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  • Explainable machine learnin...
    Teng, Xinzhi; Zhang, Jiang; Han, Xinyang; Sun, Jiachen; Lam, Sai-Kit; Ai, Qi-Yong Hemis; Ma, Zongrui; Lee, Francis Kar-Ho; Au, Kwok-Hung; Yip, Celia Wai-Yi; Chow, James Chung Hang; Lee, Victor Ho-Fun; Cai, Jing

    Radiologia medica, 07/2023, Letnik: 128, Številka: 7
    Journal Article

    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.