Diagnostic performances of hysteroscopy in post-remission surveillance of patients treated conservatively for endometrial cancer and atypical hyperplasia: a cohort study
Hysteroscopy is commonly used for diagnosing benign endometrial conditions, but its diagnostic performance in malignancies post-treatment surveillance has not been evaluated. This study evaluated the correlation between hysteroscopic appearance and histological outcomes in patients in remission after conservative treatment for atypical hyperplasia (AH) or early-stage endometrial cancer (EC). Multicenter retrospective cohort study utilizing data from the French national register of fertility-sparing management for AH/EC (PREFERE database) from May 2015 to December 2020. The study included patients over 18 years with a prior diagnosis of AH or stage IA1 EC, who achieved remission following progestin-based fertility-sparing treatment. Hysteroscopy and endometrial biopsy were performed for post-remission surveillance. Hysteroscopic appearances were categorized, and the corresponding histopathological results were reviewed as the gold standard for detecting recurrence or remission. Non-suspicious findings (normal or atrophic) and suspicious findings (thickened, polypoid, polyp-like or atypical vessels presence) in hysteroscopy were evaluated for concordance with biopsy results. The study followed 47 patients in remission yielding in 121 hysteroscopy-biopsy pairings, of which 63 were non-suspicious. Diagnostic accuracy of hysteroscopy was 63 % (95 % CI: 54-71 %), with sensitivity of 71 % (95 % CI: 52-86 %) and specificity of 60 % (95 % CI: 49-70 %). The positive predictive value was 38 %, and the negative predictive value was 86 %. Non-suspicious hysteroscopic findings correlated better with normal histopathology, while suspicious findings were less predictive of recurrence. The findings highlight the necessity of combining hysteroscopic findings with biopsy for reliable post-treatment monitoring in fertility-sparing management of AH/EC. Hysteroscopy alone lacks sufficient accuracy which aligns with current guidelines and underscores the importance of follow-up protocols to detect recurrence in this high-risk population. Integrating advanced imaging modalities or diagnostic tools based on artificial intelligence may enhance the accuracy of recurrence detection in this patient population.