A novel nomogram on predicting extrahepatic metastasis in colorectal cancer with liver metastasis for selective application of <sup>18</sup>F-FDG PET/CT

<b>Background:</b> A more accurate assessment of extrahepatic metastases (EHMs) with colorectal cancer liver metastases (CRLMs) improve patient prognosis without unnecessary surgery and economic burden. At present, PET-CT can only be used as a second-line modality. We aimed to construct a predictive model for EHMs, and provide guidance for the selective application of <sup>18</sup>F-FDG PET/CT. <b>Methods:</b> The clinical data of patients with CRLMs between December 2018 and February 2023 were retrospectively retrieved from the medical records of three large-capacity hospitals. Moreover, we explored the need for <sup>18</sup>F-FDG PET/CT to be used selectively for detecting EHMs with CRLMs. <b>Results:</b> A total of 471 patients from two hospitals were included in the training set, 174 of whom had CRLMs and EHMs. Notably, the percentages of patients with positive serum CEA, CA19-9 and CA-125 levels were significantly greater in patients with CRLMs and EHMs than in those with liver-limited metastases. Univariate and multivariate logistic regression analyses revealed that the serum levels of CEA and CA-125 and multiple liver metastases were independent risk factors for EHMs. Additionally, we recruited 190 patients with CRLMs from one of the hospitals as the validation set. The nomogram model achieved stable and accurate prediction results in the training and validation sets (AUC = 0.768 and 0.733), and was significantly superior to CEA and CA19-9. Moreover, the sensitivity and specificity of <sup>18</sup>F-FDG PET/CT for the diagnosis of EHMs were 100% and 88%, respectively. <b>Conclusions:</b> We constructed and validated a nomogram on predicting the risk of EHMs in patients with CRLMs, which can guide clinicians to selective application of <sup>18</sup>F-FDG PET/CT.

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Journal
International Journal of Medical Sciences
Published
2025-01-01
DOI
https://doi.org/10.7150/ijms.104453
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

A novel nomogram on predicting extrahepatic metastasis in colorectal cancer with liver metastasis for selective application of 18F-FDG PET/CT

Yipu Zhao, Jie Zhao, Libo Wang, Bin Zhang et al.
International Journal of Medical Sciences
Radiomics and Machine Learning in Medical Imaging
article

A novel nomogram on predicting extrahepatic metastasis in colorectal cancer with liver metastasis for selective application of 18F-FDG PET/CT

Yipu Zhao, Jie Zhao, Libo Wang, Bin Zhang, Sidong Wei, Rongtao Zhu
article en

Abstract

<b>Background:</b> A more accurate assessment of extrahepatic metastases (EHMs) with colorectal cancer liver metastases (CRLMs) improve patient prognosis without unnecessary surgery and economic burden. At present, PET-CT can only be used as a second-line modality. We aimed to construct a predictive model for EHMs, and provide guidance for the selective application of <sup>18</sup>F-FDG PET/CT. <b>Methods:</b> The clinical data of patients with CRLMs between December 2018 and February 2023 were retrospectively retrieved from the medical records of three large-capacity hospitals. Moreover, we explored the need for <sup>18</sup>F-FDG PET/CT to be used selectively for detecting EHMs with CRLMs. <b>Results:</b> A total of 471 patients from two hospitals were included in the training set, 174 of whom had CRLMs and EHMs. Notably, the percentages of patients with positive serum CEA, CA19-9 and CA-125 levels were significantly greater in patients with CRLMs and EHMs than in those with liver-limited metastases. Univariate and multivariate logistic regression analyses revealed that the serum levels of CEA and CA-125 and multiple liver metastases were independent risk factors for EHMs. Additionally, we recruited 190 patients with CRLMs from one of the hospitals as the validation set. The nomogram model achieved stable and accurate prediction results in the training and validation sets (AUC = 0.768 and 0.733), and was significantly superior to CEA and CA19-9. Moreover, the sensitivity and specificity of <sup>18</sup>F-FDG PET/CT for the diagnosis of EHMs were 100% and 88%, respectively. <b>Conclusions:</b> We constructed and validated a nomogram on predicting the risk of EHMs in patients with CRLMs, which can guide clinicians to selective application of <sup>18</sup>F-FDG PET/CT.

International Journal of Medical SciencesVol. 22(3)
Henan Provincial People's Hospital (CN), Henan Cancer Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN)
Good health and well-being
Openalex Percentile: Top 100%
Radiomics and Machine Learning in Medical Imaging
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