Prognostic model for patients with oral cavity cancer using a novel multi-parameter index including radiation response of patient-derived tumoroids

Objective: No reliable model has been established to accurately predict the prognosis of patients with oral cavity cancer (OCC) following surgery and adjuvant radiation therapy. This study aimed to develop a tumoroid-based novel prognostic model for OCC to overcome the limitations of conventional algorithms relying on the area under the curve (AUC).Methods: A database of radiation response and cell growth rates was established using tumoroids derived from Korean OCC patients (N=16). Leveraging the AUC data, along with the growth rate of each tumoroid and TNM stages, we developed a multi-parameter index (MPI)-based prognostic model. Then, the prognostic model was evaluated for the prediction of the 1-year recurrence of patients following adjuvant radiation therapy.Results: The MPI-based algorithm that predicted the 1-year recurrence was significantly more accurate than those based on the AUC or growth rate of tumoroids, with a sensitivity of 80.0%. In addition, the MPI-classified good and poor prognostic groups showed significantly different 1-year recurrence-free survival (p = 0.023), while the AUC model showed no difference (p = 0.952). When the MPI-based prediction algorithm was applied to a small cohort of patients (N=4) from different ethnic backgrounds, two patients who remained recurrence-free over one year were placed in the good prognostic group, while the other two patients who experienced recurrence within a year were placed in the poor prognostic group.Conclusion: Despite the small sample size, these preliminary findings suggest the feasibility of an MPI-based prognostic model that predicts the 1-year recurrence in OCC patients from diverse ethnicities.