Abstract
Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis. To clinically validate a metabolomics-based classification algorithm as a screening test for EC. This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019. The presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography-mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs. The study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430). In this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.
Highlights
Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in highincome countries and the sixth most frequent in women worldwide, with an estimated 380 000 new cases globally in 2018.1 Incidence is increasing, owing primarily to the increasing prevalence of risk factors, such as obesity, diabetes, metabolic syndrome, and others.[2,3,4] Sheikh et al[5] estimated an incidence rate of 42 cases per 100 000 individuals in 2030, with a 55% increase compared with 2010.Five-year survival rates of patients with EC are inversely associated with their International Federation of Gynecology and Obstetrics (FIGO) stage at diagnosis: from 85% at stage I to 25% at stage IV.[6]
Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples
Participant Characteristics According to the sample size calculation, 120 women were included in the training set: 50 (41.7%) with EC and 70 (58.3%) with no EC
Summary
Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in highincome countries and the sixth most frequent in women worldwide, with an estimated 380 000 new cases globally in 2018.1 Incidence is increasing, owing primarily to the increasing prevalence of risk factors, such as obesity, diabetes, metabolic syndrome, and others.[2,3,4] Sheikh et al[5] estimated an incidence rate of 42 cases per 100 000 individuals in 2030, with a 55% increase compared with 2010.Five-year survival rates of patients with EC are inversely associated with their International Federation of Gynecology and Obstetrics (FIGO) stage at diagnosis: from 85% at stage I to 25% at stage IV.[6]. Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in highincome countries and the sixth most frequent in women worldwide, with an estimated 380 000 new cases globally in 2018.1 Incidence is increasing, owing primarily to the increasing prevalence of risk factors, such as obesity, diabetes, metabolic syndrome, and others.[2,3,4] Sheikh et al[5] estimated an incidence rate of 42 cases per 100 000 individuals in 2030, with a 55% increase compared with 2010. It is difficult to microscopically evaluate cervical fluids because of the low abundance of endometrial malignant cells that can be detected, even in patients with advanced EC.[9] the acidic vaginal environment modifies the exfoliated endometrial cells to the extent that positive identification of tumor cells becomes difficult
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