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OBJECTIVES To investigate the prognostic performance of multi-level CT-dose fusion dosiomics at the image-, matrix- and feature-levels from the gross tumor volume at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients. MATERIALS AND METHODS Two hundred and nineteen NPC patients (175 vs. 44 for training vs. internal validation) were used to train prediction model, and thirty two NPC patients were used for external validation. We first extracted CT and dose information from intratumoral nasopharynx (GTV_nx) and lymph node (GTV_nd) regions. Then the corresponding peritumoral regions (RING_3mm and RING_5mm) were also considered. Thus, the individual and combination of intra- and peri-tumoral regions were as follows: GTV_nx, GTV_nd, RING_3mm_nx, RING_3mm_nd, RING_5mm_nx, RING_5mm_nd, GTV_nxnd, RING_3mm_nxnd, RING_5mm_nxnd, GTV+RING_3mm_nxnd and GTV+RING_5mm_nxnd. For each region, eleven models were built by combining 5 clinical parameters and 127 features from (1) dose images alone; (2-7) fused dose and CT images via wavelet-based fusion (WF) using CT weights of 0.2, 0.4, 0.6 and 0.8, gradient transfer fusion (GTF), and guided filtering-based fusion (GFF); (8) fused matrices (sumMat); (9-10) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The C-index and Kaplan-Meier curves with log-rank test were used to assess model performance. RESULTS The fusion models' performance was better than single CT/dose model on both internal and external validation. Models combined the information from both GTV_nx and GTV_nd regions outperformed the single region model. For internal validation, GTV+RING_3mm_nxnd GFF model achieved the highest C-index both in recurrence-free survival (RFS) and metastasis-free survival (MFS) predictions (RFS: 0.822; MFS: 0.786). The highest C-index in external validation set was achieved by RING_3mm_nxnd model (RFS: 0.762; MFS: 0.719). The GTV+RING_3mm_nxnd GFF model is able to significantly separate patients into high-risk and low-risk groups compared to dose-only or CT-only models. CONCLUSION Fusion dosiomics model combining the primary tumor, the involved lymph node, and 3mm peritumoral information outperformed single modality models for different outcome predictions, which is helpful for clinical decision-making and the development of personalized treatment. This article is protected by copyright. All rights reserved.