Coverage Policy Manual
Policy #: 2010022
Category: Laboratory
Initiated: April 2010
Last Review: December 2023
  Prostate Cancer Predicting Risk of Recurrence, Systems Pathology
Description:
Predicting risk of recurrence in patients undergoing treatment for prostate cancer is difficult, as it is for most malignancies. Over time, risk models for patients with prostate cancer have evolved from early efforts that relied on grade, stage, and prostate-specific antigen (PSA) levels to complex multivariate models. Shariat and colleagues, in a  in 2008 article, indicates that there are more than 65 published, externally validated prostate cancer nomograms and other tools that use standard clinical parameters such as age, clinical or pathologic stage, grade, percent of cancer on biopsy cores, and PSA or its derivatives to predict various clinical and pathologic outcomes.  
 
Recent studies have begun to study a different approach by adding both cellular and biologic features to the clinical and pathological information noted above. This approach has been called “Systems Pathology.”
 
Aureon Laboratories offered two pathology tests called the Prostate Px+ test and the Post-Op Px test (formerly called Prostate Px). Prostate Px+ was described as useful at diagnosis to patients considering surgery (radical prostatectomy) or other treatment options by providing physicians with objective information regarding the probability of disease progression. Post-Op Px estimated risk of PSA recurrence and disease progression after surgery. In October 2011, the company ceased operations and the tests are no longer offered.
 
Iris International offers the NADiA® ProsVue™ test, which received U.S. Food and Drug Administration 510(k) clearance in 2011.The NADiA ProsVue test evaluates risk of prostate cancer recurrence after radical prostatectomy when PSA levels are less than 0.1 ng/mL. The NADiA immunoassay, polymerase chain reaction test is used to determine PSA levels on 3 serum samples taken between 6 weeks and 20 months after radical prostatectomy. The PSA data are entered into the ProsVue software to ensure appropriate serum sample use and calculation of assay results and to determine the rate of PSA change, the PSA slope.
  
There are no specific CPT or HCPCS codes to report this type of testing.  It is likely that some combination of the codes listed below, including multiple billing of single codes, would be submitted.
88313 (2 units) – Special stains; Group II, all other (e.g., iron, trichrome), except immunocytochemistry and immunoperoxidase stains, including interpretation and report, each
88323 (1 unit) – Consultation and report on referred material requiring preparation of slides
88347 (8 units) – Immunofluorescent study, each antibody; indirect method
88399 (1 unit) – Unlisted, surgical pathology procedure
99090 - Analysis of clinical data stored in computers (e.g., ECGs, blood pressure, hematologic data)
 
 
Policy/
Coverage:
Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
 
Testing to predict risk of recurrence for men with prostate cancer, including but not limited to the use of systems pathology, does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
 
For members with contracts without primary coverage criteria, testing to predict risk of recurrence for men with prostate cancer, including but not limited to the use of systems pathology, is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
Rationale:
This policy was developed after receipt of a claim for the service and medical literature did not report the efficacy of this testing or how the results of the testing improved health outcomes.
 
Assessment of a diagnostic test, including tests that are used to predict clinical risk, typically focuses on 3 parameters: 1) its technical performance; 2) diagnostic performance (sensitivity, specificity, and positive and negative predictive value) in appropriate populations of patients; and 3) demonstration that the diagnostic information can be used to improve patient outcomes (clinical utility).
 
Technical performance for such testing may compare test measurements with a gold standard and may also compare results taken on different occasions (test-retest).
 
Diagnostic performance is evaluated by the ability of a test to accurately predict the clinical outcome. The sensitivity of a test is the ability to detect a disease (determine an outcome) when the condition is present (true positive), while the specificity is the ability to detect the absence of a disease or outcome when the disease is not present (true negative).
 
A key aspect in evaluating clinical test performance is evidence related to improvement of clinical outcomes with use of this testing. That is, evidence that assesses the link between use of a test to changes in health outcomes (clinical utility). In a clinical area such a prostate cancer where multiple tools to predict risk already exist, a new test must demonstrate that any improvement in predictive accuracy results in meaningful changes in therapy and leads to improved outcomes. In many cases, comparative trials are needed to demonstrate the impact of testing on net health outcome.
 
A literature review using MEDLINE through February 2010 was done.  The linkage between these publications and the commercially available tests is uncertain. It is possible that data relating to these two tests may also be part of information that has been presented at meetings and is available only as an abstract.   
 
