paywalled: Differential diagnosis of a pelvic mass: improved algorithms and novel biomarkers.

Differential diagnosis of a pelvic mass: improved algorithms and novel biomarkers.:


ABSTRACT:

More than 200,000 women undergo exploratory surgery for a pelvic mass in the United States each year and 13%-21% of pelvic lesions are found to be malignant. Individual reports and meta-analysis indicate better outcomes when cancer surgery is performed by gynecologic oncologists. Despite the advantages provided by more thorough staging and cytoreductive surgery, only 30%-50% of women with ovarian cancer are referred to surgeons with specialized training in the United States. Imaging, menopausal status and biomarkers can aid in distinguishing malignant from benign pelvic masses to inform decisions regarding appropriate referral. The risk of malignancy index (RMI) uses ultrasound, menopausal status and CA125 and has been utilized in the United Kingdom for two decades, providing sensitivity that has ranged from 71%-88% and specificity it from 97%-74% for identifying patients with malignant disease. Criteria have been established by the Society of Gynecology Oncology and American College of Obstetrics and Gynecology for referral to a gynecologic oncologist, but these have lower sensitivity and specificity than the RMI.

Recently, two new algorithms have been developed to identify women at sufficiently high risk to prompt referral to a specialized surgeon. The OVA1 multivariate index incorporates imaging, menopausal status, CA125 and four other proteomic biomarkers. Use of OVA1 provides 85%-96% sensitivity at 28%-40% specificity depending upon menopausal status. The negative predictive value for women judged to be at low risk is 94%-96%.

The risk of malignancy algorithm (ROMA) includes CA125, human epididymal protein 4 and menopausal status, but not imaging results.

open access: Identifying women with suspected ovarian cancer in primary care: derivation and validation of algorithm UK/Wales

Objective To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms.

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.

open access: Identifying women with suspected ovarian cancer in primary care: derivation and validation of algorithm | BMJ (note reference to recent NICE guidelines)

Objective
To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms.

Main outcome
The primary outcome was incident diagnosis of ovarian cancer recorded in 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.

.......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.4 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.........

Summary of key findings

We have developed and validated a new algorithm designed to estimate the absolute risk of having existing but as yet undiagnosed ovarian cancer based on a combination of symptoms and simple variables such as age and family history of ovarian cancer, which the patient is likely to know and which will increase the baseline absolute risk.....
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What is already known on this topic

• Ovarian cancer is the second most common gynaecological cancer and most women are diagnosed with late stage disease, which has a poor survival rate
• Earlier diagnosis could improve with more targeted investigation of symptomatic patients and increased public awareness of symptoms, which is a major challenge given the non-specific nature of some of the symptoms

What this study adds

• An algorithm based on simple clinical variables such as age, family history of ovarian cancer, anaemia, abdominal pain, abdominal distension, rectal bleeding, postmenopausal bleeding, appetite loss, and weight loss, which the patient is likely to know or which are routinely recorded in general practice computer systems, can estimate absolute risk of ovarian cancer in women with and without symptoms in primary care
• The algorithm could be integrated into general practice clinical computer systems and used to assess risk in women presenting with and without symptoms

open access: BMJ - Identifying women with suspected ovarian cancer in primary care: derivation and validation of algorithm | BMJ

Objective 

To derive and validate an algorithm to estimate the absolute risk of having ovarian cancer in women with and without symptoms.

Main outcome The primary outcome was incident diagnosis of ovarian cancer recorded in the next two years.

