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- 저자 : Stefano Ferla (검색결과 2 건)
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Treatment modalities for recurrent high-grade vaginal intraepithelial neoplasia
Giorgio Bogani,Antonino Ditto,Stefano Ferla,Biagio Paolini,Claudia Lombardo,Domenica Lorusso,Francesco Raspagliesi 대한부인종양학회 2019 Journal of Gynecologic Oncology Vol.30 No.2
Objective: We have investigated outcomes of women presenting with recurrent high-grade vaginal intra-epithelial neoplasia. Methods: Data of consecutive women diagnosed with recurrent high-grade vaginal intraepithelial neoplasia after primary treatment(s) were retrieved. Risk of developing new recurrence over the time was assessed using Kaplan-Meier and Cox models. Results: Data of 117 women were available for the analysis. At primary diagnosis, 41 (35%), 4 (3.4%) and 72 (61.6%) patients had had laser, pure surgical and medical treatments, respectively. Secondary treatments included: laser ablation and medical treatment in 95 (81.2%) and 22 (18.8%) cases, respectively. After a mean (standard deviation) follow-up of 72.3 (±39.5) months, 37 (31.6%) out of the entire cohort of 117 patients developed a second recurrence. Median time to recurrence was 20 (range, 5–42) months. Patients with recurrent high-grade vaginal intra-epithelial neoplasia undergoing medical treatments were at higher risk of developing a second recurrence in comparison to women having laser treatment (p=0.013, log-rank test). After we corrected our results for type of treatment used for recurrent disease, we observed that the execution of primary laser treatment was independently associated with a lower risk of developing new recurrences (hazard ratio [HR]=0.46; 95% confidence interval [CI]=0.21–0.99; p=0.050). The other variable that is independently associated with a new recurrence is the persistent infection from HPV16 or 18 (HR=3.87; 95% CI=1.15–13.0; p=0.028). Conclusion: Patients with recurrent high-grade vaginal intra-epithelial neoplasia are at high risk of developing new recurrences. Our data underline that the choice of primary treatment might have an impact of further outcomes.
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Giorgio Bogani,Diego Rossetti,Antonino Ditto,Fabio Martinelli,Valentina Chiappa,Lavinia Mosca,Umberto Leone Roberti Maggiore,Stefano Ferla,Domenica Lorusso,Francesco Raspagliesi 대한부인종양학회 2018 Journal of Gynecologic Oncology Vol.29 No.5
Objective: Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival. Methods: This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival. ANN simulates a biological neuronal system. Like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon. Results: Overall, 82.9% of patients had CC at the time of SCS. Using ANN, we observed that the 3 main factors driving the ability of achieve CC included: disease-free interval (DFI) (importance: 0.231), retroperitoneal recurrence (importance: 0.178), residual disease at primary surgical treatment (importance: 0.138), and International Federation of Gynecology and Obstetrics (FIGO) stage at presentation (importance: 0.088). Looking at connections between different covariates and overall survival (OS), we observed that DFI is the most important variable influencing OS (importance: 0.306). Other important variables included: CC (importance: 0.217), and FIGO stage at presentation (importance: 0.100). Conclusion: According to our results, DFI should be considered as the most important factor predicting both CC and OS. Further studies are needed to estimate the clinical utility of AI in providing help in decision making process.
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