Models for predicting risk of endometrial cancer: a systematic review
Background: Endometrial cancer (EC) is the most prevalent gynaecological cancer in the UK with a rising incidence. Various models exist to predict the risk of developing EC, for different settings and prediction timeframes. This systematic review aims to provide a summary of models and assess their...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
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BioMed Central
2025
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| _version_ | 1824459264651952128 |
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| author | Forder, BH Ardasheva, A Atha, K Nentwich, H Abhari, R Kartsonaki, C |
| author_facet | Forder, BH Ardasheva, A Atha, K Nentwich, H Abhari, R Kartsonaki, C |
| author_sort | Forder, BH |
| collection | OXFORD |
| description | Background: Endometrial cancer (EC) is the most prevalent gynaecological cancer in the UK with a rising incidence. Various models exist to predict the risk of developing EC, for different settings and prediction timeframes. This systematic review aims to provide a summary of models and assess their characteristics and performance. Methods: A systematic search of the MEDLINE and Embase (OVID) databases was used to identify risk prediction models related to EC and studies validating these models. Papers relating to predicting the risk of a future diagnosis of EC were selected for inclusion. Study characteristics, variables included in the model, methods used, and model performance, were extracted. The Prediction model Risk-of-Bias Assessment Tool was used to assess model quality. Results: Twenty studies describing 19 models were included. Ten were designed for the general population and nine for high-risk populations. Three models were developed for premenopausal women and two for postmenopausal women. Logistic regression was the most used development method. Three models, all in the general population, had a low risk of bias and all models had high applicability. Most models had moderate (area under the receiver operating characteristic curve (AUC) 0.60–0.80) or high predictive ability (AUC > 0.80) with AUCs ranging from 0.56 to 0.92. Calibration was assessed for five models. Two of these, the Hippisley-Cox and Coupland QCancer models, had high predictive ability and were well calibrated; these models also received a low risk of bias rating. Conclusions: Several models of moderate-high predictive ability exist for predicting the risk of EC, but study quality varies, with most models at high risk of bias. External validation of well-performing models in large, diverse cohorts is needed to assess their utility. Registration: The protocol for this review is available on PROSPERO (CRD42022303085). |
| first_indexed | 2025-02-19T04:39:02Z |
| format | Journal article |
| id | oxford-uuid:1a79d9cb-56dc-4281-a4f8-c48ddbb948fd |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:39:02Z |
| publishDate | 2025 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:1a79d9cb-56dc-4281-a4f8-c48ddbb948fd2025-02-13T20:12:42ZModels for predicting risk of endometrial cancer: a systematic reviewJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1a79d9cb-56dc-4281-a4f8-c48ddbb948fdEnglishJisc Publications RouterBioMed Central2025Forder, BHArdasheva, AAtha, KNentwich, HAbhari, RKartsonaki, CBackground: Endometrial cancer (EC) is the most prevalent gynaecological cancer in the UK with a rising incidence. Various models exist to predict the risk of developing EC, for different settings and prediction timeframes. This systematic review aims to provide a summary of models and assess their characteristics and performance. Methods: A systematic search of the MEDLINE and Embase (OVID) databases was used to identify risk prediction models related to EC and studies validating these models. Papers relating to predicting the risk of a future diagnosis of EC were selected for inclusion. Study characteristics, variables included in the model, methods used, and model performance, were extracted. The Prediction model Risk-of-Bias Assessment Tool was used to assess model quality. Results: Twenty studies describing 19 models were included. Ten were designed for the general population and nine for high-risk populations. Three models were developed for premenopausal women and two for postmenopausal women. Logistic regression was the most used development method. Three models, all in the general population, had a low risk of bias and all models had high applicability. Most models had moderate (area under the receiver operating characteristic curve (AUC) 0.60–0.80) or high predictive ability (AUC > 0.80) with AUCs ranging from 0.56 to 0.92. Calibration was assessed for five models. Two of these, the Hippisley-Cox and Coupland QCancer models, had high predictive ability and were well calibrated; these models also received a low risk of bias rating. Conclusions: Several models of moderate-high predictive ability exist for predicting the risk of EC, but study quality varies, with most models at high risk of bias. External validation of well-performing models in large, diverse cohorts is needed to assess their utility. Registration: The protocol for this review is available on PROSPERO (CRD42022303085). |
| spellingShingle | Forder, BH Ardasheva, A Atha, K Nentwich, H Abhari, R Kartsonaki, C Models for predicting risk of endometrial cancer: a systematic review |
| title | Models for predicting risk of endometrial cancer: a systematic review |
| title_full | Models for predicting risk of endometrial cancer: a systematic review |
| title_fullStr | Models for predicting risk of endometrial cancer: a systematic review |
| title_full_unstemmed | Models for predicting risk of endometrial cancer: a systematic review |
| title_short | Models for predicting risk of endometrial cancer: a systematic review |
| title_sort | models for predicting risk of endometrial cancer a systematic review |
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