Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 14 years were used to develop models with a training set of 2925 patients and a validation set of 731 patients. Our models revealed the top postoperative predictors of residual disease were the initial presence of gross abdominal disease on the diaphragm, disease located on the bowel mesentery, located on the bowel serosa, and disease located within the adjacent pelvis prior to resection. There were no statistically significant differences in performances of the top three models. Extreme gradient Boosting, Random Forest, and Logistic Regression models had comparable AUC ROC (0.90) and accuracy metrics (87–88%). Using these models, physicians can identify gynecologic cancer patients post-hysterectomy that may benefit from additional treatment. For patients at high risk for disease recurrence despite adequate surgical intervention, machine learning models may lay the basis for potential prospective trials with prophylactic/adjuvant therapy for non-clinically evident residual disease, particularly in under-resourced settings.

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PURPOSE Sensitive patient data cannot be easily shared/analyzed, severely limiting the innovative progress of research, specifically for marginalized/under-represented populations. Existing methods of deidentification are subject to data breaches. The objective of this study was to develop a neural network capable of generating a synthetic version of data for patients with novel postoperative metastatic cancer. METHODS We analyzed a metastatic cancer patient cohort of 167,474 patients obtained from the National Surgical Quality Improvement Program. Twenty-seven clinical features were analyzed. We created a volume-matched synthetic cohort of 167,474 patients and a reduced-size synthetic cohort of 5,000 patients. The volume-matched and reduced-size synthetic cohorts were compared against the ground truth data to analyze differences in principal component distribution, underlying statistical properties/associations, intervariable correlations, and machine learning classifier performance when developed on the synthetic data. RESULTS Among 167,474 patients with metastatic cancer in the original data, 50,669 (30.3%) died within 30 days of their index surgery. Our model was able to accurately capture underlying statistical properties, principal components, and intervariable correlations within the ground truth data, yielding an accuracy of 93.2% with a loss of 0.21%, and develop synthetic data capable of training accurate machine learning classifiers. The reduced-size synthetic data accurately replicated all categorical variables and every continuous variable with statistically similar records ( P > .05), with the sole exception of preoperative albumin ( P < .05). The volume-matched synthetic data frame was able to accurately replicate all categorical variables ( P > .05). CONCLUSION This described methodology can be applied to any structured medical data from any setting, significantly expedite scientific analysis/innovation, and be used to develop improved predictive classifiers with boosted tree-based algorithms, serving as the potential new gold standard of medical data sharing and data augmentation.