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Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma

  • Head, Neck and Dental Radiology
  • Published:
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Abstract

Purpose

This study aimed to discover intra-tumor heterogeneity signature and validate its predictive value for adjuvant chemotherapy (ACT) following concurrent chemoradiotherapy (CCRT) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC).

Materials and methods

397 LA-NPC patients were retrospectively enrolled. Pre-treatment contrast-enhanced T1-weighted (CET1-w) MR images, clinical variables, and follow-up were retrospectively collected. We identified single predictive radiomic feature from primary gross tumor volume (GTVnp) and defined predicted subvolume by calculating voxel-wised feature mapping and within GTVnp. We independently validate predictive value of identified feature and associated predicted subvolume.

Results

Only one radiomic feature, gldm_DependenceVariance in 3 mm-sigma LoG-filtered image, was discovered as a signature. In the high-risk group determined by the signature, patients received CCRT + ACT achieved 3-year disease free survival (DFS) rate of 90% versus 57% (HR, 0.20; 95%CI, 0.05–0.94; P = 0.007) for CCRT alone. The multivariate analysis showed patients receiving CCRT + ACT had a HR of 0.21 (95%CI: 0.06–0.68, P = 0.009) for DFS compared to those receiving CCRT alone. The predictive value can also be generalized to the subvolume with multivariate HR of 0.27 (P = 0.017) for DFS.

Conclusion

The signature with its heterogeneity mapping could be a reliable and explainable ACT decision-making tool in clinical practice.

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Data Availability

The patients' clinical and DICOM data are not publicly available for patient privacy protection purposes. Requests to access these datasets should be directed to the corresponding author.

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Acknowledgements

We would like to thank the radiation oncology department of Queen Elizabeth Hospital for providing valuable clinical and radiotherapy data.

Funding

This research was partly supported by research grants of Mainland-Hong Kong Joint Funding Scheme (MHKJFS) (MHP/005/20), Shenzhen-Hong Kong-Macau S&T Program (Category C) (SGDX20201103095002019), Shenzhen Basic Research Program (JCYJ20210324130209023), Project of Strategic Importance Fund (P0035421) and Projects of RISA (P0043001) from The Hong Kong Polytechnic University, and Health and Medical Research Fund (HMRF 09200576), Health Bureau, The Government of the Hong Kong Special Administrative Region.

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Authors and Affiliations

  1. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Y921, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong SAR, China

    Xinzhi Teng, Jiang Zhang, Xinyang Han, Jiachen Sun, Qi-Yong Hemis Ai, Zongrui Ma & Jing Cai

  2. Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China

    Sai-Kit Lam

  3. Department of Clinical Oncology, Queen Elizabeth Hospital, Yau Ma Tei, Hong Kong SAR, China

    Francis Kar-Ho Lee, Kwok-Hung Au, Celia Wai-Yi Yip & James Chung Hang Chow

  4. Department of Clinical Oncology, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China

    Victor Ho-Fun Lee

  5. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China

    Jing Cai

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  1. Xinzhi Teng
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Contributions

Xinzhi Teng and Jiang Zhang are both first authors of the manuscript. Xinzhi Teng and Jiang Zhang are contributed to manuscript writing and statistical analysis. Xinyang Han, Jiachen Sun, Sai-Kit Lam, Qi-Yong Hemis Ai, Zongrui Ma, Francis Kar-Ho Lee, Kwok-Hung Au, Celia Wai-Yi Yip, James Chung Hang Chow, Victor Ho-Fun Lee are contributed to data collection, data curation, and manuscript writing.

Corresponding author

Correspondence to Jing Cai.

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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 and consent to participate

The use of data was approved by the Research Ethics Committee (Kowloon Central/Kowloon East), reference number KC/KE-18-0085/ER-1. Due to the retrospective nature of retrospective study, the consent was waived.

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Teng, X., Zhang, J., Han, X. et al. Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma. Radiol med 128, 828–838 (2023). https://doi.org/10.1007/s11547-023-01650-5

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