Yue Li1,2, Jun Liu1, Hong-Xuan Li1, Xu-Wei Cai1, Zhi-Gang Li3, Xiao-Dan Ye4, Hao-Hua Teng5, Xiao-Long Fu1, Wen Yu1. 1. Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 2. Shanghai Jiao Tong University School of Medicine, Shanghai, China. 3. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 4. Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 5. Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Abstract
After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans. METHOD: All eligible patients treated in our center from June 2012 to June 2019 were retrospectively collected. Radiomics features extracted from pre-/post-NCRT CT images were selected by univariate logistic and LASSO regression. A radiomics signature (RS) developed with selected features was combined with clinical variables to construct RS+clinical model with multivariate logistic regression, which was internally validated by bootstrapping. Performance and clinical usefulness of RS+clinical model were assessed by receiver operating characteristic (ROC) curves and decision curve analysis, respectively. RESULTS: Among the 121 eligible patients, 51 achieved pCR (42.1%) after NCRT. Eighteen radiomics features were selected and incorporated into RS. The RS+clinical model has improved prediction performance for pCR compared with the clinical model (corrected area under the ROC curve, 0.84 vs. 0.70). At the 60% probability threshold cutoff (i.e., the patient would opt for observation if his probability of pCR was >60%), net 13% surgeries could be avoided by RS+clinical model, equivalent to implementing organ-saving strategy in 31.37% of the 51 true-pCR cases. CONCLUSION: The model built with CT radiomics features and clinical variables shows the potential of predicting pCR after NCRT; it provides significant clinical benefit in identifying qualified patients to receive individualized organ-saving treatment plans.
After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans. METHOD: All eligible patients treated in our center from June 2012 to June 2019 were retrospectively collected. Radiomics features extracted from pre-/post-NCRT CT images were selected by univariate logistic and LASSO regression. A radiomics signature (RS) developed with selected features was combined with clinical variables to construct RS+clinical model with multivariate logistic regression, which was internally validated by bootstrapping. Performance and clinical usefulness of RS+clinical model were assessed by receiver operating characteristic (ROC) curves and decision curve analysis, respectively. RESULTS: Among the 121 eligible patients, 51 achieved pCR (42.1%) after NCRT. Eighteen radiomics features were selected and incorporated into RS. The RS+clinical model has improved prediction performance for pCR compared with the clinical model (corrected area under the ROC curve, 0.84 vs. 0.70). At the 60% probability threshold cutoff (i.e., the patient would opt for observation if his probability of pCR was >60%), net 13% surgeries could be avoided by RS+clinical model, equivalent to implementing organ-saving strategy in 31.37% of the 51 true-pCR cases. CONCLUSION: The model built with CT radiomics features and clinical variables shows the potential of predicting pCR after NCRT; it provides significant clinical benefit in identifying qualified patients to receive individualized organ-saving treatment plans.
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