Development and validation of prediction models for endometrial cancer in postmenopausal bleeding

Alyssa Sze Wai Wong, Chun Wai Cheung, Linda Wen Ying Fung, Terence Tzu Hsi Lao, Ben Willem J Mol, Daljit Singh Sahota

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Objective To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). Methods A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Results Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity = 66.5%; Specificity = 68.9%; +ve LR = 2.14; -ve LR = 0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity = 82.7%; Specificity = 88.3%; +ve LR = 6.38; -ve LR = 0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone (difference = 0.19, 95% CI 0.15-0.24; p < 0.0001) and History plus ET (difference = 0.19, 95% CI 0.16-0.23, p < 0.0001) and history plus ET was similar to that of using ET alone (difference = 0.001 95% CI -0.015 to 0.0018, p = 0.84). Conclusions A risk model using only patient characteristics showed fair diagnostic accuracy. Addition of patient characteristics to ET did not improve the diagnostic accuracy as compared to ET alone in our cohort.

LanguageEnglish
Pages220-224
Number of pages5
JournalEuropean Journal of Obstetrics and Gynecology and Reproductive Biology
Volume203
DOIs
Publication statusPublished - 1 Aug 2016

Keywords

  • Endometrial cancer
  • Postmenopausal bleeding
  • Prediction models

ASJC Scopus subject areas

  • Reproductive Medicine
  • Obstetrics and Gynaecology

Cite this

Wong, Alyssa Sze Wai ; Cheung, Chun Wai ; Fung, Linda Wen Ying ; Lao, Terence Tzu Hsi ; Mol, Ben Willem J ; Sahota, Daljit Singh. / Development and validation of prediction models for endometrial cancer in postmenopausal bleeding. In: European Journal of Obstetrics and Gynecology and Reproductive Biology. 2016 ; Vol. 203. pp. 220-224.
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title = "Development and validation of prediction models for endometrial cancer in postmenopausal bleeding",
abstract = "Objective To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). Methods A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Results Out of 4383 women with PMB, 168 (3.8{\%}) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95{\%} confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95{\%} CI 0.67-0.80) and 0.72 (Sensitivity = 66.5{\%}; Specificity = 68.9{\%}; +ve LR = 2.14; -ve LR = 0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95{\%} CI 0.88-0.96) and 0.71 (Sensitivity = 82.7{\%}; Specificity = 88.3{\%}; +ve LR = 6.38; -ve LR = 0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone (difference = 0.19, 95{\%} CI 0.15-0.24; p < 0.0001) and History plus ET (difference = 0.19, 95{\%} CI 0.16-0.23, p < 0.0001) and history plus ET was similar to that of using ET alone (difference = 0.001 95{\%} CI -0.015 to 0.0018, p = 0.84). Conclusions A risk model using only patient characteristics showed fair diagnostic accuracy. Addition of patient characteristics to ET did not improve the diagnostic accuracy as compared to ET alone in our cohort.",
keywords = "Endometrial cancer, Postmenopausal bleeding, Prediction models",
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Development and validation of prediction models for endometrial cancer in postmenopausal bleeding. / Wong, Alyssa Sze Wai; Cheung, Chun Wai; Fung, Linda Wen Ying; Lao, Terence Tzu Hsi; Mol, Ben Willem J; Sahota, Daljit Singh.

In: European Journal of Obstetrics and Gynecology and Reproductive Biology, Vol. 203, 01.08.2016, p. 220-224.

Research output: Contribution to journalArticle

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T1 - Development and validation of prediction models for endometrial cancer in postmenopausal bleeding

AU - Wong, Alyssa Sze Wai

AU - Cheung, Chun Wai

AU - Fung, Linda Wen Ying

AU - Lao, Terence Tzu Hsi

AU - Mol, Ben Willem J

AU - Sahota, Daljit Singh

PY - 2016/8/1

Y1 - 2016/8/1

N2 - Objective To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). Methods A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Results Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity = 66.5%; Specificity = 68.9%; +ve LR = 2.14; -ve LR = 0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity = 82.7%; Specificity = 88.3%; +ve LR = 6.38; -ve LR = 0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone (difference = 0.19, 95% CI 0.15-0.24; p < 0.0001) and History plus ET (difference = 0.19, 95% CI 0.16-0.23, p < 0.0001) and history plus ET was similar to that of using ET alone (difference = 0.001 95% CI -0.015 to 0.0018, p = 0.84). Conclusions A risk model using only patient characteristics showed fair diagnostic accuracy. Addition of patient characteristics to ET did not improve the diagnostic accuracy as compared to ET alone in our cohort.

AB - Objective To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). Methods A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Results Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity = 66.5%; Specificity = 68.9%; +ve LR = 2.14; -ve LR = 0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity = 82.7%; Specificity = 88.3%; +ve LR = 6.38; -ve LR = 0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone (difference = 0.19, 95% CI 0.15-0.24; p < 0.0001) and History plus ET (difference = 0.19, 95% CI 0.16-0.23, p < 0.0001) and history plus ET was similar to that of using ET alone (difference = 0.001 95% CI -0.015 to 0.0018, p = 0.84). Conclusions A risk model using only patient characteristics showed fair diagnostic accuracy. Addition of patient characteristics to ET did not improve the diagnostic accuracy as compared to ET alone in our cohort.

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KW - Postmenopausal bleeding

KW - Prediction models

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