Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

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 SUVmax in 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 pretreatment 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 pretreatment 18F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18F-FDG PET and CT. The current most accurate prediction model with SUVmax as a predictor variable was compared with 6 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 with 0.58 in the SUVmax model. The discrimination slopes were 0.17 compared with 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 (SUVmax). 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.

Evaluation of progression prior to surgery after neoadjuvant chemoradiotherapy with computed tomography in esophageal cancer patients.

BACKGROUND: The risk of tumor progression during neoadjuvant chemoradiotherapy (CRT) in esophageal cancer (EC) is around 8% to 17%. We assessed the efficacy of computed tomography (CT) to identify these patients before esophagectomy. METHODS: Ninety-seven patients with locally advanced EC treated with Carboplatin/Paclitaxel and 41.4 Gy neoadjuvantly were restaged with CT. Two radiologists reviewed pre- and post-CRT CT images. The primary outcome was detection of clinically relevant progressive disease. Missed metastases were defined as metastatic disease found during surgery or within 3 months after post-CRT CT. RESULTS: Progressive disease was detected in 9 patients (9%). Both radiologists detected 5 patients with distant metastases (liver, n = 4; lung metastasis, n = 1), but missed progressive disease in 4 cases. One radiologist falsely assessed 2 metastatic lesions, but after agreement progressive disease was detected with sensitivity and specificity of 56% and 100%, respectively. CONCLUSION: CT is effective in detecting clinically relevant progressive disease in EC patients, after neoadjuvant treatment.