否的临床特征和计算机断层扫描(CT)结果之间的差异,并建立预测术后复发的术前提名图:方法:回顾性分析了168例EGC复发或未复发患者的临床和CT特征。采用多变量逻辑回归分析确定复发的独立预测因素。建立了一个术前预测复发的提名图。为了评估提名图的性能,使用了C指数和校准图:结果:复发和未复发的 EGC 在 T 分期、深度、动脉期归一化 CT 值(NCTAP)和静脉期归一化 CT 值(NCTVP)方面存在显著差异(均 P < 0.05)。T期、深度和NCTVP是预测EGC复发的独立指标(所有P均<0.05)。C指数(0.765,95%置信区间:0.703-0.827)和校准图显示,提名图具有良好的预测准确性。基于T分期和CT变量的提名图提供了数字预测复发率,优于仅基于T分期的提名图(C指数为0.765对0.608):利用临床和 CT 变量,我们开发出了一种新的提名图来预测 TLM 前 EGC 的复发情况,它可能是指导个性化治疗的一种潜在的无创工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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