In 2008, Donovan and colleagues reported on use of a systems pathology tool through integration of clinicopathologic data with image analysis and quantitative immunofluorescence of prostate cancer tissue.  In this study, an algorithm for postoperative risk was derived using a cohort of 758 patients with clinically localized or locally advanced prostate cancer who had tissue available for analysis and for whom outcomes were known. This cohort was assembled from one institution; the patients were initially treated between 1985 and 2003. Samples were identified for 971 patients, but the cohort was reduced to 881 because some patients received treatment before prostatectomy and treatment before clinical failure. An additional 123 patients were excluded because of missing data elements, including missing outcome information. The derived model predicted distant metastasis and/or androgen-independent recurrence. The model was derived using 40 potential variables. The outcome was clinical failure; clinical failure (CF) was defined as unequivocal radiographic or pathologic evidence of metastasis, increasing PSA in a castrate state, or death related to prostate cancer.
 
The model was derived using a training set of 373 patients with 33 (8.8%) CF events (24 positive bone scans and 9 patients with increasing PSA levels). The model included androgen receptor (AR) levels, dominant prostatectomy Gleason grade, lymph node involvement, and three quantitative characteristics from hematoxylin and eosin staining of prostate tissue. The model had a sensitivity of 90%, and specificity of 91% for predicting CF within 5 years after prostatectomy. The model was then validated on an independent cohort of 385 patients with 29 (7.5%) CF events (22 positive bone scans and 7 with increasing PSA levels). This gave a sensitivity of 84% and specificity of 85%. High levels of AR predicted shorter time to castrate prostate-specific antigen increase after androgen deprivation therapy (ADT). The authors concluded that the integration of clinicopathologic variables with imaging and biomarker data (systems pathology) resulted in a highly accurate tool for predicting CF within 5 years after prostatectomy. They also noted support for a role for androgen receptor signaling in clinical progression and duration of response to androgen deprivation therapy.
 
In an editorial accompanying the 2008 article by Donovan et al.  Klein (2008) raises a number of questions. A major question raised is whether the differences with these new models have sufficient clinical relevance to justify the extra effort, expense, and expertise needed for the systems-pathology approach. He comments that additional studies are needed to understand the incremental value of this new information.
 
The paper by Donovan also comments that they believe this approach will allow the development of more informed and appropriate treatment plans, including the potential for early decision about androgen deprivation therapy, radiation therapy, and/or chemotherapy in a subset of patients.
 
In a subsequent article from 2009, Donovan and colleagues reported on derivation of another systems pathology model to predict risk in prostate cancer based on preoperative assessment, including biopsy results.  This publication reported on efforts to develop a patient-specific, biology-driven tool to predict outcome at diagnosis. The study also investigated whether biopsy androgen receptor levels predict a durable response to therapy after secondary treatment. The authors evaluated paraffin embedded prostate needle biopsy tissue from 1027 patients with T1c-T3 prostate cancer treated with surgery between 1989 and 2003 and followed a median of 8 years. Information was initially compiled on 1487 patients from 6 institutions. Four-hundred-sixty (460) patients were excluded from analysis because of incomplete or missing information. Clinical failure (CF) was determined as noted in the study summarized above. Modeling again began with 40 candidate variables. In the training set of 686 patients, 87 (12.7%) had clinical failure (9 with a positive bone scan and 78 with increasing PSA in a castrate state). A total of 219 (32%) of these patients received standard androgen ablation with or without salvage radiotherapy. (These treatments were done at the discretion of the treating physician for the cohort of patients in this analysis.) Using CF within 8 years as the outcome, the model had a sensitivity of 78% and specificity of 69% in the derivation set. The six variables in this model were as follows: preoperative PSA, dominant biopsy Gleason Grade (bGG), biopsy Gleason Score, and 3 systems pathology variables (androgen receptor, distance between epithelial tumor cells, and tumor epithelial cell area). Patients from another (the fifth) institution were used for the validation set. In the validation set of 341 patients, the sensitivity was 76% and specificity 64%. There were 44 clinical failures (4 with positive bone scan and 40 with increasing PSA in a castrate state). This study also found that increased androgen receptor in biopsy tumor cells predicted resistance to therapy. The authors concluded that the additional systems pathology data adds to the value of prediction rules used to assess outcome at diagnosis. The authors also comment that the nature of this study has the potential for bias. In an attempt to reduce this bias and to perform a more robust validation study, they are investigating access to samples from randomized clinical trials.
 
