CT-based radiomics nomogram may predict local recurrence-free survival in esophageal cancer patients receiving definitive chemoradiation or radiotherapy: A multicenter study

被引:19
|
作者
Gong, Jie [1 ]
Zhang, Wencheng [2 ]
Huang, Wei [3 ]
Liao, Ye [1 ]
Yin, Yutian [1 ]
Shi, Mei [1 ,5 ]
Qin, Wei [4 ,6 ]
Zhao, Lina [1 ,5 ]
机构
[1] Air Force Med Univ, Xijing Hosp, Dept Radiat Oncol, Xian, Peoples R China
[2] Tianjin Med Univ Canc Inst & Hosp, Natl Clin Res Ctr Canc, Tianjins Clin Res Ctr Canc, Dept Radiat Oncol, Tianjin, Peoples R China
[3] Shandong First Med Univ, Shandong Acad Med Sci, Shandong Canc Hosp & Inst, Jinan, Peoples R China
[4] Xidian Univ, Life Sci Res Ctr, Sch Life Sci & Technol, Xian, Peoples R China
[5] Air Force Med Univ, Xijing Hosp, Dept Radiat Oncol, 127 West Changle Rd, Xian, Peoples R China
[6] Xidian Univ, Life Sci Res Ctr, Sch Life Sci & Technol, Xian 710126, Peoples R China
基金
中国国家自然科学基金;
关键词
Esophageal squamous cell cancer; Radiomics; Deep learning; Computed tomography; Local recurrence-free survival; PATHOLOGICAL COMPLETE RESPONSE; SQUAMOUS-CELL CARCINOMA; CHEMORADIOTHERAPY; CHEMOTHERAPY;
D O I
10.1016/j.radonc.2022.06.010
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background and purpose: 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. Materials and methods: 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. Results: 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. Conclusions: 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. (C) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码:8 / 15
页数:8
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