External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)

Abstract

IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning–based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77–0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79–0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1–2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model’s laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

“…The median number of cases is 342 (IQR: 200~661), and the median number of cases in training sets is 300 (IQR: 154~533). Only 29 studies ( 19 – 21 , 23 , 25 , 26 , 29 – 33 , 36 – 38 , 40 , 43 , 45 , 47 – 50 , 52 , 54 , 55 , 58 , 60 – 63 ) had independent validation sets. Among them, 11 studies ( 21 , 30 , 31 , 33 , 36 , 45 , 55 , 61 – 63 , 65 ) adopted the method of external validation, and the remaining studies carried out internal validation by random sampling.…”

“…Only 29 studies ( 19 – 21 , 23 , 25 , 26 , 29 – 33 , 36 – 38 , 40 , 43 , 45 , 47 – 50 , 52 , 54 , 55 , 58 , 60 – 63 ) had independent validation sets. Among them, 11 studies ( 21 , 30 , 31 , 33 , 36 , 45 , 55 , 61 – 63 , 65 ) adopted the method of external validation, and the remaining studies carried out internal validation by random sampling. Nine studies ( 20 – 22 , 31 , 32 , 35 , 45 , 52 ) did not clearly describe the variable screening method and mainly adopted single-factor and multi-factor logistic regression.…”