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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

Qingtao Qiu, J. Duan, Hong-Bin Deng, Zhujun Han, J. Gu, N. Yue, Y. Yin
Published 2020 · Medicine

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Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680–0.812) vs. 0.685 (0.620–0.750) vs. 0.614 (0.538–0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696–0.752) vs. 0.671 (0.624–0.718) vs. 0.629 (0.597–0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
This paper references
Incorporation of pre-therapy (18) F-FDGuptake data with CT texture features into a radiomics model for radiation pneumonitis
GJ Anthony (2017)
the bridge between medical imaging and personalized medicine
P Lambin (2017)
10.1148/radiol.2015142920
Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.
D. Fried (2016)
10.1158/1078-0432.CCR-18-1305
A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors
W. Liang (2018)
10.1038/s41386-019-0551-0
A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer’s disease by using a predictive nomogram
Kexin Huang (2019)
10.1148/radiol.2016151829
Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis.
J. Wu (2016)
10.1371/journal.pone.0102107
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Chintan Parmar (2014)
physician performance versus radiomic assessment
SA Mattonen (2016)
Esophageal cancer. Am Family Physician
M W Short (2017)
10.1016/j.surg.2018.01.011
Preoperative prediction of a pathologic complete response of esophageal squamous cell carcinoma to neoadjuvant chemoradiotherapy
Y. Hamai (2018)
10.1148/RADIOL.2016152234
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Yanqi Huang (2016)
10.1016/j.gtc.2008.09.012
Esophageal cancer.
P. Siersema (2008)
Cancer statistics in China ,
W Chen (2015)
10.1002/bjs.11246
Health‐related quality of life in a randomized trial of neoadjuvant chemotherapy or chemoradiotherapy plus surgery in patients with oesophageal cancer (NeoRes trial)
B. Sunde (2019)
10.21037/QIMS.2019.03.02
Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability.
Qingtao Qiu (2019)
10.1200/JCO.2013.53.6532
Surgery alone versus chemoradiotherapy followed by surgery for stage I and II esophageal cancer: final analysis of randomized controlled phase III trial FFCD 9901.
C. Mariette (2014)
10.3322/caac.21492
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
F. Bray (2018)
10.18637/JSS.V070.B02
Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition)
James E. Helmreich (2016)
10.1038/nrclinonc.2017.141
Radiomics: the bridge between medical imaging and personalized medicine
P. Lambin (2017)
With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
Frank E. Harrell J. Regression Modeling Strategies (2015)
10.3322/caac.21338
Cancer statistics in China, 2015
W. Chen (2016)
10.1002/mp.12282
Incorporation of pre‐therapy 18F‐FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis
Gregory J Anthony (2017)
impact of tumor segmentation variability
Q Qiu (2019)
10.1016/j.ijrobp.2015.12.369
Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment.
Sarah A. Mattonen (2016)
X - tile : a new bioinformatics tool for biomarker assessment and outcome - based cut - point optimization
RL Camp (2004)
10.1016/j.tranon.2018.04.005
Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction
C. Shen (2018)
10.1016/j.ebiom.2018.07.029
Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Shaoxu Wu (2018)
10.1093/jrr/rrz027
CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
Z. Yang (2019)
10.1245/s10434-015-4840-5
Prognosis and Treatment After Diagnosis of Recurrent Esophageal Carcinoma Following Esophagectomy with Curative Intent
K. Parry (2015)
10.1016/j.athoracsur.2008.11.001
Complete pathologic response after neoadjuvant chemoradiotherapy for esophageal cancer is associated with enhanced survival.
J. Donahue (2009)
a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization
RL Camp (2004)
CT-based radiomic signatures for prediction of pathologic complete response in esophageal Frontiers in Oncology | www.frontiersin.org 9 August 2020 | Volume 10 | Article
Z Yang (2019)
10.1158/1078-0432.CCR-17-1510
A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Shaoxu Wu (2017)
10.1056/NEJMoa1112088
Preoperative chemoradiotherapy for esophageal or junctional cancer.
P. V. van Hagen (2012)
10.1016/j.ebiom.2019.05.023
Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy
C. Xie (2019)
long term outcomes of a phase II study
Z Tan (2018)
10.21037/jtd.2018.08.88
Clinical predictors of pathologically response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma: long term outcomes of a phase II study.
Zihui Tan (2018)
10.1200/JCO.2015.65.9128
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Yanqi Huang (2016)
10.1007/s00268-015-3356-2
The Global Burden of Esophageal Cancer: A Disability-Adjusted Life-Year Approach
B. Pardo (2015)
10.1016/j.jtcvs.2018.09.136
Patterns and risk of recurrence in patients with esophageal cancer with a pathologic complete response after chemoradiotherapy followed by surgery
A. Barbetta (2018)
10.1016/j.ijrobp.2014.07.020
Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.
D. Fried (2014)
10.2991/dsahmj.k.200901.001
National Burden of Cancers in Tunisia: A Disability Adjusted Life–year Approach
H. Ayed (2020)
10.1038/s41598-017-00665-z
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer
X. Fave (2017)
10.1158/1078-0432.CCR-04-0713
X-Tile
R. Camp (2004)
10.1148/radiol.2015150358
Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.
Y. Cui (2016)
10.1158/0008-5472.CAN-17-0339
Computational Radiomics System to Decode the Radiographic Phenotype.
J. van Griethuysen (2017)
10.1097/SLA.0000000000002670
Multi-institutional Analysis of Recurrence and Survival After Neoadjuvant Chemoradiotherapy of Esophageal Cancer: Impact of Histology on Recurrence Patterns and Outcomes
M. Xi (2019)
10.1148/radiol.2015151169
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
a potential biomarker for the prediction of disease-free survival in earlystage (I or II) non-small cell lung cancer
Y Huang (2016)
10.1186/1477-7819-12-170
Predictors of pathological complete response to neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma
Ren-Wen Huang (2014)
10.1007/s00330-019-06427-x
A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma
P. Nie (2019)
10.1016/j.ejca.2013.05.029
Preoperative chemo(radio)therapy versus primary surgery for gastroesophageal adenocarcinoma: systematic review with meta-analysis combining individual patient and aggregate data.
U. Ronellenfitsch (2013)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)



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