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T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer

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Abstract

Background

To prospectively develop and validate the T2WI texture analysis model based on a node-by-node comparison for improving the diagnostic accuracy of lymph node metastasis (LNM) in rectal cancer.

Methods

A total of 381 histopathologically confirmed lymph nodes (LNs) were collected. LNs texture features were extracted from MRI-T2WI. Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection to construct the LN rad-score. Then the clinical risk factors and LN texture features were combined to establish combined predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) and nomogram were used to evaluate the clinical application of the model.

Results

A total of 107 texture features were extracted from LN-MRI images. After selection and dimensionality reduction, the radiomics prediction model consisting of 8 texture features showed well-predictive performance in the training and validation cohorts (AUC, 0.676; 95% CI 0.582–0.771) (AUC, 0.774; 95% CI 0.648–0.899). A clinical–radiomics prediction model with the best performance was created by combining clinical and radiomics features, 0.818 (95% CI 0.742–0.893) for the training and 0.922 (95% CI 0.863–0.980) for the validation cohort. The LN Rad-score in clinical–radiomics nomogram obtained the highest classification contribution and was well calibrated. DCA demonstrated the superiority of the clinical–radiomics model.

Conclusion

The lymph node T2WI-based texture features can help to improve the preoperative prediction of LNM.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

MRI:

Magnetic resonance imaging

LNM:

Lymph node metastasis

LNs:

Lymph nodes

ROC:

Receiver operating characteristic

AUC:

Area under the curve

DCA:

Decision curve analysis

TME:

Total mesorectal excision

CEA:

Carcinoembryonic antigen

CA19-9:

Carbohydrate antigen 19–9

LASSO:

Least absolute shrinkage and selection operator

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Acknowledgements

Not applicable

Funding

This study was supported by Department of Science and Technology of Sichuan Province (No. 2021YFS0025); 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (No. 2019HXFH031; 20HXJS003); Post-Doctor Research Project, West China Hospital, Sichuan University (No.2021HXBH033; 20826041E4084); the Ethicon Excellent in Surgery Grant (EESG) (No. HZB-20190528-4).

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Authors and Affiliations

  1. Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China

    Zixuan Zhuang, Yang Zhang, Xuyang Yang, Xiangbing Deng & Ziqiang Wang

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  1. Zixuan Zhuang
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  2. Yang Zhang
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Contributions

ZZ designed the project, developed the search strategy, and wrote the manuscript. YZ, XD, and XY participated in feature extraction, data curation, and formal analysis. ZW designed the conception, supervision, and revised critical intellectual content. All authors read and approved the final manuscript and agreed to be accountable for all aspects of work to ensure that questions regarding accuracy and integrity investigated and resolved.

Corresponding author

Correspondence to Zixuan Zhuang.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval

This study was approved by the Medical Ethics Committee of West China Hospital.

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Informed consent was obtained in accordance with the standards set forth by hospital regulations.

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Zhuang, Z., Zhang, Y., Yang, X. et al. T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04209-8

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