JCO precision oncology · 2026
Machine Learning-Driven Risk Stratification and Adjuvant Treatment Guidance in Oral Cavity Cancer.
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Costantino Andrea, Tsur Nir, Uralov Daniel, Zhu Jianbin, Alamoudi Uthman, Spriano Giuseppe, Magnuson J Scott
Аннотация
PURPOSE: To develop and validate machine learning (ML) models for postoperative risk stratification in oral cavity squamous cell carcinoma (OCSCC) and to examine whether ML-derived risk groups modify the association between adjuvant therapy and overall survival (OS).
METHODS: Using the National Cancer Database, we identified adults with invasive OCSCC treated with primary surgery. The surgery-alone cohort (n = 18,543) was split 70/30 for training/testing to develop DeepSurv, Neural Multi-Task Logistic Regression (NMTLR), and Random Survival Forest (RSF) models. Risk scores were generated for the full cohort (n = 35,625) and converted to low, intermediate, and high groups. Within groups, treatment effects of adjuvant radiotherapy (RT) and chemoradiotherapy (CRT) were estimated using multivariable Cox models.
RESULTS: The best performance was achieved by DeepSurv (C-index 0.73), with similar discrimination for NMTLR/RSF (C-index 0.71-0.72). For DeepSurv, the full cohort was partitioned into low- (50.0%), intermediate- (32.0%), and high-risk (18.0%) groups with distinct 5-year OS rates: 77.6%, 53.0%, and 29.3%, respectively. In the low-risk group, adjuvant RT (adjusted hazard ratios [aHR], 0.94 [95% CI, 0.87 to 1.02]) and CRT (aHR, 1.03 [95% CI, 0.91 to 1.17]) did not improve OS. In the intermediate-risk group, OS improved with RT (aHR, 0.61 [95% CI, 0.57 to 0.65]) and CRT (aHR, 0.56 [95% CI, 0.52 to 0.61]). In the high-risk group, both adjuvant RT (aHR, 0.47 [95% CI, 0.43 to 0.51]) and CRT (aHR, 0.39 [95% CI, 0.36 to 0.41]) were associated with improved OS compared with surgery alone. CRT was associated with a modest benefit compared with RT. NMTLR and RSF yielded concordant patterns. Top features included pT4a stage, age ≥70 years, and extranodal extension.
CONCLUSION: ML-derived risk stratification identifies patients with OCSCC most likely to benefit from adjuvant therapy, supporting intensification for intermediate-/high-risk patients and potential deintensification for low-risk patients. External prospective validation is warranted to enable clinical implementation.
METHODS: Using the National Cancer Database, we identified adults with invasive OCSCC treated with primary surgery. The surgery-alone cohort (n = 18,543) was split 70/30 for training/testing to develop DeepSurv, Neural Multi-Task Logistic Regression (NMTLR), and Random Survival Forest (RSF) models. Risk scores were generated for the full cohort (n = 35,625) and converted to low, intermediate, and high groups. Within groups, treatment effects of adjuvant radiotherapy (RT) and chemoradiotherapy (CRT) were estimated using multivariable Cox models.
RESULTS: The best performance was achieved by DeepSurv (C-index 0.73), with similar discrimination for NMTLR/RSF (C-index 0.71-0.72). For DeepSurv, the full cohort was partitioned into low- (50.0%), intermediate- (32.0%), and high-risk (18.0%) groups with distinct 5-year OS rates: 77.6%, 53.0%, and 29.3%, respectively. In the low-risk group, adjuvant RT (adjusted hazard ratios [aHR], 0.94 [95% CI, 0.87 to 1.02]) and CRT (aHR, 1.03 [95% CI, 0.91 to 1.17]) did not improve OS. In the intermediate-risk group, OS improved with RT (aHR, 0.61 [95% CI, 0.57 to 0.65]) and CRT (aHR, 0.56 [95% CI, 0.52 to 0.61]). In the high-risk group, both adjuvant RT (aHR, 0.47 [95% CI, 0.43 to 0.51]) and CRT (aHR, 0.39 [95% CI, 0.36 to 0.41]) were associated with improved OS compared with surgery alone. CRT was associated with a modest benefit compared with RT. NMTLR and RSF yielded concordant patterns. Top features included pT4a stage, age ≥70 years, and extranodal extension.
CONCLUSION: ML-derived risk stratification identifies patients with OCSCC most likely to benefit from adjuvant therapy, supporting intensification for intermediate-/high-risk patients and potential deintensification for low-risk patients. External prospective validation is warranted to enable clinical implementation.