Evaluation of the cost of CA-125 measurement, physical exam, and imaging in the diagnosis of recurrent ovarian cancer

abstract

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Highlights

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Ovarian cancer patients are followed with National Comprehensive Cancer Network Guidelines

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95% of patients with recurrence had findings at time of office visit or elevated CA 125

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Imaging for post treatment screening increased cost with little increase in recurrence detection

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Objective

Ovarian cancer accounts for 50% of deaths from gynecologic malignancies. We sought to determine the cost of common methods of surveillance of women with ovarian cancer in first clinical remission. The current standard for post treatment surveillance is the National Comprehensive Cancer Network (NCCN) guidelines.

Methods

We retrospectively determined how recurrence was initially detected at our institution and a cost model was created and applied to the United States population to calculate surveillance costs using the Surveillance Epidemiology & End Results (SEER) database.

Results

57% (n = 60) of first recurrences were identified by increasing CA 125 level. Routine office visit identified 27% (n = 29) of recurrences, and 15% (n = 16) were diagnosed initially with CT scan. In 5% (5/105), CT abnormality was the only finding. 95% (100/105) of patients had either elevated CA 125 or office visit findings at time of recurrence. Of the 22,000 women diagnosed with ovarian cancer yearly, 60% (n = 13,266) will have advanced disease and are likely to recur. The surveillance cost for this population for 2 years using our model is $32,500,000 using NCCN guidelines and $58,000,000 if one CT scan is obtained.

Conclusions

Our data suggests that following NCCN guidelines will detect 95% of recurrences. An additional $26 million will be needed to identify the 5% of women with recurrence seen on CT only. Post treatment surveillance of ovarian cancer patients contributes significantly to health care costs. Use of CT scan to follow these patients largely increases cost with only a small increase in recurrence detection.

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