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01.07.2023 | Head, Neck and Dental Radiology

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

verfasst von: Xinzhi Teng, Jiang Zhang, 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, Jing Cai

Erschienen in: La radiologia medica | Ausgabe 7/2023

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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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Titel: Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma
verfasst von: Xinzhi Teng
Jiang Zhang
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
Jing Cai
Publikationsdatum: 01.07.2023
Verlag: Springer Milan
Erschienen in: La radiologia medica / Ausgabe 7/2023
Print ISSN: 0033-8362
Elektronische ISSN: 1826-6983
DOI: https://doi.org/10.1007/s11547-023-01650-5

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