Some of the investigators from these 2 studies were also involved in an earlier report from Memorial Sloan-Kettering on using this approach to predict CF (as measured by PSA recurrence) following radical prostatectomy (Cordon-Cardo, 2007).  This study involved a training set of 323 patients.
 
Similarly, Eggener and colleagues from the University of Chicago (2009) described development of two systems pathology models to determine which patients undergoing radical prostatectomy are likely to manifest systemic disease.  They found their models to be accurate and commented that use of the novel markers may enhance the accuracy of the systems pathology approach.
 
Veltri and colleagues from Johns Hopkins (2008) reported on use of nuclear morphometric signatures such as nuclear size, shape, DNA content, and chromatin texture in predicting PSA recurrence.  This model was found to have an area under the receiver operating characteristic (ROC) curve of 0.80. The authors concluded that PSA recurrence is more accurately predicted using these markers compared with routine pathology information alone.
 
As demonstrated by the data, the studies described above do not address the clinical utility of this testing. Currently it is not known whether use of these models that use systems pathology will result in changes in care that lead to improved patient outcomes. Additional data are needed to answer this important question.
 
In addition, studies are needed to determine which patients may benefit from this testing, as well as to determine when in the course of diagnosis and treatment the systems-pathology assessment should be performed. There also should be further discussion about which outcomes are the best to be used in developing models; there can be substantial differences in models that predict PSA recurrence from those that predict metastatic disease and those that predict death. In addition, models may be needed that evaluate risk following treatments other than radical prostatectomy.
 
The search term “protein biomarkers” on www.clinicaltrial.gov results in 1,361 hits for a very long list of both malignant and nonmalignant diseases.  
 
2011 Update
In 2010, Donovan et al. investigated whether clinical variables before treatment and tumor specimen characteristics from patients with castrate-resistant metastatic prostate cancer can be used to predict time to prostate cancer-specific mortality and overall survival (Donovan, 2010). Hematoxylin and eosin (H&E) slides, paraffin blocks, and outcome data from 104 castrate patients with metastatic prostate cancer were independently reviewed. Pathology samples were from prostatectomy specimens (n=43) and prostate needle biopsies (n=61). The patients included in the study had local and advanced disease (T1-T4), had been managed with radiotherapy or primary hormonal therapy, 47% had PSA level 20 ng/mL or higher, and 52% had a Gleason sum of greater than 7 at the time of diagnosis. H&E morphometry and quantitative immunofluorescence assays for cytokeratin-18 (epithelial cells), 4’,6-diamidino-2-phenylindole (nuclei), p63/high molecular weight keratin (basal cells), androgen receptors, and α-methyl CoA-racemase were performed. Immunofluorescence images were acquired with spectral imaging software and processed for quantification with specific algorithms. Median follow-up was 12 years from diagnosis. Of the 104 patients, 66 had evaluable immunofluorescence features. PSA level was the only clinical variable associated with outcome. The amount of androgen receptors present within tumor nuclei correlated with a greater risk of a shorter time to prostate cancer-specific mortality (p<0.05). No H&E features correlated with mortality. The authors concluded that, using systems pathology, they were able to identify and characterize a population of cells that expressed very high levels of androgen receptors that predict a more aggressive phenotype of prostate cancer.
 
In summary, studies are needed to determine which patients may benefit from this testing, as well as to determine when in the course of diagnosis and treatment the systems pathology assessment should be performed. There also should be further discussion about which outcomes are the best to be used in developing models; there can be substantial differences in models that predict PSA recurrence from those that predict metastatic disease and those that predict death. In addition, models may be needed that evaluate risk following treatments other than radical prostatectomy.
 
The value of using the systems pathology approach to determine risk is not known based on currently available studies. Thus, the impact on clinical outcomes is not known and the clinical utility of this testing is not known.
 
2013 Update
A literature search was conducted using MEDLINE database through September 2013. There was no new information identified that would prompt a change in the coverage statement. The following is a summary of the key identified literature.
 
