A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients
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RESEARCH
A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer He‑San Luo1,2, Shao‑Fu Huang2, Hong‑Yao Xu2, Xu‑Yuan Li3, Sheng‑Xi Wu2 and De‑Hua Wu1*
Abstract Purpose: To develop and validate a nomogram model to predict complete response (CR) after concurrent chemora‑ diotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods: Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomo‑ gram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results: A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95% CI 0.742–0.869, p
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