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An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer

British Journal of Cancer volume 129, pages 1095–1104 (2023)Cite this article

Subjects

Abstract

Background

Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC.

Methods

This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients, we developed the mrDEC scoring system and assessed inter-reader agreement. Additionally, we plotted Kaplan–Meier curves to compare the 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates among patients with different mrDEC scores.

Results

A total of 1287 patients with LARC were included in this study. We observed substantial inter-reader agreement for mrDEC. Based on the mrDEC scores ranging from 0 to 3, the patients were categorized into four groups. The 3-year DFS rates for the groups were 91.0%, 79.5%, 65.5%, and 44.0% (P < 0.0001), respectively, and the 5-year OS rates were 92.9%, 87.1%, 74.8%, and 44.5%, respectively (P < 0.0001).

Conclusions

The mrDEC scoring system proved to be an effective tool for predicting the prognosis of patients with LARC and can assist clinicians in clinical decision-making.

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Fig. 1: Evaluation workstreams and construction of scoring system.
Fig. 2: Heat map of inter-reader agreement for mrDEC score and MRI markers.
Fig. 3: The prognostic value of mrTD status in patients with the same mrN stage.
Fig. 4: Kaplan–Meier curves for disease-free survival (DFS).

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Data availability

All data supporting the findings in this study are presented in the manuscript and the supplementary information, and additional raw data can be made available by the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the National Postdoctoral Program for Innovative Talents of China (No.BX20220359), Science Foundation of Yunnan Basic Research (202201AT070010), NSFC Incubation Project of Guangdong Provincial People’s Hospital (KY0120220037), National Science Foundation for Young Scientists of China (82202267), National Natural Science Foundation of China (No. 82001789), National Science Fund for Distinguished Young Scholars [81925023], Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No.U22A20345), and Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application [2022B1212010011].

Funding

Funding sources can be found in “Acknowledgements” section.

Author information

Author notes

  1. These authors contributed equally: Minning Zhao, Lili Feng, Ke Zhao, Yanfen Cui, Zhenhui Li.

Authors and Affiliations

  1. Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

    Minning Zhao, Ke Zhao, Chenglu Ke, Qing Qiu, Yanting Liang & Zaiyi Liu

  2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China

    Minning Zhao

  3. Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China

    Minning Zhao & Xinyue Yang

  4. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China

    Lili Feng & ChuanMiao Xie

  5. Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lili Feng & ChuanMiao Xie

  6. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China

    Ke Zhao & Zaiyi Liu

  7. Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Ke Zhao

  8. Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, China

    Yanfen Cui

  9. Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China

    Zhenhui Li

  10. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China

    Weirong Lu

  11. Provincial Key Laboratory of Radiation Medicine in Henan (Under construction), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Xiangbo Wan

  12. Department of Radiation Oncology, the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Xiangbo Wan

  13. Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Xiangbo Wan

Authors
  1. Minning Zhao
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Contributions

Study design: MNZ, LLF and ZK. Performed the research and collected data: MNZ, LLF, YFC, ZHL, CMX and XBW. Analyzed the data: MNZ, CLK, XYY, QQ, WRL and YTL. Manuscript drafting: MNZ and KZ. Provided discussion, critical feedback and manuscript editing: ZYL, CMX, KZ and XBW.

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Correspondence to Ke Zhao, ChuanMiao Xie, Xiangbo Wan or Zaiyi Liu.

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Zhao, M., Feng, L., Zhao, K. et al. An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer. Br J Cancer 129, 1095–1104 (2023). https://doi.org/10.1038/s41416-023-02384-x

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