Prediction of the tumor response and survival based on computed tomography in esophageal squamous cell carcinoma after trimodality therapy
Related Experiment Video
Updated: Nov 10, 2025

Endobronchial Ultrasound-guided Intratumoral Injection of Cisplatin for the Treatment of Isolated Mediastinal Recurrence of Lung Cancer
Published on: February 12, 2017
Ryosuke Hirohata1, Yoichi Hamai2, Manabu Emi1
1Department of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-Ku, Hiroshima, 734-8551, Japan.
Purpose:
Predicting the response to neoadjuvant chemoradiotherapy (NCRT) and the prognosis of esophageal squamous cell carcinoma (ESCC) is challenging. This study evaluated the potential of a preoperative computed tomography (CT) analysis for predicting the pathological response and survival of patients with ESCC who received trimodality therapy.
Methods:
A total of 119 patients with cT3 or T4 ESCC who underwent surgery following NCRT between 2007 and 2019 were assessed. The CT-based parameters were measured with enhanced CT preoperatively, prior to and during treatment. Associations between these parameters and the pathologic response, as well as the prognosis, were examined.
Results:
Pretreatment maximum CT value (p = 0.009), pretreatment mean CT value (p = 0.022), preoperative whole tumor volume (WTV, p = 0.003), preoperative largest cross section (LCS, p = 0.002), ΔLCS (pretreatment to preoperative, p = 0.004), ΔWTV (pretreatment to preoperative, p = 0.012), and Δmean CT value (pretreatment to preoperative, p = 0.012) of the primary lesion were significantly associated with a good pathological response. A Cox-regression analysis of recurrence-free survival (RFS) and overall survival (OS) showed that preoperative LCS was an independent CT-based predictor. RFS and OS were significantly higher at the optimal cut-off value of the preoperative LCS (p = 0.036 and p = 0.016, respectively).
Conclusions:
CT-based parameters are thus considered to be valuable predictors of the tumor response and survival after trimodality therapy for ESCC.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024