Predictive factors in the evaluation of treatment response to neoadjuvant chemoradiotherapy in patients with advanced esophageal squamous cell cancer

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.MethodAll 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.ResultsAmong 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.ConclusionThe 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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608 RADIOMICS MODEL FACILITATES ORGAN-SAVING STRATEGY IN PATIENTS WITH ESOPHAGEAL SQUAMOUS CELL CANCER RECEIVING NEOADJUVANT CHEMORADIOTHERAPY

Diseases of the Esophagus ◽

10.1093/dote/doab052.608 ◽

2021 ◽

Vol 34 (Supplement_1) ◽

Author(s):

Yue Li

Keyword(s):

Squamous Cell ◽

Squamous Cell Cancer ◽

Cell Cancer ◽

Locally Advanced ◽

Neoadjuvant Chemoradiotherapy ◽

Esophageal Squamous Cell Cancer ◽

Minimum Diameter ◽

Clinical Model ◽

Clinical Variables ◽

Treatment Plans

Abstract After neoadjuvant chemoradiotherapy(NCRT) in locally advanced esophageal squamous cell cancer(ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR) of the primary tumor. 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. We aim to establish a CT-based radiomics model to predict tumor response to NCRT. Methods 121 patients with ESCC who underwent NCRT followed by esophagectomy were retrospectively collected. Radiomics features extracted from pre−/post-NCRT CT images were selected by univariate logistic (p < 0.157) and LASSO regression. A radiomics signature(RS) developed with selected features was combined with 4 clinical variables, including percentage of tumor thickness reduction, tumor adventitia type, tumor minimum diameter on post-NCRT esophagogram and age, 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, comparing with the model of clinical variables alone. Results Among the 121 patients, 51 achieved pCR(42%) after NCRT. 16 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,0.843 vs. 0.700). At the 60% probability threshold cutoff(i.e., the patient would opt for observation if his probability of pCR was >60%), net 12% surgeries could be avoided by RS + clinical model without an increase in the number of missed residual diseases, equivalent to implementing organ-saving strategy in 29.4% 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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