Literature DB >> 31903981

Textural features based enhanced contrast CT images predicts prognosis to concurrent chemoradiotherapy in stage III esophageal squamous cell cancer.

Yuquan Xie1, Qifeng Wang2, Baorong Cao2, Jiahua Lv2, Yi Wang2, Lei Wu2, Mingqiang Dong1, Tao Li2.   

Abstract

INTRODUCTION: To study the relationship between the tumor heterogeneity based on CT and overall survival (OS) in oesophageal squamous cell carcinoma treated with chemotherapy and radiation therapy (CRT).
METHODS: Fifty-seventh clinical patients who underwent definitive CRT were analyzed. The results were analyzed in terms of whole-tumor texture, with quantification of entropy, mean gray-level intensity for fine to coarse textures (filters 1.0-2.5, respectively). The association between texture parameters and survival time was assessed by Kaplan-Meier analysis and a Cox proportional hazards model.
RESULTS: The median, 1 and 3 years OS, were 20.2 months, 75.4%, and 32.1%. In the univariate analysis performed using the log-rank test found global entropies (P= 0.0119), global mean (P= 0.088), global Std (P= 0.0209), and global uniformity (P= 0.0284) were found to be significant OS prognostic factors for filter value 1.0. Cox proportional hazards models that included a combination of pretreatment GlobalStd and post-treatment volume yields the best performance in predicting OS. Kaplan-Meier curves show that the patients in the high-risk group have significantly worse OS (log-rank test, P= 0.0009) and progression-free survival (PFS) (log-rank test, P= 0.0019) than those in the low-risk group.
CONCLUSION: Pretreatment texture parameters are associated with survival time, and the combination of post volume performed better in survival models.

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Year:  2020        PMID: 31903981     DOI: 10.3233/CBM-190586

Source DB:  PubMed          Journal:  Cancer Biomark        ISSN: 1574-0153            Impact factor:   4.388


  2 in total

1.  Prognostic value of fibroblast activation protein expressing tumor volume calculated from [68 Ga]Ga-FAPI PET/CT in patients with esophageal squamous cell carcinoma.

Authors:  Liang Zhao; Yizhen Pang; Shanyu Chen; Jianhao Chen; Yimin Li; Yifeng Yu; Chunbin Huang; Long Sun; Hua Wu; Haojun Chen; Qin Lin
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-10-12       Impact factor: 10.057

Review 2.  Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.

Authors:  Zhenwei Shi; Zhen Zhang; Zaiyi Liu; Lujun Zhao; Zhaoxiang Ye; Andre Dekker; Leonard Wee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-12-23       Impact factor: 10.057

  2 in total

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