Machine learning-based prediction of clinical outcomes in cervical cancer using routine hematological indices: development and web implementation.

Background: Cervical cancer prognosis critically depends on tumor invasiveness, yet existing predictive tools lack accessibility and generalizability. We aimed to develop predictive models using comprehensive hematological profiling of routine tests to assess invasiveness and survival, improving clinical decision-making. Methods: We conducted a retrospective analysis of 512 cervical cancer patients who underwent radical surgery. A panel of hematological indices was evaluated, including inflammatory markers, coagulation parameters, and metabolic indicators. Machine learning (ML) algorithms innovatively integrated with traditional regression were employed for feature selection and model development. Models were internally validated by bootstrap methods for discrimination (AUC/C-index) and calibration. Clinical utility was assessed by decision curve analysis (DCA). Web-based Shiny applications of these models were deployed. Results: Using routine hematological indices selected from ML-based methods, we identified the optimal variable set for each clinical outcome prediction model based on C-index comparisons. The multivariable analyses of these variables identified hematological parameters associated with cervical cancer progression and prognosis. TG, HGB, Eosinophil count, TCLR, and NAR acted as protective factors, while LDL, WBC, FAR, DDI, FLR, ENLR, SII and platelet count were risk factors linked to advanced disease features. In addition, Tbil and DDI were consistent risk factors for both recurrence-free survival (RFS) and overall survival (OS). The models assessed invasiveness risk and survival risk in two critical periods: pre-surgery and post-surgery. The AUC values for predicting locally advanced cervical cancer (LACC), uterine body invasion (UBI), lymph node positivity (LNP), adjuvant therapy (ADT), parauterine invasion (PUI), and vaginal invasion (VI) were 0.714, 0.781, 0.781, 0.719, 0.756, and 0.700, respectively. For OS, the pre-surgery and post-surgery models achieved C-index of 0.875 and 0.906, while the RFS models yielded 0.790 and 0.863, respectively. All models showed AUC ≥ 0.7, strong calibration, and positive net benefit on DCA. Interactive web tools were implemented based on these models. Conclusions: Comprehensive hematological profiling enables accurate prediction of cervical cancer invasiveness and survival during different decision-making periods. Our ML-enhanced, web-implemented models can enhance risk stratification and clinical decisions, particularly in resource-limited settings.