列线图
医学
无线电技术
放射治疗
食管癌
肿瘤科
阶段(地层学)
内科学
放射科
癌症
古生物学
生物
作者
Jie Gong,Wencheng Zhang,Wei Huang,Ye Liao,Yuexin Yin,Mei Shi,Wei Qin,Lina Zhao
标识
DOI:10.1016/j.radonc.2022.06.010
摘要
To establish and validate a contrast-enhanced computed tomography-based hybrid radiomics nomogram for prediction of local recurrence-free survival (LRFS) in esophageal squamous cell cancer (ESCC) patients receiving definitive (chemo)radiotherapy in a multicenter setting.This retrospective study included 302 ESCC patients from Xijing Hospital receiving definitive (chemo)radiotherapy, which were randomly assigned to the training (n = 201) and internal validation sets (n = 101). And 74 and 21 ESCC patients from the other two centers were used as the external validation set (n = 95). A hybrid radiomics nomogram was established by integrating clinical factors, radiomic signature and deep-learning signature in training set and was tested in two validation sets.The deep-learning signature showed better prognostic performance than radiomic signature for predicting LRFS in training (C-index: 0.73 vs 0.70), internal (Cindex: 0.72 vs 0.64) and external validation sets (C-index: 0.72 vs 0.63), which could stratify patients into high and low-risk group with different prognosis (cut-off value: -0.06). Low-risk groups had better LRFS than high-risk groups in training (p < 0.0001; 2-y LRFS 71.1% vs 33.0%), internal (p < 0.01; 2-y LRFS 58.8% vs 34.8%) and external validation sets (p < 0.0001; 2-y LRFS 61.9% vs 22.4%), respectively. The hybrid radiomics nomogram established by integrating radiomic signature, deep-learning signature with clinical factors including T stage and concurrent chemotherapy outperformed any one or two combinations in training (C-index: 0.82), internal (Cindex: 0.78), and external validation sets (C-index: 0.76). Calibration curves showed good agreement.The hybrid radiomics based on pretreatment contrast-enhanced computed tomography provided a promising way to predict local recurrence of ESCC patients receiving definitive (chemo)radiotherapy.
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