Computed Tomography-based Radiomic Analysis for Predicting Pathological Response and Prognosis After Neoadjuvant Chemotherapy in Patients with Locally Advanced Esophageal Cancer

Overview

Journal Abdom Radiol (NY)

Publisher Springer

Specialties Gastroenterology
Radiology

Date 2023 May 12

PMID 37171586

Authors

Shioto Oda

Hirofumi Kuno

Takashi Hiyama

Shingo Sakashita

Tomoaki Sasaki

Tatsushi Kobayashi

Affiliations

Soon will be listed here.

Abstract

Purpose: Accurate prediction of prognosis and pathological response to neoadjuvant chemotherapy (NAC) is crucial for optimizing treatment strategies for patients with locally advanced esophageal cancer (LA-EC). This study aimed to investigate the use of radiomics for pretreatment CT in predicting the pathological response of patients with LA-EC to NAC.

Methods: Overall, 144 patients (145 lesions) with LA-EC who underwent pretreatment contrast-enhanced CT and then received NAC followed by surgery with pathological tumor regression grade (TRG) analysis were enrolled. The obtained dataset was randomly divided into training and validation cohorts using fivefold cross-validation. CT-based radiomic features were extracted followed by the feature selection process using the variance threshold, SelectKBest, and least absolute shrinkage and selection operator methods. The radiomic model was constructed using six machine learning classifiers, and predictive performance was evaluated using ROC curve analysis in the training and validation cohorts.

Results: All patients were divided into responders (n = 40, 28%) and non-responders (n = 104, 72%) based on the TRG results and a statistically significant split by overall survival analysis (0.899 [0.754-0.961] vs. 0.630 [0.510-0.729], respectively). There were no significant differences between responders and non-responders in terms of age, sex, tumor size, tumor location, or histopathology. The mean AUC of fivefold in the validation cohort was 0.720 (confidence interval [CI]: 0.594-0.982), and the best AUC of the radiomic model using logistic regression to predict the non-responders was 0.815 (CI: 0.626-1.000, sensitivity 0.620, specificity 0.860).

Conclusion: A radiomic model derived from contrast-enhanced CT may help stratify chemotherapy effect prediction and improve clinical decision-making.

Citing Articles

CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study.

Zheng Y, Mei P, Wang M, Luo Q, Li H, Ding C BMC Med Imaging. 2024; 24(1):329.

PMID: 39627736 PMC: 11616236. DOI: 10.1186/s12880-024-01503-1.

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