Cone-beam computed-tomography-based delta-radiomic analysis for investigating prognostic power for esophageal squamous cell cancer patients undergoing concurrent chemoradiotherapy

被引:4
作者
Nakamoto, Takahiro [1 ,2 ]
Yamashita, Hideomi [2 ]
Jinnouchi, Haruka [2 ]
Nawa, Kanabu [2 ]
Imae, Toshikazu [2 ]
Takenaka, Shigeharu [2 ]
Aoki, Atsushi [2 ]
Ohta, Takeshi [2 ]
Ozaki, Sho [2 ,3 ]
Nozawa, Yuki [2 ]
Nakagawa, Keiichi [2 ]
机构
[1] Hokkaido Univ, Fac Hlth Sci, Dept Biol Sci & Engn, N12-W5,Kita Ku, Sapporo, Hokkaido 0600812, Japan
[2] Univ Tokyo Hosp, Dept Radiol, 7-3-1 Hongo,Bunkyo Ku, Tokyo 1138655, Japan
[3] Hirosaki Univ, Grad Sch Sci & Technol, 3 Bunkyo, Hirosaki, Aomori 0368561, Japan
来源
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS | 2024年 / 117卷
基金
日本学术振兴会;
关键词
Delta-radiomics; Cone-beam computed tomography; Esophageal squamous cell cancer; Concurrent chemoradiotherapy; Prognosis prediction; DEFORMABLE IMAGE REGISTRATION; DEFINITIVE CHEMORADIOTHERAPY; SURVIVAL; PREDICTION; CARCINOMA; MODELS;
D O I
10.1016/j.ejmp.2023.103182
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To investigate the prognostic power of cone-beam computed-tomography (CBCT)-based delta-radiomics in esophageal squamous cell cancer (ESCC) patients treated with concurrent chemoradiotherapy (CCRT).Methods: We collected data from 26 ESCC patients treated with CCRT. CBCT images acquired at five time points (1st-5th week) per patient during CCRT were used in this study. Radiomic features were extracted from the five CBCT images on the gross tumor volumes. Then, 17 delta-radiomic feature sets derived from five types of calculations were obtained for all the cases. Leave-one-out cross-validation was applied to investigate the prognostic power of CBCT-based delta-radiomic features. Feature selection and construction of a prediction model using Coxnet were performed using training samples. Then, the test sample was classified into high or low risk in each cross-validation fold. Survival analysis for the two groups were performed to evaluate the prognostic power of the extracted CBCT-based delta-radiomic features.Results: Four delta-radiomic feature sets indicated significant differences between the high- and low-risk groups (p < 0.05). The highest C-index in the 17 delta-radiomic feature sets was 0.821 (95 % confidence interval, 0.735-0.907). That feature set had p-value of the log-rank test and hazard ratio of 0.003 and 4.940 (95 % confidence interval, 1.391-17.544), respectively.Conclusions: We investigated the potential of using CBCT-based delta-radiomics for prognosis of ESCC patients treated with CCRT. It was demonstrated that delta-radiomic feature sets based on the absolute value of relative difference obtained from the early to the middle treatment stages have high prognostic power for ESCC.
引用
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页数:9
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