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Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning

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Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Purpose

To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on 18F-FDG PET/CT and radiomic features using machine-learning methods.

Methods

A total of 199 colorectal cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and CRC radical surgery. The Chang-Gung Image Texture Analysis toolbox (CGITA) was used to extract 70 PET radiomic features reflecting 18F-FDG uptake heterogeneity of tumors. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomic signature score (Rad-score). The training set was used to establish five machine-learning prediction models and the test set was used to test the efficacy of the models. The effectiveness of the models was compared by ROC analysis.

Results

The CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808–0.925) and XGBoost (AUC 0.903, 95% CI 0.855–0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by 18F-FDG PET/CT for differentiating positive and negative regional LN metastases in CRC (all p < 0.05). Good efficacy of the above two models was also achieved in the test set. We created a nomogram based on the logistic regression model that visualized the results and provided an easy-to-use method for predicting regional LN metastasis in patients with CRC.

Conclusion

In this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on 18F-FDG PET/CT and PET-based radiomic features were successfully developed and validated. Among them, the logistic regression and XGBoost models performed the best, with higher efficacy than 18F-FDG PET/CT in both the training and test sets.

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Code availability

The code in this study is written in R (https://www.r-project.org/).

Abbreviations

AUC:

Area under the curve

CEA:

Carcinoembryonic antigen

CGITA:

Chang-Gung Image Texture Analysis toolbox

CI:

Confidence interval

CRC:

Colorectal cancer

DICOM:

Digital imaging and communications in medicine

18F-FDG:

18F-Fluorodeoxy Glucose

LASSO:

Least absolute shrinkage and selection operator

LN:

Lymph node

NCCN:

National Comprehensive Cancer Network

NPV:

Negative predictive value

MBq:

Megabecquerel

MC:

Mean convergence

PET/CT:

Positron Emission/Computed Tomography

PPV:

Positive predictive value

ROC:

Receiver-operating characteristic

ROI:

Region of interest

SD:

Standard deviation

SE:

Standard error

SUV:

Standardized uptake value

SVM:

Support vector machine

TFC:

Texture feature coding

XGBoost:

eXtreme gradient boosting

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Acknowledgements

The authors appreciate all the staff of the PET Center of Nanfang Hospital of Southern Medical University for their important help in this study.

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Authors and Affiliations

  1. Department of Radiology, The Second Affiliated Hospital of Shenzhen University, The People’s Hospital of Baoan Shenzhen, Shenzhen, 518100, Guangdong, China

    Jiahong He, Yongsheng Zhou & Shuangquan Zhao

  2. PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Quanshi Wang, Yin Zhang & Hubing Wu

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  1. Jiahong He
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All the authors contributed to the study conception and design.

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Correspondence to Jiahong He.

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He, J., Wang, Q., Zhang, Y. et al. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning. Ann Nucl Med 35, 617–627 (2021). https://doi.org/10.1007/s12149-021-01605-8

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