Purpose: To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with stage II nasopharyngeal carcinoma (NPC) to identify patients with a low risk of progression for whom intensity-modulated radiotherapy (IMRT) alone is sufficient.
Methods: This multicenter, retrospective study enrolled 999 patients with stage II NPC from two centers. 3DResNet was used to extract deep learning MRI features and eXtreme Gradient Boosting model was employed to integrate the pre-trained features and clinical information to obtain an overall score for each patient. Based on the optimal cutoff value of the overall score, patients were stratified into high- and low- risk groups. Model performance was evaluated using concordance indexes (C-indexes), the area under the curve (AUC) values and calibration tests. Survival curves were used to analyze the clinical benefits of additional chemotherapy in each risk group.
Results: The combined model achieved a concordance index of 0.789 (95 % confidence interval [CI] 0.787-0.791), 0.768 (95 % CI 0.764-0.771), and 0.804 (95 % CI 0.801-0.807) for the training, internal validation, and external test cohorts, respectively, demonstrating a statistically significant improvement compared to the MRI model, T Stage, and N Stage. An overall score of < 0.405 in patients was significantly associated with a low risk of progression. In the low-risk group, patients treated with IMRT alone had comparable or even superior progression-free survival (PFS) compared to those who received additional chemotherapy.
Conclusion: The model demonstrated a satisfactory prognostic and predictive performance for PFS. Patients with stage II NPC were stratified into different risk groups to help identify optimal candidates who could benefit from IMRT alone.
Keywords: Deep Learning; Intensity-modulated radiotherapy; Nasopharyngeal carcinoma; Prognostic and predictive model; Progression-free survival.
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