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FDG metabolic parameter-based models for predicting recurrence after upfront surgery in synchronous colorectal cancer liver metastasis

  • Nuclear Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

This study aimed to develop and validate post- and preoperative models for predicting recurrence after curative-intent surgery using an FDG PET-CT metabolic parameter to improve the prognosis of patients with synchronous colorectal cancer liver metastasis (SCLM).

Methods

In this retrospective multicenter study, consecutive patients with resectable SCLM underwent upfront surgery between 2006 and 2015 (development cohort) and between 2006 and 2017 (validation cohort). In the development cohort, we developed and internally validated the post- and preoperative models using multivariable Cox regression with an FDG metabolic parameter (metastasis-to-primary-tumor uptake ratio [M/P ratio]) and clinicopathological variables as predictors. In the validation cohort, the models were externally validated for discrimination, calibration, and clinical usefulness. Model performance was compared with that of Fong’s clinical risk score (FCRS).

Results

A total of 374 patients (59.1 ± 10.5 years, 254 men) belonged in the development cohort and 151 (60.3 ± 12.0 years, 94 men) in the validation cohort. The M/P ratio and nine clinicopathological predictors were included in the models. Both postoperative and preoperative models showed significantly higher discrimination than FCRS (p < .05) in the external validation (time-dependent AUC = 0.76 [95% CI 0.68–0.84] and 0.76 [0.68–0.84] vs. 0.65 [0.57–0.74], respectively). Calibration plots and decision curve analysis demonstrated that both models were well calibrated and clinically useful. The developed models are presented as a web-based calculator (https://cpmodel.shinyapps.io/SCLM/) and nomograms.

Conclusions

FDG metabolic parameter-based prognostic models are well-calibrated recurrence prediction models with good discriminative power. They can be used for accurate risk stratification in patients with SCLM.

Key Points

• In this multicenter study, we developed and validated prediction models for recurrence in patients with resectable synchronous colorectal cancer liver metastasis using a metabolic parameter from FDG PET-CT.

• The developed models showed good predictive performance on external validation, significantly exceeding that of a pre-existing model.

• The models may be utilized for accurate patient risk stratification, thereby aiding in therapeutic decision-making.

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Abbreviations

AUC:

Area under the receiver-operating characteristic curve

CI95% :

95% confidence interval

CLM:

Colorectal cancer liver metastasis

FCRS:

Fong’s clinical risk score

FDG:

2-[18F]fluoro-2-deoxy-D-glucose

M/P ratio:

Metastasis-to-primary-tumor uptake ratio

nSUVpeak :

Normalized peak standardized uptake value

RFS:

Recurrence-free survival

SCLM:

Synchronous colorectal cancer liver metastasis

SUV:

Standardized uptake value

SUVpeak :

Peak standardized uptake value

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Funding

This study was supported by a grant of the Medical Data-Driven Hospital Support Project through the Korea Health Information Service (KHIS), funded by the Ministry of Health & Welfare, Republic of Korea; the Medical Research Promotion Program, through the GangNeung Asan Hospital, funded by the Asan Foundation (2021IB001); the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIT) (2021R1G1A1009606); and the research grant from Korea University (K2023081).

Author information

Authors and Affiliations

  1. Department of Nuclear Medicine, GangNeung Asan Hospital, University of Ulsan College of Medicine, 38 Bangdong-gil, Sacheon-myeon, Gangneung-si, Gangwon-do, 25440, Republic of Korea

    Hyo Sang Lee

  2. Department of Nuclear Medicine, Korea University College of Medicine, Seoul, Republic of Korea

    Hyun Woo Kwon, Hyun Joo Kim & Sungeun Kim

  3. Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Seok-Byung Lim, Jin Cheon Kim & Chang Sik Yu

  4. Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Yong Sang Hong & Tae Won Kim

  5. Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Minyoung Oh, Sangwon Han & Jae Seung Kim

  6. Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea

    Jae Hwan Oh

  7. Department of Nuclear Medicine, Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea

    Sohyun Park, Tae-Sung Kim & Seok-ki Kim

  8. Department of Surgery, GangNeung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea

    Jae Young Kwak

  9. Division of Hemato-oncology in the Department of Internal Medicine, GangNeung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea

    Ho-Suk Oh

  10. Department of Surgery, Korea University College of Medicine, Seoul, Republic of Korea

    Jung-Myun Kwak

  11. Clinical Research Center in the Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Ji Sung Lee

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  1. Hyo Sang Lee
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Corresponding author

Correspondence to Hyo Sang Lee.

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Guarantor

The scientific guarantors of this publication are Hyo Sang Lee and Ji Sung Lee.

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

Ji Sung Lee (a PhD statistician) kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects (120 patients in the development cohort) have been previously reported in J Nucl Med. 2014; 55(4):582-9. The previous study was a prognostic marker study that analyzed the prognostic value of FDG metabolic parameters. The current study is about the prognostic modelling using the FDG metabolic parameter, which is the natural extension of the previous study.

Methodology

• retrospective

• prognostic study

• multicenter study

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Hyo Sang Lee and Hyun Woo Kwon equally share first-authorship.

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Lee, H.S., Kwon, H.W., Lim, SB. et al. FDG metabolic parameter-based models for predicting recurrence after upfront surgery in synchronous colorectal cancer liver metastasis. Eur Radiol 33, 1746–1756 (2023). https://doi.org/10.1007/s00330-022-09141-3

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  • DOI: https://doi.org/10.1007/s00330-022-09141-3

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