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
This study developed a simplified prognostic model for head and neck ACC based on six core features.
Adenoid cystic carcinoma (ACC) of the head and neck is a rare malignancy with high rates of perineural invasion (PNI) and distant metastasis. This study aimed to develop and validate a simplified prognostic model using a multi‑algorithm machine learning (ML) consensus approach. The study included 3,042 patients from the SEER database and an external Sichuan cohort. Six core prognostic features were identified: M stage, age, brain metastasis, T stage, surgery, and N stage. The simplified logistic regression model achieved an AUC of 0.825 in the internal testing set and 0.817 in the external validation cohort.
The model demonstrates that a small set of clinically available variables can accurately predict overall survival, providing a practical tool for risk stratification and guiding follow‑up imaging and treatment decisions.
Bizonyítékszint: Klinikai vizsgálat. Formális klinikai vizsgálati eredmény.
Kapcsolódó jelek
- Correction: Primary cutaneous adenoid cystic carcinoma of the right forearm: a case report and dermoscopic features
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- Deciphering the potential pathogeny of rare tracheal adenoid cystic carcinoma by single‑cell RNA‑sequencing
- Adenoid cystic carcinoma (ACC)
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