scholarly journals The Correlation Between Computed Tomography Volumetry and Prognosis of Advanced Gastric Cancer Treated with Neoadjuvant Chemotherapy

2020 ◽  
Vol Volume 12 ◽  
pp. 759-768
Author(s):  
Chao Chen ◽  
Hao Dong ◽  
Chunhui Shou ◽  
Xiaoxiao Shi ◽  
Qing Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computed Tomography ◽  
Neoadjuvant Chemotherapy ◽  
Advanced Gastric Cancer ◽  
Computed Tomography Volumetry

scholarly journals Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy

2021 ◽  
Author(s):  
Qinmei Xu ◽  
Zeyu Sun ◽  
Xiuli Li ◽  
Chen Ye ◽  
Changsheng Zhou ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computed Tomography ◽  
Neoadjuvant Chemotherapy ◽  
Early Detection ◽  
Advanced Gastric Cancer ◽  
Early Stage ◽  
Preoperative Evaluation ◽  
Predictive Ability ◽  
Cross Combination ◽  
Accurate Identification

Abstract Objectives To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC). Methods We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3–4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3–4 cycles chemotherapy) for evaluating models’ predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection. Results PR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000). Conclusions CT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients. Key Points • Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC.

Sign in / Sign up

Export Citation Format

Share Document