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    Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis
    (Wiley, 2023) Shazly, Sherif A.; Coronado, Pluvio J.; Yilmaz, Ercan; Melekoglu, Rauf; Sahin, Hanifi; Giannella, Luca; Ciavattini, Andrea
    ObjectiveTo establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. MethodsA multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). ResultsOf 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. ConclusionThe Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.
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    Placenta accreta risk-antepartum score in predicting clinical outcomes of placenta accreta spectrum: A multicenter validation study
    (Wiley, 2022) Shazly, Sherif A.; Anan, Mohamed A.; Makukhina, Tatiana B.; Melekoglu, Rauf; Ahmed, Farhat Ul A.; Pinto, Pedro, V; Takahashi, Hironori
    Objective To validate the use of placenta accreta risk-antepartum (PAR-A) score as a predictive tool of clinical outcomes of placenta accreta spectrum (PAS). Methods This is a prospective study, conducted in six PAS specialized centers in six different countries. The study was conducted between October 1, 2020 and March 31, 2021. Women who were provisionally diagnosed with PAS during pregnancy were considered eligible. A machine-learning-based PAR-A score was calculated. Diagnostic performance of the PAR-A score was evaluated using a receiver operating characteristic curve, for perioperative massive blood loss and admission to intensive care unit (ClinicalTrials.gov identifier NCT04525001). Results Of 97 eligible women, 86 were included. PAS-associated massive blood loss occurred in 10 patients (11.63%). Median PAR-A scores of massive blood loss in the current cohort were 8.9 (interquartile range 6.9-14.1). In predicting massive blood loss, the area under the curve of PAR-A scores was 0.85 (95% confidence interval [CI] 0.74-0.95), which was not significantly different from the original cohort (P = 0.2). PAR-A score prediction of intensive care unit admission was slightly higher compared with the original cohort (0.88, 95% CI 0.81-0.95; P = 0.06). Conclusion PAR-A score is a novel scoring system of PAS outcomes, which showed external validity based on current data.
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    Prediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter study
    (Taylor & Francis Ltd, 2022) Shazly, Sherif A.; Hortu, Ismet; Shih, Jin-Chung; Melekoglu, Rauf; Fan, Shangrong; Ahmed, Farhat ul Ain; Karaman, Erbil
    Introduction Placenta accreta spectrum is a major obstetric disorder that is associated with significant morbidity and mortality. The objective of this study is to establish a prediction model of clinical outcomes in these women Materials and methods PAS-ID is an international multicenter study that comprises 11 centers from 9 countries. Women who were diagnosed with PAS and were managed in the recruiting centers between 1 January 2010 and 31 December 2019 were included. Data were reanalyzed using machine learning (ML) models, and 2 models were created to predict outcomes using antepartum and perioperative features. ML model was conducted using python(R) programing language. The primary outcome was massive PAS-associated perioperative blood loss (intraoperative blood loss >= 2500 ml, triggering massive transfusion protocol, or complicated by disseminated intravascular coagulopathy). Other outcomes include prolonged hospitalization >7 days and admission to the intensive care unit (ICU). Results 727 women with PAS were included. The area under curve (AUC) for ML antepartum prediction model was 0.84, 0.81, and 0.82 for massive blood loss, prolonged hospitalization, and admission to ICU, respectively. Significant contributors to this model were parity, placental site, method of diagnosis, and antepartum hemoglobin. Combining baseline and perioperative variables, the ML model performed at 0.86, 0.90, and 0.86 for study outcomes, respectively. Ethnicity, pelvic invasion, and uterine incision were the most predictive factors in this model. Discussion ML models can be used to calculate the individualized risk of morbidity in women with PAS. Model-based risk assessment facilitates a priori delineation of management.
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    Prediction of success of uterus-preserving management in women with placenta accreta spectrum (CON-PAS score): A multicenter international study
    (Wiley, 2021) Shazly, Sherif A.; Hortu, Ismet; Shih, Jin-Chung; Melekoglu, Rauf; Fan, Shangrong; ul Ain Ahmed, Farhat; Karaman, Erbil
    Objective To create a model for prediction of success of uterine-preserving procedures in women with placenta accreta spectrum (PAS). Methods PAS-ID is a multicenter study that included 11 centers from 9 countries. Women with PAS, who were managed between January 1, 2010 and December 31, 2019, were retrospectively included. Data were split into model development and validation cohorts, and a prediction model was created using logistic regression. Main outcome was success of uterine preservation. Results Out of 797 women with PAS, 587 were eligible. Uterus-preserving procedures were successful in 469 patients (79.9%). Number of previous cesarean sections (CS) was inversely associated with management success (adjusted odds ratio [aOR] 0.02, 95% confidence interval [CI] 0.001-3.63 with five previous CS). Other variables were complete placental invasion (aOR 0.14, 95% CI 0.05-0.43), type of CS incision (aOR 0.04, 95% CI 0.01-0.25 for classical incision), compression sutures (aOR 2.48, 95% CI 1.00-6.16), accreta type (aOR 3.76, 95% CI 1.13-12.53), incising away from placenta (aOR 5.09, 95% CI 1.52-16.97), and uterine resection (aOR 102.57, 95% CI 3.97-2652.74). Conclusion The present study provides a prediction model for success of uterine preservation, which may assist preoperative and intraoperative decisions, and promote incorporation of uterine preservation procedures in comprehensive PAS protocols.