RESUMO
OBJECTIVE: The present study aimed to assess long-term follow-up outcomes in women with in situ/microinvasive adenocarcinoma (AC) of the uterine cervix treated conservatively. METHODS: Retrospective multi-institutional study including women with early glandular lesions and 5-year follow-up undergoing fertility-sparing treatment. Independent variables associated with recurrence were evaluated. Logistic regression analysis and Kaplan-Meier survival analysis with Logrank test were performed. RESULTS: Of 269 women diagnosed with in situ/microinvasive AC, 127 participants underwent conservative treatment. During follow-up, recurrences were found in nine women (7.1%). The only factor associated with recurrence during follow-up was positive high-risk Human Papillomavirus (hr-HPV) testing (odds ratio 6.21, confidence interval 1.47-26.08, p = 0.012). HPV positivity in follow-up showed a recurrence rate of 21.7% against 3.8% in patients who were HPV-negative (p = 0.002, Logrank test). Among women with negative high-risk HPV tests in follow-up, recurrences occurred in 20.0% of non-usual-type histology vs. 2.1% of usual-type cases (p = 0.005). CONCLUSION: HPV testing in follow-up is of pivotal importance in women with early glandular lesions undergoing conservative treatment, given its recurrence predictive value. However, women who are high-risk HPV-negative in follow-up with non-usual-type histopathology may represent a sub-population at increased risk of recurrences. Further studies should confirm these findings.
RESUMO
BACKGROUND: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study aimed to assess the performance of various models based on patient characteristics in predicting EC in women with AEH. METHODS: This is a retrospective multi-institutional study including women with AEH undergoing definitive surgery. The women were divided according to the final histology (EC vs. no-EC). The available cases were divided into a training and validation set. Using k-fold cross-validation, we built many predictive models, including regressions and artificial neural networks (ANN). RESULTS: A total of 193/629 women (30.7%) showed EC at hysterectomy. A total of 26/193 (13.4%) women showed high-risk EC. Regression and ANN models showed a prediction performance with a mean area under the curve of 0.65 and 0.75 on the validation set, respectively. Among the best prediction models, the most recurrent patient characteristics were age, body mass index, Lynch syndrome, diabetes, and previous breast cancer. None of these independent variables showed associations with high-risk diseases in women with EC. CONCLUSIONS: Patient characteristics did not show satisfactory performance in predicting EC in AEH. Risk stratification in AEH based mainly on patient characteristics may be clinically unsuitable.