An AI approach that uses GPT‑4o to analyze pelvic CT exams could help clinicians diagnose early‑stage ovarian cancer more accurately and consistently, according to a study published February 17 in the Annals of Surgical Oncology.
“[Our research found that] GPT-4o identifies the key CT features of ovarian cancer and achieves promising diagnostic accuracy with high-quality diagnostic evidence,” wrote a team led by Shimin Zhang, MD, of Shengjing Hospital of China Medical University in Liaoning, China.
Early detection of ovarian cancer can be a challenge, and more than half of cases are still diagnosed at metastatic stages, contributing to a five‑year survival rate of 31.4% compared with more than 90% when the disease is confined to the ovaries, the group explained. The gold standard for ovarian cancer diagnosis has long been surgical pathology, and clinicians rely heavily on imaging — particularly pelvic CT — to guide preoperative assessment.
But CT interpretation depends on the radiologist’s experience and can be affected by high interobserver variability. Zhang’s group explored the use of GPT-4o to improve CT’s ability to diagnose early ovarian cancer via a study that included 479 patients with pathologically confirmed benign or early‑stage malignant ovarian lesions. The researchers trained the algorithm to identify four CT features associated with malignancy — cyst wall and septum characteristics, nodular or papillary protrusions, density and enhancement patterns, and cystic versus solid composition — and to recognize ovarian lesions, report key CT features of ovarian lesions, and make a benign or malignant diagnosis based on these features.
They found the following:
- GPT-4o achieved diagnostic accuracies of 80.8%, 79.1%, and 93.3% in the three datasets, respectively (i.e., ovarian lesions, key CT features of ovarian lesions, and benign or malignant diagnoses).
- Clinician ratings for GPT‑4o’s reliability in detecting four key CT features were 4.2 for cyst wall and septum status; 4.2 for nodular or papillary protrusions; 4.3 for density and enhancement distribution; and 4.2 for cystic-solid characteristics (all scores out of 5).
- GPT-4o increased the accuracy of radiologist and gynecologic oncologist diagnoses by 1.96% and 10.5%, respectively.
The group noted that using GPT-4o with pelvic CT images improved the diagnostic performance of less experienced clinicians, reporting that the diagnostic accuracy of gynecologic oncologists with less than seven years of experience increased from 67.9% to 78.1% when they were assisted by the model.
“With further validation in diverse datasets, GPT-4o holds promise as a novel approach for early ovarian cancer detection, ultimately improving the current landscape of early-stage ovarian cancer management,” the authors concluded.
Access the full study here.