Mean platelet volume as a potential biomarker for survival outcomes in ovarian clear cell carcinoma

Objective: This study aimed to explore the prognostic value of mean platelet volume (MPV) in patients with ovarian clear cell carcinoma (OCCC) and evaluate the predictive performance of a random forest model incorporating MPV and other key clinicopathological factors.

Methods: A total of 204 patients with OCCC treated between January 2004 and December 2019 were retrospectively analyzed. Clinicopathological characteristics and preoperative laboratory data were collected, and survival outcomes were evaluated using the Kaplan-Meier method and Cox proportional hazards models. An optimal MPV cutoff was determined by receiver operating characteristic (ROC) curve analysis. A random forest model was then constructed using the identified independent prognostic factors, and its predictive performance was evaluated.

Results: The ROC analysis identified 9.3 fL as the MPV cutoff value for predicting 2-year survival. The MPV-low group had lower 5-year overall survival and progression-free survival rates than the MPV-high group (p = 0.003 and p = 0.034, respectively). High MPV emerged as an independent prognostic factor (p = 0.006). The random forest model, incorporating the FIGO stage, residual tumors, peritoneal cytology, and MPV, demonstrated robust predictive performance (area under the curve: 0.905).

Conclusion: MPV is a promising prognostic indicator in OCCC. Lower MPV correlated with worse survival rates, advocating its potential utility in refining patient management strategies. The commendable predictive performance of the random forest model, integrating MPV and other significant prognostic factors, suggests a pathway toward enhanced survival prediction, thereby warranting further research.