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European Radiology

, Volume 29, Issue 3, pp 1211–1220 | Cite as

Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

  • Yanfen Cui
  • Xiaotang YangEmail author
  • Zhongqiang Shi
  • Zhao Yang
  • Xiaosong Du
  • Zhikai Zhao
  • Xintao Cheng
Oncology

Abstract

Objectives

To develop and validate a radiomics predictive model based on pre-treatment multiparameter magnetic resonance imaging (MRI) features and clinical features to predict a pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT).

Methods

One hundred and eighty-six consecutive patients with LARC (training dataset, n = 131; validation dataset, n = 55) were enrolled in our retrospective study. A total of 1,188 imaging features were extracted from pre-CRT T2-weighted (T2-w), contrast-enhanced T1-weighted (cT1-w) and ADC images for each patient. Three steps including least absolute shrinkage and selection operator (LASSO) regression were performed to select key features and build a radiomics signature. Combining clinical risk factors, a radiomics nomogram was constructed. The predictive performance was evaluated by receiver operator characteristic (ROC) curve analysis, and then assessed with respect to its calibration, discrimination and clinical usefulness.

Results

Thirty-one of 186 patients (16.7%) achieved pCR. The radiomics signature derived from joint T2-w, ADC, and cT1-w images, comprising 12 selected features, was significantly associated with pCR status and showed better predictive performance than signatures derived from either of them alone in both datasets. The radiomics nomogram, incorporating the radiomics signature and MR-reported T-stages, also showed good discrimination, with areas under the ROC curves (AUCs) of 0.948 (95% CI, 0.907-0.989) and 0.966 (95% CI, 0.924-1.000), as well as good calibration in both datasets. Decision curve analysis confirmed its clinical usefulness.

Conclusions

This study demonstrated that the pre-treatment radiomics nomogram can predict pCR in patients with LARC and potentially guide treatments to select patients for a “wait-and-see” policy.

Key Points

• Radiomics analysis of pre-CRT multiparameter MR images could predict pCR in patients with LARC.

• Proposed radiomics signature from joint T2-w, ADC and cT1-w images showed better predictive performance than individual signatures.

• Most of the clinical characteristics were unable to predict pCR.

Keywords

Nomograms Predictive value of tests Magnetic resonance imaging Rectal neoplasms Neoadjuvant therapy 

Abbreviations

CA199

Carbohydrate antigen-199

CEA

Carcinoembryonic antigen

CRT

Chemoradiotherapy

DCA

Decision curve analysis

GLCM

Grey-level co-occurrence matrix

GLRLM

Grey-level run length matrix

GLSZM

Grey-level size zone matrix

LARC

Locally advanced rectal cancer

LASSO

Least absolute shrinkage and selection operator

pCR

Pathological complete response

TME

Total mesorectal excision

TRG

Tumour response grading

Notes

Funding

This work was supported by the fund of Science and Technology Project of Shanxi Province (No. 20150313007-5).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Xiaotang Yang.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5683_MOESM1_ESM.doc (267 kb)
ESM 1 (DOC 267 kb)
330_2018_5683_MOESM2_ESM.doc (58 kb)
ESM 2 (DOC 58 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Yanfen Cui
    • 1
  • Xiaotang Yang
    • 1
    Email author
  • Zhongqiang Shi
    • 2
  • Zhao Yang
    • 1
  • Xiaosong Du
    • 1
  • Zhikai Zhao
    • 1
  • Xintao Cheng
    • 1
  1. 1.Department of Radiology, Shanxi Province Cancer HospitalShanxi Medical UniversityTaiyuanChina
  2. 2.GE Healthcare ChinaShanghaiChina

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