Dataset Information

Development and validation of circulating CA125 prediction models in postmenopausal women.

ABSTRACT:

Background

Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker.

Methods

We developed and validated linear and dichotomous (?35?U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n?=?26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n?=?861), the Nurses' Health Studies (NHS/NHSII, n?=?81), and the New England Case Control Study (NEC, n?=?923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC.

Result

The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r?=?0.18) and external validation datasets (r?=?0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset.

Conclusions

The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.

SUBMITTER: Sasamoto N

PROVIDER: S-EPMC6878636 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Development and validation of circulating CA125 prediction models in postmenopausal women.

Sasamoto Naoko N Babic Ana A Rosner Bernard A BA Fortner Renée T RT Vitonis Allison F AF Yamamoto Hidemi H Fichorova Raina N RN Titus Linda J LJ Tjønneland Anne A Hansen Louise L Kvaskoff Marina M Fournier Agnès A Mancini Francesca Romana FR Boeing Heiner H Trichopoulou Antonia A Peppa Eleni E Karakatsani Anna A Palli Domenico D Grioni Sara S Mattiello Amalia A Tumino Rosario R Fiano Valentina V Onland-Moret N Charlotte NC Weiderpass Elisabete E Gram Inger T IT Quirós J Ramón JR Lujan-Barroso Leila L Sánchez Maria-Jose MJ Colorado-Yohar Sandra S Barricarte Aurelio A Amiano Pilar P Idahl Annika A Lundin Eva E Sartor Hanna H Khaw Kay-Tee KT Key Timothy J TJ Muller David D Riboli Elio E Gunter Marc M Dossus Laure L Trabert Britton B Wentzensen Nicolas N Kaaks Rudolf R Cramer Daniel W DW Tworoger Shelley S SS Terry Kathryn L KL

Journal of ovarian research 20191126 1

<h4>Background</h4>Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker.<h4>Methods</h4>We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian can ...[more]

PMID: 31771659

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Project description:ImportanceEndometrial 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.ObjectiveTo clinically validate a metabolomics-based classification algorithm as a screening test for EC.Design, setting, and participantsThis 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.Main outcomes and measuresThe 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.ResultsThe 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).Conclusions and relevanceIn 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.

Project description:Background and aimMetabolic-associated fatty liver disease (MAFLD) has gradually become one of the main health concerns regarding liver diseases. Postmenopausal women represent a high-risk group for MAFLD; therefore, it is of great importance to identify and intervene with patients at risk at an early stage. This study established a predictive nomogram model of MAFLD in postmenopausal women and to enhance the clinical utility of the new model, the researchers limited variables to simple clinical and laboratory indicators that are readily obtainable.MethodsData of 942 postmenopausal women from January 2023 to October 2023 were retrospectively collected and divided into two groups according to the collection time: the training group (676 cases) and the validation group (226 cases). Significant indicators independently related to MAFLD were identified through univariate logistic regression and stepwise regression, and the MAFLD prediction nomogram was established. The C-index and calibration curve were used to quantify the nomogram performance, and the model was evaluated by measuring the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsOf 37 variables, 11 predictors were identified, including occupation (worker), body mass index, waist-to-hip ratio, number of abortions, anxiety, hypertension, hyperlipidemia, diabetes, hyperuricemia, and diet (meat and processed meat). The C-index of the training group predicting the related risk factors was 0.827 (95% confidence interval [CI] 0.794-0.860). The C-index of the validation group was 0.787 (95% CI 0.728-0.846). Calibration curves 1 and 2 (BS1000 times) were close to the diagonal, showing a good agreement between the predicted probability and the actual incidence in the two groups. The AUC of the training group was 0.827, the sensitivity was 0.784, and the specificity was 0.735. The AUC of the validation group was 0.787, the sensitivity was 0.674, and the specificity was 0.772. The DCA curve showed that the nomogram had a good net benefit in predicting MAFLD in postmenopausal women.ConclusionsA predictive nomogram for MAFLD in postmenopausal women was established and verified, which can assist clinicians in evaluating the risk of MAFLD at an early stage.

Dataset Information

Development and validation of circulating CA125 prediction models in postmenopausal women.

Background

Methods

Result

Conclusions

Publications

Development and validation of circulating CA125 prediction models in postmenopausal women.

Similar Datasets

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