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Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma

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Abstract

We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). Recurrence/non-recurrence data were collected from 131 patients after intensity-modulated radiation therapy. The cases were divided into training (80%) and test (20%) datasets. A total of 327 dosiomics features, including cold spot volume, first-order features, and texture features, were extracted from the original dose distribution (ODD) and LBP on gross tumor volume, clinical target volume, and planning target volume. The CoxNet algorithm was employed in the training dataset for feature selection and dosiomics signature construction. Based on a dosiomics score (DS)-based Cox proportional hazard model, two recurrence prediction models (DSODD and DSLBP) were constructed using the ODD and LBP dosiomics features. These models were used to evaluate the overall adequacy of the recurrence prediction using the concordance index (CI), and the prediction performance was assessed based on the accuracy and area under the receiver operating characteristic curve (AUC). The CIs for the test dataset were 0.71 and 0.76 for DSODD and DSLBP, respectively. The accuracy and AUC for the test dataset were 0.71 and 0.76 for the DSODD model and 0.79 and 0.81 for the DSLBP model, respectively. LBP-based dosiomics models may be more accurate in predicting recurrence after radiation therapy in patients with HNSCC.

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Acknowledgements

We would like to thank Editage (www.editage.com) for English language editing.

Funding

This work was supported by JSPS KAKENHI Grant Number JP22K15808 and Teikyo University Research Grant Number 2100007.

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Authors and Affiliations

  1. Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22 Misaki-machi, Omuta-City, Fukuoka, 836-8505, Japan

    Hidemi Kamezawa

  2. Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Hidetaka Arimura

Authors
  1. Hidemi Kamezawa
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Contributions

All the authors contributed to the conception and design of the study. The material preparation, data collection, and analysis were performed by HK. The first draft of the manuscript was written by HK, and all the authors commented on the previous versions of the manuscript. All authors have read and approved the final manuscript.

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Correspondence to Hidemi Kamezawa.

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Kamezawa, H., Arimura, H. Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma. Phys Eng Sci Med 46, 99–107 (2023). https://doi.org/10.1007/s13246-022-01201-8

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  • DOI: https://doi.org/10.1007/s13246-022-01201-8

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