Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.

Ovarian cancer represents one of the most lethal gynecologic malignancies, characterized by low early detection rates and challenging prognostic assessment. Conventional diagnostic modalities demonstrate limited sensitivity and specificity for early-stage disease identification. Recent research has begun to explore causal inference methodologies as complementary approaches that may enhance diagnostic precision and prognostic capability. This systematic review evaluates the current state and future prospects of causal inference methodologies in enhancing ovarian cancer diagnosis and prognosis. We performed a comprehensive systematic review focusing on causal inference methodologies applied to ovarian cancer research. The analysis encompassed biomarker identification, pathogenic mechanism elucidation, and multimodal data integration. Additionally, we analyzed the synergistic combination of causal inference with machine learning approaches across genomic, transcriptomic, proteomic, and imaging datasets. Causal inference methods have shown effectiveness in identifying crucial biomarkers and revealing underlying pathogenic mechanisms of ovarian cancer. The integration of machine learning with causal inference has enhanced model interpretability, clinical applicability, and diagnostic-prognostic accuracy. These approaches have achieved improved predictions of disease progression and optimization of treatment strategies by leveraging clinical, genetic, and imaging data. Causal inference shows considerable potential in advancing precision medicine for ovarian cancer, offering robust frameworks for addressing confounding factors and establishing causal relationships. As these methodologies evolve and data volumes expand, their application may become increasingly valuable in oncology practice.