Prostate cancer progression is inherently heterogeneous, driven by complex interactions among tumor biology, patient-specific factors, and treatment response. Existing deterministic models inadequately capture this variability, limiting their ability to represent the stochastic nature of disease evolution and to support reliable prediction in clinical settings. This study introduces a probabilistic framework for modeling prostate cancer progression based on the Fokker–Planck equation, which governs the temporal evolution of the probability density of a latent disease state. The latent state, associated with tumor burden and prostate-specific antigen (PSA) dynamics, evolves under the combined influence of deterministic and stochastic processes. The drift term characterizes tumor growth and therapeutic effects, while the diffusion term captures intrinsic biological variability arising from genetic mutations, microenvironmental conditions, and inter-patient heterogeneity. Numerical simulations demonstrate the evolution of disease-state distributions under varying treatment scenarios, highlighting the ability of the proposed framework to capture a spectrum of plausible trajectories rather than a single deterministic outcome. This enables a more realistic representation of disease progression and treatment response at both individual and population levels. The proposed approach provides a principled foundation for integrating stochastic tumor dynamics with clinical biomarkers and therapeutic interventions. By moving beyond deterministic assumptions, it supports the development of predictive, patient-specific models and advances the application of probabilistic reasoning in oncology and health informatics.
Prostate cancer progression is a complex and heterogeneous process that cannot be fully captured by deterministic models or by reliance on a single biomarker such as prostate-specific antigen (PSA). While PSA is widely used in clinical practice, it provides an incomplete and sometimes misleading representation of the underlying tumor dynamics, particularly in cases of low PSA but significant disease burden or during treatment response. In this study, we utilize anonymous clinical data from prostate cancer patients and propose a stochastic modeling framework to characterize the temporal evolution of prostate cancer as a complex process, incorporating both deterministic biological mechanisms and stochastic variability across patients. The proposed model introduces a latent disease state representing tumor burden, which evolves according to drift and diffusion components reflecting tumor growth, treatment effects, and intrinsic biological uncertainty. In addition to PSA, key clinical variables such as Gleason grade group, disease stage, and treatment exposure are integrated into the model to enhance its clinical interpretability and predictive capability. Numerical simulations demonstrate that the stochastic framework captures clinically meaningful behaviors, including heterogeneous progression trajectories, treatment-induced declines in PSA, and divergence between observed PSA levels and true disease burden. Unlike traditional survival or regression-based approaches, the model provides a full probability distribution of disease states over time, allowing for uncertainty quantification and personalized risk assessment. The results suggest that incorporating latent-state stochastic dynamics can significantly improve the understanding and prediction of prostate cancer progression, offering a foundation for next-generation decision-support systems in precision oncology.
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death, and anti-silencing function 1B histone chaperone (ASF1B) has been implicated in several cancers. This study aimed to investigate the role and molecular mechanism of ASF1B in HCC. ASF1B expression was analyzed using the TIMER2, GEO, Oncomine, and GEPIA2 databases, as well as western blotting. Cell viability, cell cycle distribution, and apoptosis were assessed via CCK-8 assay and flow cytometry, respectively. Survival analysis and immune infiltration analysis were performed using the TIMER2 database, and enrichment analysis was conducted via Metascape. Results showed that ASF1B expression was significantly higher in HCC tissues than in normal tissues, and high ASF1B expression predicted poor prognosis and was associated with higher tumor stage. Knockdown of ASF1B by siRNA significantly reduced cell viability, promoted apoptosis, and induced G1 phase cell cycle arrest in SNU-423 cells. Moreover, ASF1B expression was positively correlated with the infiltration levels of immune cells and tumor microenvironment signature cells, particularly functional T cells. Enrichment analysis further indicated that ASF1B may contribute to HCC progression through mechanisms involving cell cycle, cell division and differentiation, and DNA replication and repair. Collectively, these findings suggest that ASF1B overexpression predicts poor prognosis and increased immune infiltration in HCC, highlighting ASF1B as a potential therapeutic target for this malignancy.
This study integrates Kaplan–Meier survival analysis with the Stochastic and Augmented Interpretable Health Analytics (SAIHA) framework to model long-term survival in pancreatic cancer, a malignancy characterized by late diagnosis, rapid progression, and poor prognosis. The Kaplan–Meier estimator was first employed to nonparametrically characterize empirical survival probabilities across the observed follow-up period, capturing censoring patterns and short-term mortality dynamics without imposing distributional assumptions. This step provided a transparent baseline representation of survival up to approximately four years post-diagnosis, where empirical data density remains sufficient for reliable estimation. To address the limitations of traditional Kaplan–Meier analysis in extrapolating beyond observed follow-up, the SAIHA framework was then applied using a Weibull survival model to propagate uncertainty, incorporate population heterogeneity, and generate probabilistic survival projections into the long-term horizon. The Weibull distribution was selected for its flexibility in modeling monotonic hazard functions commonly observed in aggressive cancers and for its interpretability within clinical contexts. Parameter uncertainty was explicitly modeled to reflect variability in disease progression and treatment response across patients. The combined model predicts a pronounced decline in survival beyond year four, with the most likely five-year survival probability estimated near 3% and a median six-year survival approaching 1.5%. These projections align with known epidemiological patterns of pancreatic cancer and underscore the persistent lethality of the disease despite advances in therapy. Importantly, the SAIHA framework provides full survival distributions rather than point estimates, enabling clinicians and researchers to assess uncertainty bounds and tail risks associated with long-term outcomes. Overall, the integrated Kaplan–Meier–SAIHA approach extends classical survival analysis by combining empirical rigor with stochastic, distribution-aware forecasting. This methodology offers a robust and interpretable framework for high-risk clinical prediction, supporting more informed decision-making in oncology research, population health modeling, and precision medicine applications.
Background: Cervical cancer remains a major public health concern in low- and middle-income countries. Screening uptake in Cameroon is far below the World Health Organization’s elimination targets. This study assessed socio-demographic, economic, and informational determinants of low cervical cancer screening uptake among sexually active women in an urban district of Yaoundé. Methods: A cross-sectional study was conducted from September to October 2023 in the Biyem-Assi Health District. A convenience sample of 250 sexually active women aged 25–59 years completed a pre-tested structured questionnaire. Logistic regression was used to identify factors associated with non-participation. Significance was set at p < 0.05. Results: Overall, 89.0% of participants had never been screened. Independent predictors of non-participation were being single (AOR 5.79; 95% CI 3.60–9.45), lack of awareness of screening centers (AOR 5.02; 95% CI 1.24–20.29), no health insurance (AOR 3.91; 95% CI 1.70–8.98), poor knowledge of cervical cancer (AOR 3.16; 95% CI 1.12–8.94), unemployment (AOR 2.16; 95% CI 1.18-4.00), and having ≤1 child (AOR 1.94; 95% CI 1.21-3.12). Conclusion: Cervical cancer screening uptake is critically low in this urban population. The main barriers relate to socioeconomic vulnerability and lack of specific information on where screening services are offered. Improving service visibility, reducing costs, and integrating screening into routine health services may help increase uptake, although further research is required to evaluate feasibility and impact.