Two studies published by Donovan et al. in 2012 both used the same sample of postoperative tissue specimens described in the 2008 paper by Donovan et al. (Donavon, 2012a; Donavon 2012b) One compared the Postop Px algorithm with 2 other nomograms for predicting PSA recurrence and clinical failure (PSA rise, bone metastasis or prostate cancer-related death) (Donovan, 2012b).  Data came from 373 patients included in the 2008 training set. The concordance-index (CI) was used as a measure of classification accuracy. Regarding PSA recurrence, the Px algorithm was more accurate (0.76) than the D’Amico nomogram (0.70) and the Kattan nomogram (0.75). Similarly, the Px model was more accurate for predicting clinical failure (0.84) than the D’Amico nomogram (0.73) and the Kattan nomogram (0.79). The other study (Donovan, 2012a) used specimens from transurethral resection of the prostate (TURP) in a postoperative model for predicting prostate cancer-specific survival and disease progression. A training set consisted of 256 patients and a validation set included 269 patients. Performance of the training set was a CI of 0.79, sensitivity of 75%, and specificity of 86%. In the validation set, CI was 0.76, sensitivity was 59% and specificity was 80%.
 
2014 Update
 
A literature search conducted through February 2014 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
In 2012 Moul et al reported on the ability of the NADiA ProsVue to predict prostate cancer recurrence after radical prostatectomy (Moul, 2012). The NADiA test is a PSA immunoassay, polymerase chain reaction test designed to measure PSA levels less than 0.01ng/mL. The ProsVue software calculates the risk of prostate cancer recurrence based on the rate of PSA change or slope of the 3 sequential NADiA PSA values. To validate the NADiA ProsVue, archived serum samples were tested from 304 men with biopsy-confirmed prostate cancer who underwent radical prostatectomy. Included patients had 3 serum samples available from 3 different time points after prostatectomy. PSA levels in the first serum sample after radical prostatectomy were required to be less than 0.1ng/mL. Study patients had been treated from 1990 to 2001 and were followed for up to 17.6 years. The median NADiA PSA level was 3.1 pg/mL after prostatectomy in patients who did not have prostate cancer recurrence and 14.1 pg/mL in patients with recurrence (p<0.001). In the prostate cancer recurrent group, PSA levels increased in the subsequent 2 serum samples tested but changed minimally in patients without recurrence. Patients with a PSA slope of greater than 2.0 pg/mL/mo had a median disease-free survival of 4.8 years compared with 17.6 years in patients with a PSA slope of 2.0 pg/mL/mo or less (p<0.001). PSA slope of greater than 2.0 pg/mL/mo predicted a significantly higher risk of recurrence with a univariate hazard ratio of 18.3 (95% confidence interval, 10.6 to 31.8, p<0.001). When the PSA slope was evaluated with the covariates of preprostatectomy PSA level, Gleason score and pathologic stage, the multivariate hazard ratio was 9.8 (95% CI, 5.4 to 17.8, p<0.001). Gleason score of 7 or more was the only other covariate that significantly predicted risk of recurrence with a hazard ratio of 5.4 (95% CI, 2.1 to 13.8, p<0.001). It is unknown whether the NADiA ProsVue would alter clinical management after radical prostatectomy, and there is no evidence to demonstrate incremental predictive value over other variables such as Gleason score or independent PSA levels.
    
2015 Update
 
A literature search conducted through January 2015 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
A 2014 update of the 2012 Moul study reanalyzed the prognostic value of a ProsVue result ≤2.0 pg/mL/mo and risk as stratified by a nomogram called the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) nomogram, for a reduced risk of prostate cancer-specific survival (Moul, 2014a). The authors also assessed its value for predicting clinical outcome in men who received salvage treatment for biochemical recurrence. Median overall survival for men with a ProsVue slope of ≤2.0 and >2.0 pg/mL/mo was 11.0 years (95% CI, 9.4-12.9) and 9.2 years (95% CI, 4.9-11.6), respectively. ProsVue univariate hazard ratio (95% CI) for prostate cancer-specific survival was 20.6 (6.8-62.7), with p<.0001 for a ProsVue result >2.0 pg/mL/mo versus a result ≤2.0 pg/mL/mo. ProsVue multivariate hazard ratio adjusted by CAPRA-S nomogram was 16.7 [4.7-58.6]; p<.0001. Based on 18 events, salvage treatment for biochemical recurrence did not significantly reduce the hazard of clinical recurrence or prostate cancer-specific mortality.
 
In 2014, Moul and colleagues reported on the prospective enrollment of men treated by radical prostatectomy into a multicenter trial, in order to assess the clinical utility of ProsVue PSA slope results (Moul, 2014b). At post-surgical follow-up, men were stratified into low-, intermediate-, or high-risk groups for cancer recurrence based on clinicopathologic findings and other findings. Three serial serum samples for ProsVue testing were collected. Investigators recorded whether their initial treatment plan was changed after the ProsVue result was reported. Of 225 men, 128 (57%) were stratified into intermediate and high risk groups. Investigators reported that they would have referred 41/128 (32%) of these men for secondary treatment, but that after the ProsVue result was reported, they referred 15/128 (12%) of men.
 
