Abstract
Some aspects of endometrial cancer (EC) preoperative work-up are still controversial, and debatable are the roles played by lymphadenectomy and radical surgery. Proper preoperative EC staging can help design a tailored surgical treatment, and this study aims to propose a new algorithm able to predict extrauterine disease diffusion. 293 EC patients were consecutively enrolled, and age, BMI, children’s number, menopausal status, contraception, hormone replacement therapy, hypertension, histological grading, clinical stage, and serum HE4 and CA125 values were preoperatively evaluated. In order to identify before surgery the most important variables able to classify EC patients based on FIGO stage, we adopted a new statistical approach consisting of two-steps: 1) Random Forest with its relative variable importance; 2) a novel algorithm able to select the most representative Regression Tree (RERT) from an ensemble method. RERT, built on the above mentioned variables, provided a sensitivity, specificity, NPV and PPV of 90%, 76%, 94% and 65% respectively, in predicting FIGO stage > I. Notably, RERT outperformed the prediction ability of HE4, CA125, Logistic Regression and single cross-validated Regression Tree. Such algorithm has great potential, since it better identifies the true early-stage patients, thus providing concrete support in the decisional process about therapeutic options to be performed.
Highlights
While early stage type I EC is usually treated through extra-fascial hysterectomy with bilateral salpingo-oophorectomy, the optimal surgical management for more advanced stage type I disease is not yet univocally defined
The present study aims to preoperatively predict the surgical/pathological stage of the disease in a large cohort of EC patients, using a novel statistical approach, called REpresentative Regression Tree (RERT), combined with Random Forest[19] and its relative variable importance, in order to identify the main drivers (HE4 and CA125 serum biomarkers, together with other clinical and pathological variables) impacting on FIGO Stage
Even though 10 patients with FIGO stage ≥ III were not subjected to lymphadenectomy, they were included anyway in the study because lymph node status showed no impact on their advanced FIGO stage
Summary
While early stage type I EC is usually treated through extra-fascial hysterectomy with bilateral salpingo-oophorectomy, the optimal surgical management for more advanced stage type I disease is not yet univocally defined. Conventional preoperative assessment, based on imaging techniques (Magnetic Resonance Imaging, Computed Tomography, Transvaginal Sonography) and endometrial biopsy, are not able to systematically identify the extrauterine diffusion of disease and to correctly stratify between “truly early stage” vs “truly advanced stage” patients[8]. Elevated preoperative sCA125 has been usually associated with advanced FIGO stage and lymph node metastasis, even though the sensitivity in predicting extrauterine disease is controversial[9,10,11]. SHE4 was recently found significantly associated with deeper myometrial invasion, higher histological grade, lymph node metastasis, and advanced FIGO stage[12,13,14], suggesting its possible application in the preoperative assessment of EC patients
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