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Multiple prognostic prediction models for subglottic laryngeal cancer based on machine learning
1Department of Otorhinolaryngology Head and Neck Surgery, Zhongshan Hospital Xiamen University, Xiamen, China.
Background:
Subglottic laryngeal cancer is a relatively rare subtype of laryngeal cancer, and its limited case number makes prognostic prediction challenging. This study aimed to compare multiple prognostic models for subglottic laryngeal cancer and to determine whether incorporating data from other laryngeal subsites could improve predictive performance for this rare subtype.
Methods:
A total of 4,701 eligible laryngeal cancer cases were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. According to tumor location, cases were classified into LarynxSubglottic (n=75), LarynxGlottic (n=2,691), LarynxSupraglottic (n=1,514), and LarynxOther (n=421). Three combined subsets were further created: subset 1 (LarynxSubglottic + LarynxGlottic), subset 2 (LarynxSubglottic + LarynxSupraglottic), and subset 3 (LarynxSubglottic + LarynxOther). Four prognostic models were developed, including stepwise Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, random survival forest (RSF), and deep survival (DeepSurv). Model performance for subglottic cases was evaluated using the concordance index (C-index) and time-dependent areas under the curve (AUCs) for 5-year and 10-year survival.
Results:
For models trained on the complete cohort, the 5-year/10-year AUCs for subglottic cases were 0.748/0.676 for stepwise Cox, 0.666/0.677 for LASSO Cox, and 0.716/0.665 for RSF; the DeepSurv model achieved a C-index of 0.690. In subset 1, the corresponding values were 0.739/0.680, 0.663/0.671, and 0.716/0.626, with a DeepSurv C-index of 0.624. In subset 2, the values were 0.679/0.620, 0.698/0.756, and 0.713/0.792, with a DeepSurv C-index of 0.701. In subset 3, the values were 0.731/0.648, 0.671/0.694, and 0.725/0.722, with a DeepSurv C-index of 0.672. Overall, subset 2 showed the most favorable performance in LASSO Cox and DeepSurv, whereas the complete cohort performed best in stepwise Cox and subset 3 performed best in RSF.
Conclusions:
Multiple prognostic models showed potential utility for predicting outcomes in subglottic laryngeal cancer. Incorporating supraglottic cases into the training cohort may improve prognostic prediction for this rare subtype. Among the evaluated approaches, the stepwise Cox model demonstrated the most robust and stable overall performance under the present study conditions.