It is unknown whether the NADiA ProsVue after radical prostatectomy results in improved health outcomes, and there is no evidence to demonstrate incremental predictive value over other variables such as Gleason score or independent PSA levels.
 
2017 Update
A literature search conducted using the MEDLINE database through November 2017 did not reveal any new information that would prompt a change in the coverage statement.
 
2018 Update
A literature search was conducted through November 2018.  There was no new information identified that would prompt a change in the coverage statement.  
 
2021 Update
Annual policy review completed with a literature search using the MEDLINE database through November 2021. No new literature was identified that would prompt a change in the coverage statement.
 
2022 Update
Annual policy review completed with a literature search using the MEDLINE database through November 2022. No new literature was identified that would prompt a change in the coverage statement.
 
2023 Update
Annual policy review completed with a literature search using the MEDLINE database through November 2023. No new literature was identified that would prompt a change in the coverage statement.
CPT/HCPCS:
88323Consultation and report on referred material requiring preparation of slides
88350Immunofluorescence, per specimen; each additional single antibody stain procedure (List separately in addition to code for primary procedure)
88399Unlisted surgical pathology procedure
References: Cordon-Cardo C, Kotsianti A, et al.(2007) Improved prediction of prostate cancer recurrence through systems pathology. J Clin Invest, 2007; 117(7):1876-83.

Donovan MJ, Hamann S, et al.(2008) Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy. J Clin Oncol, 2008; 26(24):3923-9.

Donovan MJ, Khan FM, Bayer-Zubek V et al.(2012) A systems-based modelling approach using transurethral resection of the prostate (TURP) specimens yielded incremental prognostic significance to Gleason when predicting long-term outcome in men with localized prostate cancer. BJU Int 2012a; 109(2):207-13.

Donovan MJ, Khan FM, et al.(2009) Personalized prediction of tumor response and cancer progression on prostate needle biopsy. J Urol, 2009; 182(1):125-32.

Donovan MJ, Khan FM, Powell D et al.(2012) Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10-year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery. BJU Int 2012b; 109(1):40-5.

Donovan MJ, Osman I, Khan FM.(2010) Androgen receptor expression is associated with prostate cancer-specific survival in castrate patients with metastatic disease. BJU Int 2010; 105(4):462-7.

Eggener SE, Vickers AJ, et al.(2009) Comparison of models to predict clinical failure after radical prostatectomy. Cancer, 2009; 115(2):303-10.

Klein EA, Stephenson AJ, et al.(2008) Systems pathology and predicting outcome after radical prostatectomy. J Clin Oncol, 2008; 26(24):3916-7.

Moul JW, Chen DY, Trabulsi EJ, et al.(2014) Impact of NADiA ProsVue PSA slope on secondary treatment decisions after radical prostatectomy. Prostate Cancer Prostatic Dis. Sep 2014b;17(3):280-285. PMID 25027863

Moul JW, Lilja H, Semmes OJ et al.(2012) NADiA ProsVue prostate-specific antigen slope is an independent prognostic marker for identifying men at reduced risk of clinical recurrence of prostate cancer after radical prostatectomy. Urology 2012; 80(6):1319-25.

Moul JW, Sarno MJ, McDermed JE, et al.(2014) NADiA ProsVue prostate-specific antigen slope, CAPRA-S, and prostate cancer--specific survival after radical prostatectomy. Urology. Dec 2014a;84(6):1427-1432. PMID 25432832

Shariat SF, Karakiewicz PI, et al.(2008) Inventory of prostate cancer predictive tools. Curr Opin Urol, 2008; 18(3):279-96.

Shariat SF, Kattan MW, et al.(2009) Critical review of prostate cancer predictive tools. Future Oncol, 2009; 5(10):1555-84.

Veltri RW, Miller MC, et al.(2008) Prediction of prostate-specific antigen recurrence in men with long-term follow-up postprostatectomy using quantitative nuclear morphometry. Cancer Epidemiol Biomarkers Prev, 2008; 17(1):102-10.
Group specific policy will supersede this policy when applicable. This policy does not apply to the Wal-Mart Associates Group Health Plan participants or to the Tyson Group Health Plan participants.
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