ArticleGland surgery2026
Development and external validation of a machine learning model for predicting overall survival in head and neck adenoid cystic carcinoma based on the SEER database and a Chinese clinical cohort.
Article in Gland surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Adenoid cystic carcinoma (ACC) of the head and neck is a rare malignancy with high rates of perineural invasion (PNI) and distant metastasis. Although the tumor-node-metastasis (TNM) staging system provides a crucial anatomical foundation, it may not fully capture the biological heterogeneity of ACC. Existing predictive models often lack robust external validation or contain too many variables for routine clinical use. This study aimed to develop and validate a simplified prognostic model using a multi-algorithm machine learning (ML) consensus approach. Methods: This retrospective study included patients with pathologically confirmed primary head and neck ACC and complete follow-up data from two cohorts: a training cohort from the Surveillance, Epidemiology, and End Results (SEER) database (n=2,870) and an external validation cohort from Sichuan Cancer Hospital (n=172). The primary endpoint was overall survival (OS), defined as a binary outcome (all-cause mortality Results: A total of 3,042 patients were analyzed. In the external cohort, the median follow-up was 86.0 months, with 49 mortality events (28.5%) observed. Six core prognostic features were identified: M stage, age, brain metastasis, T stage, surgery, and N stage. The simplified LR model achieved the best performance among all tested algorithms, with an area under the curve (AUC) of 0.825 in the internal testing set. In the external validation cohort, it achieved an AUC of 0.817, outperforming the other three algorithms, with good calibration observed. Conclusions: This study developed a simplified prognostic model for head and neck ACC based on six core features. The model showed promising discrimination in both the internal testing set (AUC =0.825) and the external validation cohort (AUC =0.817). However, the external cohort was relatively small (n=172), which limits our ability to fully assess the model's generalizability. These findings should therefore be interpreted with caution. Future prospective studies with larger, multi-center cohorts are needed to confirm these results before the model can be recommended for routine clinical use.
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