Biomarkers for Oncology

This LCD supplements but does not replace, modify or supersede existing Medicare applicable National Coverage Determinations (NCDs) or payment policy rules and regulations for biomarkers for oncology services. Federal statute and subsequent Medicare regulations regarding provision and payment for medical services are lengthy. They are not repeated in this LCD. Neither Medicare payment policy rules nor this LCD replace, modify or supersede applicable state statutes regarding medical practice or other health practice professions acts, definitions and/or scopes of practice. All providers who report services for Medicare payment must fully understand and follow all existing laws, regulations and rules for Medicare payment for biomarkers for oncology services and must properly submit only valid claims for them. Please review and understand them and apply the medical necessity provisions in the policy within the context of the manual rules. Relevant CMS manual instructions and policies may be found in the following Internet-Only Manuals (IOMs) published on the CMS Web site.

IOM Citations:

• CMS IOM Publication 100-02, Medicare Benefit Policy Manual,
  • Chapter 15, Section 80.1 Clinical Laboratory Services
• CMS IOM Publication 100-03, Medicare National Coverage Determinations (NCD) Manual,
  • Chapter 1, Part 2, Section 90.2 Next-Generation Sequencing for Patients with Advanced Cancer
  • Chapter 1, Part 4, Section 210.3 Colorectal Cancer Screening Tests
• CMS IOM Publication 100-08, Medicare Program Integrity Manual,
  • Chapter 3, Section 3.4.1.3 Diagnosis Code Requirements, Section 3.6.2.3 Limitations of Liability Determinations
  • Chapter 13, Section 13.5.4 Reasonable and Necessary Provisions in an LCD 

Social Security Act (Title XVIII) Standard References:

• Title XVIII of the Social Security Act, Section 1862(a)(1)(A) states that no Medicare payment shall be made for items or services which are not reasonable and necessary for the diagnosis or treatment of illness or injury.
• Title XVIII of the Social Security Act, Section 1862(a)(7). This section excludes routine physical examinations.

Code of Federal Regulations (CFR) References:

• CFR, Title 42, Volume 2, Chapter IV, Part 410.32(d)(3) Diagnostic x-ray tests, diagnostic laboratory tests, and other diagnostic tests: Conditions.

Compliance with the provisions in this policy may be monitored and addressed through post payment data analysis and subsequent medical review audits.

History/Background and/or General Information

The emergence of personalized laboratory medicine has been characterized by a multitude of testing options which can more precisely pinpoint management needs of individual patients. As a result, the growing compendium of products described as biomarkers requires careful evaluation by both clinicians and laboratorians as to what testing configurations are reasonable and necessary under the Medicare Act. There are a plethora of burgeoning tools, including both gene-based (genomic) and protein-based (proteomic) assay formats, in tandem with more conventional (longstanding) flow cytometric, cytogenetic, etc. biomarkers. Classified somewhat differently, there are highly-diverse approaches ranging from single mutation biomarkers to multiple biomarker platforms, the latter of which often depend upon sophisticated biomathematical interpretative algorithms.

The term “biomarker” refers to a broad subcategory of medical signs (i.e., objective indications of medical state observed from outside the patient) which can be measured accurately and reproducibly. Medical signs stand in contrast to medical symptoms, which are limited to indications of health or illness perceived by the patient. In 1998, the National Institute of Health (NIH) defined a biomarker as: "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes pathogenic processes, or pharmacologic response to a therapeutic intervention."

This current LCD focuses upon selected testing in oncology, with some emphasis upon applying the revised 2016 CPT molecular coding format. The LCD primarily applies to molecular biomarker testing but does involve some other types of related biomarker testing, such as proteomics.

There are separate Local Coverage Determinations (LCDs) that address other biomarkers, which include a multitude of assays which are not specifically discussed below. (Please refer to the Novitas website at www.novitas-solutions.com for a complete listing of LCDs.)

Local Medicare coverage of such biomarkers must be predicated upon four fundamental principles:

1. First, the biomarkers must have proven clinical validity/utility (CVU). 
2. Second, to support the medical necessity of the service, there must be acceptance/uptake of specific testing into patient management. It is essential that physicians be familiar enough with all specific biomarkers, which they order, such that all test results may become clinically actionable.
3. Providers managing oncological conditions must demonstrate that the use of biomarkers will be used to assist in the management/treatment of the beneficiary.
4. Peer-reviewed full manuscript evidence is required to support combination panels for multiple biomarkers, particularly regarding their alleged composite clinical validity/utility. For example; such potential billing for multiple, diverse biomarkers (e.g., diagnostic/monitoring/prognostic/predictive) can only achieve medical necessity when it is clearly evident how each requested biomarker can be individually contributory.