The incidence rate in our population was higher than published national data based on cancer registries.2

What is already known on this topic

• Ovarian cancer is the second most common gynaecological cancer and most women are diagnosed with late stage disease, which has a poor survival rate
• Earlier diagnosis could improve with more targeted investigation of symptomatic patients and increased public awareness of symptoms, which is a major challenge given the non-specific nature of some of the symptoms

What this study adds

• An algorithm based on simple clinical variables such as age, family history of ovarian cancer, anaemia, abdominal pain, abdominal distension, rectal bleeding, postmenopausal bleeding, appetite loss, and weight loss, which the patient is likely to know or which are routinely recorded in general practice computer systems, can estimate absolute risk of ovarian cancer in women with and without symptoms in primary care
• The algorithm could be integrated into general practice clinical computer systems and used to assess risk in women presenting with and without symptoms

The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? (HE4/CA125/pre-post menopausal)

Conclusions: The ROMA is a simple scoring system which shows excellent diagnostic performance for the detection of EOC in post-menopausal women, but not in pre-menopausal women. Moreover, the dual marker combination of HE4 and CA125 (ROMA) does not show better performance than HE4 alone.

Breast cancer classification algorithm to identify 20 gene signature developed using Microsoft Excel

"....The 10 most highly ranked genes predictive of poor prognosis and those 10 genes most highly predictive of good prognosis established a 20-gene expression based predictor, which was found to perform as well as two other models in the validation group. According to Hassell, "Our algorithm produces prediction models with comparable accuracy to other feature selection techniques while having generally better accessibility and useability for biological research scientists. We've begun using our algorithm to generate gene expression based prediction models of breast cancer cell sensitivity to commonly used anti-cancer therapies"....cont'd

abstract: A prospective U.S. ovarian cancer screening study using the risk of ovarian cancer algorithm (ROCA)

ASCO (Post-Meeting Edition)
Abstract
5003
Background: There are currently no effective screening tools for the early detection of ovarian cancer in women at average population risk. We evaluated a screening strategy that incorporates change of CA-125 over time and age of the participant to estimate risk of ovarian cancer, referring a small fraction (2%) of apparently healthy individuals annually to transvaginal sonography (TVS).
Methods: A single arm, prospective, multicenter screening study enrolled postmenopausal women age 50 to 74 with no significant family history of breast or ovarian cancer. Participants underwent a CA-125 blood test annually. Based on the Risk of Ovarian Cancer Algorithm (ROCA) result, women were triaged to the next annual CA-125 (low risk), repeat CA-125 in 3 months (intermediate risk), or TVS and referral to a gynecologic oncologist (high risk). Based on clinical findings and TVS, the gyn onc made the decision whether to proceed with surgery.
Results: 3238 women participated over an eight year period. The average annual rate of referral to 3 monthly CA125 was 6.8%, and the average annual rate of TVS and gyn onc referral was 0.9%. Cumulatively 85 women (2.6%) received TVS and referral to a gyn onc. Eight women subsequently underwent surgery based on the TVS and referral, with 3 invasive ovarian cancers, 2 borderline ovarian tumors and 3 benign ovarian tumors, providing a positive predictive value of 37.5% (95% CI 8.5%,75.5%).The combined specificity of ROCA followed by TVS for referral to surgery is 99.7% (95% CI 99.5%, 99.9%). The 3 invasive ovarian cancers were high-grade epithelial tumors that were all early stage (two stage 1C and stage IIB). All 3 women with invasive ovarian cancer had at least 3 years with low risk, annual CA-125 values prior to a rising CA-125.
Conclusions: In this prospective, single arm study, the ROCA followed by TVS demonstrated excellent specificity and PPV in a population of U.S. women at average risk for ovarian cancer. As expected, less than 1% of participants annually required a TVS. In addition, the invasive high-grade ovarian cancers that were detected were early stage. This study provides early evidence that ROCA followed by TVS is a feasible strategy for screening women over 50 years of age.

ASCO Annual Meeting: Ovarian Cancer: Some Hope in the Quest for an Effective Screening Method

Note: "The prospective study of 3,238 postmenopausal women aged 50 to 74 with no significant family history of breast or ovarian cancer, enrolled over a course of nine years."
CHICAGO—In the continuing quest for an effective screening method to detect ovarian cancer at an early stage, a new approach using an algorithm has shown promise in early testing as reported here at the ASCO Annual Meeting and featured beforehand in a teleconference by the society.