CT-based radiomics nomogram may predict local recurrence-free survival in esophageal cancer patients receiving definitive chemoradiation or radiotherapy: A multicenter study. (September 2022)

• Record Type: Journal Article
• Authors: Gong, Jie; Zhang, Wencheng; Huang, Wei; Liao, Ye; Yin, Yutian; Shi, Mei; Qin, Wei; Zhao, Lina
• Is Part Of: Radiotherapy and oncology. Volume 174(2022)
• Page Start: 8
• Page End: 15
• Publication Date: 2022-09
• Subjects: AJCC American Joint Committee on Cancer -- ANN artificial neural network -- AUC areas under the receiver operating characteristic curve -- cCR clinical complete response -- CECT contrast-enhanced computed tomography -- CR complete response -- dCRT definitive chemoradiation -- DL deep learning -- ESCC esophageal squamous cell cancer -- EC esophageal cancer -- FC fully connected -- IBSI image biomarker standardization initiative -- IC induction chemotherapy -- ICCs intra-classcorrelation coefficients -- LASSO least absolute shrinkage and selection operator -- LPFS local progression-free survival -- LRFS local recurrence-free survival -- nCRT neoadjuvant chemoradiotherapy -- OS overall survival -- OSCC oesophageal squamous cell carcinoma -- pCR pathological complete response -- ROI region of interest -- SVM support vector machine; Esophageal squamous cell cancer -- Radiomics -- Deep learning -- Computed tomography -- Local recurrence-free survival; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.06.010
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: CECT-based signature could stratify ESCC patients into high and low-risk group with different prognosis. Compared with clinical factors and radiomic signature, deep-learning signature might provide better prognostic performance. The hybrid radiomics nomogramoutperformed the model that used a subset of the predictors. The hybrid radiomics nomogram may serve as a powerful tool in the evaluation of clinical outcomes in patients with ESCC. Abstract: 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.