CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy

The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were incl...

Full description

Saved in:
Bibliographic Details
Published in:Journal of radiation research 2019-07, Vol.60 (4), p.538-545
Main Authors: Yang, Zhining, He, Binghui, Zhuang, Xinyu, Gao, Xiaoying, Wang, Dandan, Li, Mei, Lin, Zhixiong, Luo, Ren
Format: Article
Language:English
Subjects:
Adult
Aged
Chemoradiotherapy, Adjuvant
Esophageal Neoplasms - diagnostic imaging
Esophageal Neoplasms - drug therapy
Esophageal Neoplasms - radiotherapy
Esophageal Squamous Cell Carcinoma - diagnostic imaging
Esophageal Squamous Cell Carcinoma - drug therapy
Esophageal Squamous Cell Carcinoma - radiotherapy
Female
Humans
Logistic Models
Male
Middle Aged
Regression Analysis
Regular Paper
Retrospective Studies
ROC Curve
Tomography, X-Ray Computed
Treatment Outcome
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84-0.86) and the testing cohorts (AUC, 0.71-0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance.
ISSN:0449-3060
1349-9157
DOI:10.1093/jrr/rrz027