Identifying Women With Suspected Ovarian Cancer
Identifying Women With Suspected Ovarian Cancer
Objective To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms.
Design Cohort study with data from 375 UK QResearch general practices for development and 189 for validation.
Participants Women aged 30-84 without a diagnosis of ovarian cancer at baseline and without appetite loss, weight loss, abdominal pain, abdominal distension, rectal bleeding, or postmenopausal bleeding recorded in previous 12 months.
Main outcome The primary outcome was incident diagnosis of ovarian cancer recorded in the next two years.
Methods Risk factors examined included age, family history of ovarian cancer, previous cancers other than ovarian, body mass index (BMI), smoking, alcohol, deprivation, loss of appetite, weight loss, abdominal pain, abdominal distension, rectal bleeding, postmenopausal bleeding, urinary frequency, diarrhoea, constipation, tiredness, and anaemia. Cox proportional hazards models were used to develop the risk equation. Measures of calibration and discrimination assessed performance in the validation cohort.
Results In the derivation cohort there were 976 incident cases of ovarian cancer from 2.03 million person years. Independent predictors were age, family history of ovarian cancer (9.8-fold higher risk), anaemia (2.3-fold higher), abdominal pain (sevenfold higher), abdominal distension (23-fold higher), rectal bleeding (twofold higher), postmenopausal bleeding (6.6-fold higher), appetite loss (5.2-fold higher), and weight loss (twofold higher). On validation, the algorithm explained 57.6% of the variation. The receiver operating characteristics curve (ROC) statistic was 0.84, and the D statistic was 2.38. The 10% of women with the highest predicted risks contained 63% of all ovarian cancers diagnosed over the next two years.
Conclusion The algorithm has good discrimination and calibration and, after independent validation in an external cohort, could potentially be used to identify those at highest risk of ovarian cancer to facilitate early referral and investigation. Further research is needed to assess how best to implement the algorithm, its cost effectiveness, and whether, on implementation, it has any impact on health outcomes.
Ovarian cancer is the seventh most common cancer in women worldwide, affecting 225 000 new patients each year. Of these, about 6700 women are in the United Kingdom, giving the UK one of the highest rates in Europe. Most women are diagnosed with stage III or stage IV cancer, for which the five year survival is 20% and 6%, respectively. Less than 30% of women are diagnosed with stage I ovarian cancer, and, of these, 90% will survive to five years. While ovarian cancer is the leading cause of death in the UK from gynaecological malignancies, there have been improvements in survival in the past two decades, which might reflect earlier diagnosis and more effective treatments. In general terms, the earlier the cancer is diagnosed, the more treatment options are available and the better the prognosis.
As there are few established risk factors, targeted screening of asymptomatic patients at risk of developing ovarian cancer is unlikely to be cost effective at present (although further information is likely to become available when the UK ovarian cancer screening trial reports in 2015-6). The challenge presented by ovarian cancer, therefore, is to make the correct diagnosis as early as possible, despite the non-specific nature of symptoms and signs. This is particularly the case in primary care, where general practitioners need to differentiate those patients for whom further investigation is warranted from those who require reassurance or a "watch and wait" policy. Moreover, primary care clinicians need to decide which patients require urgent investigation or referral and which require routine tests or referral. Earlier diagnosis, however, could improve with more targeted investigation of symptomatic patients and increased public awareness of symptoms as encouraged by the National Awareness and Early Diagnosis Initiative (NAEDI). It has been estimated that 10% of deaths from ovarian cancers might be avoidable. Other guidelines and policies aim to increase access to diagnostic investigations for general practitioners, and tools to help assess absolute risk of different types of cancer are needed to help ensure the right patients are investigated as well as to optimise the use of scarce resources including abdominal and transvaginal ultrasonography, computed tomography, or magnetic resonance imaging. For ovarian cancer, the current guidance from the National Institute for Health and Clinical Excellence encourages the use of blood tests to measure CA125 concentration for symptomatic women as a prelude to ultrasound scanning, although this has not been validated in a primary care setting. CA125 concentration is raised in half the women who have early stage ovarian cancer and 90% of those with more advanced disease.
We developed and validated a risk prediction algorithm to estimate the individualised absolute risk of having ovarian cancer, incorporating both symptoms and other risk factors, to help identify those at highest risk for further investigation or referral. We used QResearch (a large UK primary care database) to develop the risk prediction models as it contains robust data on many of the relevant exposures and outcomes. It is also representative of the population in which such a model is likely to be used and has been used successfully to develop and validate a range of prognostic models for use in primary care as well as models designed to help earlier detection of other cancers.
