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Abdominal Radiology

, Volume 44, Issue 9, pp 2978–2987 | Cite as

Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

  • Lijuan Wan
  • Chongda Zhang
  • Qing Zhao
  • Yankai Meng
  • Shuangmei Zou
  • Yang Yang
  • Yuan Liu
  • Jun Jiang
  • Feng Ye
  • Han Ouyang
  • Xinming Zhao
  • Hongmei ZhangEmail author
Special Section: Rectal Cancer
  • 88 Downloads

Abstract

Purpose

The aim of this study was to build an appropriate diagnostic model for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), by combining magnetic resonance imaging (MRI) parameters with clinical factors.

Methods

Eighty-four patients with LARC who underwent MR examination before and after nCRT were enrolled in this study. MRI parameters including cylindrical approximated tumor volume (CATV) and relative signal intensity of tumor (rT2wSI) were measured; corresponding reduction rates (RR) were calculated; and MR tumor regression grade (mrTRG) and other conventional MRI parameters were assessed. Logistic regression with lasso regularization was performed and the appropriate prediction model for pCR was built up. An external cohort of thirty-six patients was used as the validation group for testing the model. Receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performance.

Results

In the development and the validation group, 17 patients (20.2%) and 11 patients (30.6%), respectively, achieved pCR. Two CATV-related parameters (CATVpost, which is the CATV measured after nCRT and CATVRR), one rT2wSI-related parameter (rT2wSIRR), and mrTRG were the most important parameters for predicting pCR and were retained in the diagnostic model. In the development group, the area under the receiver-operating characteristic curve (AUC) for predicting pCR is 0.88 [95% confidence interval (CI) 0.78–0.97, p < 0.001], with a sensitivity of 82.4% and a specificity of 83.6%. In the validation group, the AUC is 0.84 (95% CI 0.70–0.98, p = 0.001), with a sensitivity of 81.8% and a specificity of 76.0%.

Conclusion

A diagnostic model including CATVpost, CATVRR, rT2wSIRR, and mrTRG was useful for predicting pCR after nCRT in patients with LARC and may be used as an effective organ-preservation strategy.

Keywords

Rectal neoplasms Magnetic resonance imaging Magnetic resonance tumor regression grading Neoadjuvant chemoradiotherapy Pathological complete response 

Abbreviations

ADC

Apparent diffusion coefficient

CP

Circumferential percentage

CATV

Cylindrical approximated tumor volume

CATVRR

The reduction rate of cylindrical approximated tumor volume

DTA

Distance from tumor to anal verge

EMVI

Extramural venous invasion

LARC

Locally advanced rectal cancer

MRF

Mesorectal fascia

mrTRG

Magnetic resonance tumor regression grading

nCRT

Neoadjuvant chemoradiotherapy

pCR

Pathological complete response

rT2wSI

Relative signal intensity of tumor

rT2wSIRR

The reduction rate of relative signal intensity of tumor

TME

Total mesorectal excision

TP

Tumor position

Notes

Acknowledgements

The authors acknowledge Sainan Cheng and Shunan Che for their assistance with statistical analyses.

Conflict of interest

All authors (Lijuan Wan, Chongda Zhang, Qing Zhao, Yankai Meng, Shuangmei Zou, Yang Yang, Yuan Liu, Jun Jiang, Feng Ye, Han Ouyang, Xinming Zhao, and Hongmei Zhang) have no conflicts of interest to be disclosed related to this article.

Author contributions

Study concepts/study design, data acquisition or data analysis/interpretation, all authors; quality control of date and algorithms, Jun Jiang, Feng Ye, Han Ouyang, Hongmei Zhang; statistical analysis, Lijuan Wan, Chongda Zhang, Hongmei Zhang. drafting the article or revising it critically for important intellectual content, all author; final approval of the version to be submitted, all author.

Funding information

This research is supported by the Special scientific research projects of Beijing science and technology project [Grant Number Z16110000051610]; Beijing hope marathon special fund [Grant Number LC2016A05]; Peking Union Medical College Youth Fund, the Fundamental Research Funds for the Central Universities [Grant Number 3332018078]; Beijing Hope Run Special Fund of the Cancer Foundation of China [Grant Number LC2017B18].

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Lijuan Wan
    • 1
  • Chongda Zhang
    • 1
  • Qing Zhao
    • 1
  • Yankai Meng
    • 1
  • Shuangmei Zou
    • 2
  • Yang Yang
    • 1
  • Yuan Liu
    • 1
  • Jun Jiang
    • 1
  • Feng Ye
    • 1
  • Han Ouyang
    • 1
  • Xinming Zhao
    • 1
  • Hongmei Zhang
    • 1
    Email author
  1. 1.Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
  2. 2.Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

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