Development and validation of a multimodal feature fusion-based model for predicting postoperative recurrence-free
Feng Zhao1, Xiaoying Huang1, Jiangmiao Li1
1Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Objectives:
Given the high postoperative recurrence of locally advanced laryngeal squamous cell carcinoma (LSCC) and American Joint Committee on Cancer (AJCC) staging system prediction limitations, this study aims to construct and validate a postoperative recurrence-free survival (RFS) prediction model using multimodal feature fusion and explore data integration strategies to enhance prediction efficacy.
Methods:
Data from 278 patients diagnosed with locally advanced LSCC between 2013 and 2024 were collected retrospectively. These data were then separated into a training dataset (n = 196) and a validation dataset (n = 82), using a near 7:3 allocation strategy. By integrating clinicopathological features, preoperative blood markers, and enhanced computed tomography imaging data, we constructed clinicopathological (Clinic-score), radiomics (Rad-score), and two fusion models: feature-level (FF-Model) and decision-level (DF-Model). Model performance was evaluated using the concordance index, time-dependent area under the receiver operating characteristic curve, calibration curve, and decision curve analyses. Improvement in model discriminative ability was assessed using continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI).
Results:
At 24.5 months median follow-up, 95 patients (34.2%) experienced recurrence. In the validation set, the DF-Model significantly outperformed the FF-Model, Rad-score and Clinic-score models, and AJCC stages. Additionally, the DF-Model demonstrated superior calibration and clinical utility, better prediction of 1-year, 3-year, and 5-year RFS through cNRI/IDI analysis, and excellent risk stratification across datasets, AJCC stages, and tumor locations.
Conclusion:
The multimodal prediction DF-Model effectively integrates multi-source heterogeneous information, significantly improving the prediction accuracy of postoperative RFS in locally advanced LSCC, outperforming the FF-Model, single-modal models, and AJCC staging system, and demonstrating its potential clinical translational value.
