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Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics

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Abstract

Purpose

To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning.

Methods

The radiomic features and dosimetric parameters of 94 EC patients were extracted and modeled using Support Vector Classification (SVM) and Extreme Gradient Boosting algorithm (XGBoost). The 94-sample dataset was randomly divided into a 70-sample training subset and a 24-sample independent test set while keeping the class proportions intact via stratification. A receiver operating characteristic (ROC) curve was used to assess the performance of models using radiomic features alone and using combined radiomic features and dosimetric parameters.

Results

A total of 42 radiomic features and 18 dosimetric parameters plus the patients’ characteristic parameters were extracted for these 94 cases (58 responders and 36 non-responders). XGBoost plus principal component analysis (PCA) achieved an accuracy and area under the curve of 0.708 and 0.541, respectively, for models with radiomic features combined with dosimetric parameters, and 0.689 and 0.479, respectively, for radiomic features alone. Image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the model. The dosimetric parameters of gross tumor volume (GTV) homogeneity index (HI), Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.

Conclusions

The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomic features alone in predicting the treatment response of patients with EC who underwent CRT.

Key Points

• The model with radiomic features combined with dosimetric parameters is promising in predicting the treatment response of patients with EC who underwent CRT.

• The model with radiomic features combined with dosimetric parameters (prediction accuracy of 0.708 and AUC of 0.689) outperforms that with radiomic features alone (best prediction accuracy of 0.625 and AUC of 0.412).

• The image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the treatment response prediction model. The dosimetric parameters of GTV HI, Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.

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Abbreviations

3DCRT:

Three-dimensional conformal radiotherapy

AUC:

Area under curve

CRT:

Chemoradiation

CT:

Computed tomography

EC:

Esophageal cancer

FDG-PET:

Fluorodeoxyglucose positron emission tomography

GLCM:

Gray-level co-occurrence matrix

GTV:

Gross tumor volume

HI:

Homogeneity index

ID:

Intensity direct

IMRT:

Intensity-modulated radiotherapy

NID:

Neighbor intensity difference

NR:

Non-responsive

OARs:

Organs at risk

OS:

Overall survival; CR: complete response

PCA:

Principal component analysis

RBF:

Radial basis function

ROC:

Receiver operating characteristic

RS:

Responsive

SCC:

Squamous cell carcinoma

SVM:

Support vector classification

TPS:

Treatment planning system

VMAT:

Volumetric-modulated arc therapy

XGBoost:

Extreme Gradient Boosting algorithm

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Funding

This study has received funding by National Natural Science Foundation of China (11675122).

Author information

Author notes

  1. Xiance Jin and Xiaomin Zheng contributed equally to this work.

Authors and Affiliations

  1. Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, No.2 Fuxue Lane, Wenzhou, 325000, People’s Republic of China

    Xiance Jin, Xiaomin Zheng, Didi Chen, Xia Deng, Ce Han, Changfei Gong, Yongqiang Zhou & Congying Xie

  2. Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People’s Republic of China

    Juebin Jin

  3. College of Control Science and Engineering, Zhejiang University, 866 Yuhangtang Rd, Hangzhou, 310058, People’s Republic of China

    Guojie Zhu

  4. Ningbo Institute of Technology, Zhejiang University, 1 South Qianhu Rd, Ningbo, 315100, People’s Republic of China

    Cong Liu

Authors
  1. Xiance Jin
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Corresponding authors

Correspondence to Cong Liu or Congying Xie.

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Guarantor

The scientific guarantor of this publication is Congying Xie.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise: Cong Liu.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

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• performed at one institution

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Jin, X., Zheng, X., Chen, D. et al. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol 29, 6080–6088 (2019). https://doi.org/10.1007/s00330-019-06193-w

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  • DOI: https://doi.org/10.1007/s00330-019-06193-w

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