Modeling adjusted for age and menopause statuses dependent on PET/CT scan for ovarian cancer diagnosis and staging

Abstract

Aims: The main objective of this observational study is to develop a linear regression model that incorporates age, menopausal status, and family history to predict the risk and severity of ovarian cancer. Methods: In early 2023, the King Hussein Medical Centre's gynaecological clinic began using PET/CT scanning and histopathological analysis to identify ovarian cancer cases. The data was then used to strategize interventions for each patient. The study aimed to assess the probability of ovarian cancer in female patients by analysing their age, menopausal onset, and family history. Patients were classified as pre-menopausal or post-menopausal, and PET/CT scan results were converted into FIGO classifications. Histopathological findings were analysed using ROC and binary logistic regression analyses. The study also used multiple linear regression to determine correlations and variations in the estimated Federation of Obstetrics and Gynaecology (FIGO) grade for females with suspected ovarian cancer. The research developed a pragmatic model to forecast ovarian cancer likelihood and severity levels. Results: The study examined 105 patients with suspected ovarian cancer at King Hussein Medical Centre between 2021 and mid-2023. Only 97 patients (92.38%) had matched FIGO-derived PET/CT scans with biopsy-based histopathological positivity. The optimal FIGO grade was 3.5, with a sensitivity of 77.2%, a specificity of 76.92%, a positive predictive value of 95.95%, a negative predictive value of 32.26%, an accuracy index of 77.14%, and a Youden index of 54.10%. Conclusion: A regression-based model was developed to triage the risk of ovarian cancer. This model enables us to early prioritise suspected females who should undergo PET/CT at the clinic level, with a high positive predictive value of over 90%.