Abstract and Introduction
Abstract
Objective To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms.
Design Cohort study with data from 375 UK QResearch general practices for development and 189 for validation.
Participants Women aged 30-84 without a diagnosis of ovarian cancer at baseline and without appetite loss, weight loss, abdominal pain, abdominal distension, rectal bleeding, or postmenopausal bleeding recorded in previous 12 months.
Main outcome The primary outcome was incident diagnosis of ovarian cancer recorded in the next two years.
Methods Risk factors examined included age, family history of ovarian cancer, previous cancers other than ovarian, body mass index (BMI), smoking, alcohol, deprivation, loss of appetite, weight loss, abdominal pain, abdominal distension, rectal bleeding, postmenopausal bleeding, urinary frequency, diarrhoea, constipation, tiredness, and anaemia. Cox proportional hazards models were used to develop the risk equation. Measures of calibration and discrimination assessed performance in the validation cohort.
Results In the derivation cohort there were 976 incident cases of ovarian cancer from 2.03 million person years. Independent predictors were age, family history of ovarian cancer (9.8-fold higher risk), anaemia (2.3-fold higher), abdominal pain (sevenfold higher), abdominal distension (23-fold higher), rectal bleeding (twofold higher), postmenopausal bleeding (6.6-fold higher), appetite loss (5.2-fold higher), and weight loss (twofold higher). On validation, the algorithm explained 57.6% of the variation. The receiver operating characteristics curve (ROC) statistic was 0.84, and the D statistic was 2.38. The 10% of women with the highest predicted risks contained 63% of all ovarian cancers diagnosed over the next two years.
Conclusion The algorithm has good discrimination and calibration and, after independent validation in an external cohort, could potentially be used to identify those at highest risk of ovarian cancer to facilitate early referral and investigation. Further research is needed to assess how best to implement the algorithm, its cost effectiveness, and whether, on implementation, it has any impact on health outcomes.
Introduction
Ovarian cancer is the seventh most common cancer in women worldwide, affecting 225 000 new patients each year. Of these, about 6700 women are in the United Kingdom, giving the UK one of the highest rates in Europe. Most women are diagnosed with stage III or stage IV cancer, for which the five year survival is 20% and 6%, respectively. Less than 30% of women are diagnosed with stage I ovarian cancer, and, of these, 90% will survive to five years. While ovarian cancer is the leading cause of death in the UK from gynaecological malignancies, there have been improvements in survival in the past two decades, which might reflect earlier diagnosis and more effective treatments. In general terms, the earlier the cancer is diagnosed, the more treatment options are available and the better the prognosis.
As there are few established risk factors, targeted screening of asymptomatic patients at risk of developing ovarian cancer is unlikely to be cost effective at present (although further information is likely to become available when the UK ovarian cancer screening trial reports in 2015-6). The challenge presented by ovarian cancer, therefore, is to make the correct diagnosis as early as possible, despite the non-specific nature of symptoms and signs. This is particularly the case in primary care, where general practitioners need to differentiate those patients for whom further investigation is warranted from those who require reassurance or a "watch and wait" policy. Moreover, primary care clinicians need to decide which patients require urgent investigation or referral and which require routine tests or referral. Earlier diagnosis, however, could improve with more targeted investigation of symptomatic patients and increased public awareness of symptoms as encouraged by the National Awareness and Early Diagnosis Initiative (NAEDI). It has been estimated that 10% of deaths from ovarian cancers might be avoidable. Other guidelines and policies aim to increase access to diagnostic investigations for general practitioners, and tools to help assess absolute risk of different types of cancer are needed to help ensure the right patients are investigated as well as to optimise the use of scarce resources including abdominal and transvaginal ultrasonography, computed tomography, or magnetic resonance imaging. For ovarian cancer, the current guidance from the National Institute for Health and Clinical Excellence encourages the use of blood tests to measure CA125 concentration for symptomatic women as a prelude to ultrasound scanning, although this has not been validated in a primary care setting. CA125 concentration is raised in half the women who have early stage ovarian cancer and 90% of those with more advanced disease.
We developed and validated a risk prediction algorithm to estimate the individualised absolute risk of having ovarian cancer, incorporating both symptoms and other risk factors, to help identify those at highest risk for further investigation or referral. We used QResearch (a large UK primary care database) to develop the risk prediction models as it contains robust data on many of the relevant exposures and outcomes. It is also representative of the population in which such a model is likely to be used and has been used successfully to develop and validate a range of prognostic models for use in primary care as well as models designed to help earlier detection of other cancers.
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