Objectives
The aim of this study was to evaluate and identify the predictive value of combining
CT radiomics features and clinical features to determine recurrence-free survival
(RFS) and overall survival (OS) after surgery in patients with esophageal squamous
cell carcinoma (ESCC).
Materials and Methods
A total of 372 patients with surgically and pathologically confirmed ESCC from 2 institutions
were retrospectively included. All patients from institution 1 were randomized at
a 7:3 ratio into a training cohort (n=206) and an internal validation cohort (n=88),
and patients from institution 2 were used as an external validation cohort (n=78).
The association between the radiomics features and RFS and OS was assessed in the
training cohort and verified in the validation cohort. Furthermore, the performance
of the radiomics nomogram was evaluated by combining the radiomics score (rad-score)
and clinical risk factors.
Results
The radiomics nomogram that combined radiomics features and clinical risk factors
was better than the clinical nomogram and radiomics model alone at predicting RFS
and OS in ESCC patients. All calibration curves showed significant consistency between
predicted survival and actual survival.
Conclusion
Radiomics features could be used to stratify patients with ESCC following radical
resection into high- and low-risk groups. Furthermore, the radiomics nomograms provided
better predictive accuracy than other predictive models and might serve as a therapeutic
decision-making reference for clinicians and be used to monitor the risks of recurrence
and death.
Key Words
Abbreviations:
AIC (Akaike information criterion), CI (Confidence interval), C-index (Harrell's concordance index), CT (Computed tomography), OS (Overall survival), ESCC (Esophageal squamous cell carcinoma), HR (Hazard ratio), ICC (Inter- and intraclass correlation coefficient), LASSO (Least absolute shrinkage and selection operator), LNR (Lymph node ratio), LoG (Laplace of Gaussian), LVI (Lymphovascular invasion), mRMR (The minimum redundancy maximal relevance), Rad-score (Radiomics score), RFS (Recurrence-free survival), ROI (Region of interest)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
REFERENCES
- Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.CA Cancer J Clin. 2021; 71: 209-249https://doi.org/10.3322/caac.21660
- Esophageal and esophagogastric junction cancers, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology.J Natl Compr Canc Netw. 2019; 17: 855-883https://doi.org/10.6004/jnccn.2019.0033
- Cancer of the esophagus and esophagogastric junction-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual.CA Cancer J Clin. 2017; 67: 304-317https://doi.org/10.3322/caac.21399
- Application of the eighth AJCC TNM staging system in patients with esophageal squamous cell carcinoma.Ann Thorac Surg. 2018; 105: 1516-1522https://doi.org/10.1016/j.athoracsur.2017.12.032
- Development and validation of a radiomics nomogram model for predicting postoperative recurrence in patients with esophageal squamous cell cancer who achieved pCR after neoadjuvant chemoradiotherapy followed by surgery.Front Oncol. 2020; 10: 1398https://doi.org/10.3389/fonc.2020.01398
- Clinical characteristics and survival of 5283 esophageal cancer patients: a multicenter study from eighteen hospitals across six regions in China.Cancer Commun (Lond). 2020; 40: 531-544https://doi.org/10.1002/cac2.12087
- Nomograms for predicting risk of locoregional recurrence and distant metastases for esophageal cancer patients after radical esophagectomy.BMC Cancer. 2018; 18: 879https://doi.org/10.1186/s12885-018-4796-5
- Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy.Cancer Imaging. 2021; 21: 38https://doi.org/10.1186/s40644-021-00407-5
- Low preoperative albumin-globulin score predicts favorable survival in esophageal squamous cell carcinoma.Oncotarget. 2016; 7: 30550-30560https://doi.org/10.18632/oncotarget.8868
- Prostate Cancer, Version 2.2019, NCCN clinical practice guidelines in oncology.J Natl Compr Canc Netw. 2019; 17: 479-505https://doi.org/10.6004/jnccn.2019.0023
- Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients.Eur Radiol. 2019; 29: 2196-2206https://doi.org/10.1007/s00330-018-5770-y
- Contrast-enhanced CT-based radiomics analysis in predicting lymphovascular invasion in esophageal squamous cell carcinoma.Front Oncol. 2021; 11644165https://doi.org/10.3389/fonc.2021.644165
- Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma.Eur Radiol. 2019; 29: 392-400https://doi.org/10.1007/s00330-018-5581-1
- MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study.Eur Radiol. 2021; 31: 7575-7583https://doi.org/10.1007/s00330-021-07856-3
- A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules.Clin Radiol. 2019; 74: 933-943https://doi.org/10.1016/j.crad.2019.07.026
- Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy.Radiology. 2014; 270: 141-148https://doi.org/10.1148/radiol.13122869
- Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics.Eur Radiol. 2020; 30: 4952-4963https://doi.org/10.1007/s00330-020-06872-z
- Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features.Med Phys. 2018; 45: 5116-5128https://doi.org/10.1002/mp.13202
- A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning.Br J Radiol. 2021; 9420210525https://doi.org/10.1259/bjr.20210525
- A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer.Radiat Oncol. 2020; 15: 249https://doi.org/10.1186/s13014-020-01692-3
- 2D and 3D CT radiomic features performance comparison in characterization of gastric cancer: a multi-center study.IEEE J Biomed Health Inform. 2021; 25: 755-763https://doi.org/10.1109/jbhi.2020.3002805
- 2D and 3D texture analysis to predict lymphovascular invasion in lung adenocarcinoma.Eur J Radiol. 2020; 129109111https://doi.org/10.1016/j.ejrad.2020.109111
- The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.Radiology. 2020; 10191145https://doi.org/10.1148/radiol.2020191145
Article info
Publication history
Published online: March 14, 2022
Accepted:
January 25,
2022
Received in revised form:
January 25,
2022
Received:
November 23,
2021
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
