Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is the maximal standardized uptake value (SUV_max) in ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pre-treatment clinical parameters and ¹⁸F-FDG PET/CT derived textural features. Methods: From a prospectively maintained single institution database, we reviewed 97 consecutive patients with locally advanced EC and a pre-treatment ¹⁸F-FDG PET/CT between 2009 and 2015. All patients were treated with nCRT (Carboplatin/Paclitaxel/41.4Gy) followed by esophagectomy. We analyzed clinical, geometrical, and pre-treatment textural features extracted from both ¹⁸F-FDG PET and CT. Current most accurate prediction model with SUV_max as predictor variable was compared with five different response prediction models constructed using Least Absolute Shrinkage and Selection Operator regularized logistic regression. Internal validation was performed to estimate the model’s performances. Pathologic response was defined as: complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least Absolute Shrinkage and Selection Operator regularization selected the clinical parameters: histologic type and clinical T-stage, the ¹⁸F-FDG PET derived textural feature ‘long run low gray level emphasis’, and the CT derived textural feature ‘run percentage’. Introducing these variables to a logistic regression analysis showed areas under the receiver operating characteristic curve (AUCs) of 0.78 compared to 0.58 in the SUV_max model. The discrimination slopes were 0.17 compared to 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV_max). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery.

Nearly one third of patients undergoing neoadjuvant chemoradiotherapy (nCRT) for locally advanced esophageal cancer have a pathologic complete response (pCR) of the primary tumor upon histopathological evaluation of the resection specimen. The primary aim of this study is to develop a model that predicts the probability of pCR to nCRT in esophageal cancer, based on diffusion-weighted magnetic resonance imaging (DW-MRI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography with computed tomography (18F-FDG PET-CT). Accurate response prediction could lead to a patient-tailored approach with omission of surgery in the future in case of predicted pCR or additional neoadjuvant treatment in case of non-pCR. The PRIDE study is a prospective, single arm, observational multicenter study designed to develop a multimodal prediction model for histopathological response to nCRT for esophageal cancer. A total of 200 patients with locally advanced esophageal cancer - of which at least 130 patients with adenocarcinoma and at least 61 patients with squamous cell carcinoma - scheduled to receive nCRT followed by esophagectomy will be included. The primary modalities to be incorporated in the prediction model are quantitative parameters derived from MRI and 18F-FDG PET-CT scans, which will be acquired at fixed intervals before, during and after nCRT. Secondary modalities include blood samples for analysis of the presence of circulating tumor DNA (ctDNA) at 3 time-points (before, during and after nCRT), and an endoscopy with (random) bite-on-bite biopsies of the primary tumor site and other suspected lesions in the esophagus as well as an endoscopic ultrasonography (EUS) with fine needle aspiration of suspected lymph nodes after finishing nCRT. The main study endpoint is the performance of the model for pCR prediction. Secondary endpoints include progression-free and overall survival. If the multimodal PRIDE concept provides high predictive performance for pCR, the results of this study will play an important role in accurate identification of esophageal cancer patients with a pCR to nCRT. These patients might benefit from a patient-tailored approach with omission of surgery in the future. Vice versa, patients with non-pCR might benefit from additional neoadjuvant treatment, or ineffective therapy could be stopped. The article reports on a health care intervention on human participants and was prospectively registered on March 22, 2018 under ClinicalTrials.gov Identifier: NCT03474341 .