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Radiomics from dual-energy CT-derived iodine maps predict lymph node metastasis in head and neck squamous cell carcinoma

  • Computed Tomography
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Abstract

Objective

To develop and validate an iodine maps-based radiomics nomogram for preoperatively predicting cervical lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC).

Materials and methods

A total of 278 patients who pathologically confirmed as HNSCC were retrospectively recruited from two medical centers between June 2012 and July 2022. The training set (n = 152) and internal set (n = 67) were randomly selected from medical center A, and the patients from medical center B were enrolled as the external set (n = 69). The minority group in the training set was balanced by the adaptive synthetic sampling (ADASYN) approach. Radiomics features were extracted from dual-energy CT-derived iodine maps at arterial phase (AP) and venous phase (VP), respectively. Three radiomics signatures were constructed to predict the LNM by using a random forest algorithm. The independent clinical predictors for LNM were identified by multivariate analysis and combined with radiomics signatures to establish a radiomic–clinical nomogram. The performance of radiomic–clinical nomogram was evaluated with respect to its discrimination and clinical usefulness.

Results

The AP–VP-incorporated radiomics model exhibited a great predictive performance for LNM prediction with an area under curve (AUC) of 0.885 (95% CI, 0.836–0.933) in ADASYN-training set and confirmed in all validation sets. The nomogram that incorporated AP–VP radiomics signatures, CT-reported LN status, and histological grades yielded AUCs of 0.920 (95% CI, 0.881–0.959) in ADASYN-training set, 0.858 (95% CI, 0.771–0.944) in internal validation, and 0.849 (95% CI, 0.752–0.946) in external validation, with good calibration in all cohorts (p > 0.05). Decision curve analyses indicated the nomogram was clinically useful. In addition, the predictive performance of clinical–radiomics nomogram was also validation in combing cohorts. Stratified analysis confirmed the stability of nomogram, particularly in group negative for CT-reported LNM.

Conclusion

Clinical–radiomics nomogram based on iodine maps exhibited promising performance in predicting LNM and providing valuable information for making individualized therapy decisions.

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Acknowledgements

The authors would like to thank Dr. Siyun Liu (GE Healthcare, Beijing) for data analysis and statistical support.

Funding

This work was supported by the Natural Science Foundation of China (No. 81960310).

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Author notes

  1. Weiyuan Zhang and Jin Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China

    Weiyuan Zhang, Wenfeng Jin, Ruihong Li, Xiaojie Xie, Wen Zhao & Dan Han

  2. Center of PET/CT, 920th Hospital of Joint Logistics Support Force, PLA, Kunming, 650032, China

    Jin Liu

  3. Department of Radiology, The First Central Clinical School, Tianjin Medical University, Tianjin, 300192, China

    Shuang Xia

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Contributions

WZ and JL contributed to this study equally. The study conception and design were directed by DH. Material preparation and data collection and analysis were performed by WZ, JL, WFJ, RL, XX, WZ, and SX. The first draft of the manuscript was written by WZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Dan Han.

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This retrospective study was approved by the institutional review board at The First Affiliated Hospital of Kunming Medical University, and the informed consent was waived.

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Zhang, W., Liu, J., Jin, W. et al. Radiomics from dual-energy CT-derived iodine maps predict lymph node metastasis in head and neck squamous cell carcinoma. Radiol med 129, 252–267 (2024). https://doi.org/10.1007/s11547-023-01750-2

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