A pragmatic model challenging the primacy of biopsy in the initial staging and prognostication of ovarian cancer

Invasive histopathological biopsy has historically been the preferred method for ovarian cancer diagnosis and staging. Despite its certainty, a biopsy has dangers and delays. This study investigates the viability of integrating clinical data with non-invasive PET/CT imaging to construct a prediction model for early risk assessment and FIGO staging. For project success, this will be done. 

Methods: techniques 111 suspected ovarian cancer patients were retrospectively analysed in this study. Clinical, demographic, and PET/CT imaging data were collected. Histology and FIGO staging, assessed independently using PET/CT criteria, were compared. To establish method superiority, this comparison was made. Multiple linear regression was employed to determine FIGO stage, whereas binary logistic regression predicted malignancy.

PET/CT FIGO staging agreed 78% with histological staging. Data determined this. The multivariate predictive model has 91% sensitivity, 83% specificity, and 87% accuracy. Age above 50, post-menopausal status, and PET/CT hypermetabolic lesions were statistically significant indicators of malignancy and advanced stage.

A PET/CT and clinical data model may predict ovarian cancer risk and stage. The necessity for a biopsy as the first and sole step in assessment is questioned. When resources are few or biopsy risk is high, this method provides quick, non-invasive first triage. This is beneficial when biopsy is needed often.