It is useful to categorize oncology biomarkers into functional clusters which reflect both (1) The predominant intent of testing (with the caveat that individual assays may cross over into more than one category) and (2) the relative evidentiary expectations:

1. Oncology Biomarkers Used for Diagnosis/Classification/Monitoring/Surveillance: These types of assays are supportable by case-control sensitivity/specificity studies, with appropriate designs in place to minimize the extent of bias and confounding.
2. Oncology Biomarkers Used for Prognosis/Prediction: Oncology biomarkers used for prognosis/prediction (i.e., a predictive biomarker is associated with response [benefit] or lack of response to a particular therapy, relative to other available therapy, whereas a prognostic biomarker provides information on the likely outcome of the disease in an untreated individual).

There is a complex and diverse set of study methods which can drive the robust formulation of evidence for such esoteric testing, which are well-summarized by Deverka et al. at the Center for Medical Technology Policy, but there are currently NO standardized thresholds or benchmarks for evaluating the CVU/medical necessity of emerging biomarkers. However, the following sources (although not exhaustive and complete) may help support CVU when requesting reconsideration for coverage of biomarkers that are not included in this LCD:

1. FDA labeling documentation.
2. National Comprehensive Cancer Network (NCCN) Biomarkers Compendium recommendations, particularly where Category 1 evidence is noted.
3. Findings from well-established, independent technology assessments (e.g., Evaluation of Genomic Applications in Practice and Prevention [EGAPP], Agency for Healthcare Research and Quality [AHRQ], Blue Cross and Blue Shield Association Technology Evaluation Center [BCBSA TEC] and the Cochrane Collaboration).
4. Other independent, objective evaluations or systematic literature reviews, which can substantively contribute to the evidence base, including, but not restricted to, emerging National Institutes of Health (National Cancer Institute) guidelines for the accrual of genomics/proteomics clinical validity/utility evidence. Although there is not a prescriptive format for such systematic reviews, the documentation (submitted to Novitas) for reconsideration purposes should include the following three elements:
   • Some type of recurring/periodic Committee structure, which is comprised of at least qualified biomathematicians/methodologists, molecular pathology laboratory specialists and relevant clinicians (e.g., oncologists).
   • Evidence of active sharing of the critical evaluations in a manner that enables sufficiently broad input into this process, and a feasibly wide acceptance of this process by representative molecular pathology stakeholders. There is no preference between such a Committee being based at a single site, or even rotating among several sites.
   • Transparency of the biomarker evaluations via minutes (or a summary of minutes).

Covered Indications

MOLECULAR TESTS

Covered clinical types of application(s) are identified below as diagnostic (DX), prognostic (PROG) or predictive (PRED).

1.  Colorectal Cancer

• • KRAS (12/13) - PRED of resistance to an anti-EGFR agent
  • KRAS codon 61 - PRED of resistance to an anti-EGFR agent
  • KRAS codon 146 - PRED of resistance to an anti-EGFR agent
  • NRAS - PRED of resistance to an anti-EGFR agent
  • BRAF - PRED of resistance to an anti-EGFR agent + DX (sporadic vs. Lynch syndrome)
  • PIK3CA - PRED of resistance to an anti-EGFR agent + PROG for local recurrence
  • MSI by PCR - PRED of 5-FU resistance + DX
  • MLH1 promoter hypermethylation - PRED of 5-FU resistance + DX
  • mRNA (oncotype-Colon) – PRED for the recurrence risk for patients with Stage II colon cancer
  • Hereditary colon cancer disorders
  • Sept9

ColonSeq®

This testing provides information to the patient and provider regarding potential treatment options and implications for RAS and BRAF mutations.

Please refer to DA52986-Billing and Coding: Biomarkers for Oncology regarding coding and billing information.

2.  Non-Small Cell Lung Cancer (NSCLC)

• EGFR- PRED of anti-EGFR response
• KRAS (12/13) - PRED of anti-EGFR resistance
• KRAS codon 61 - PRED of anti-EGFR resistance
• KRAS codon 146 - PRED of anti-EGFR resistance
• BRAF - PROG + PRED for anti-RAF inhibitor
  
  ThermoFisher Oncomine DX Target Test for Non-Small Cell Lung Cancer (NSCLC) is a 23 gene panel including a 3 gene target test (companion test) approved by the FDA in June 2017 for NSCLC from tissue specimens.  It can simultaneously identify the three gene variants that are a key to targeted therapy selection: BRAF and ROS1, and EGFR.  The targeted therapies are dabrafenib (Tafinlar) in combination with trametinib (Mekinist), crizotinib (Xalkori), and gefitinib (Iressa), respectively.  These three drugs are approved therapies for NSCLC patients with the above gene variants. Oncomine DX Target Test is the only FDA approved companion test that detects ROS1 fusions and that detects BRAF V600E, but it does not detect ALK fusions. Coverage is limited as specified in NCD 90.2, Next Generation Sequencing (NGS) for Patients with Advanced Cancer. Please refer to the NCD at https://www.cms.gov/medicare-coverage-database/details/ncd-details.aspx?NCDId=372&ncdver=1&bc=AAAAQAAAAAAA for full coverage details.

LungSeq®

Testing for genetic alteration in these genes can determine targeted therapy options that have the potential to decrease tumor burden, decrease symptoms, increase survival, and dramatically improve the quality of life for patients with specific genetic alterations.

Please refer to A52986, Billing and Coding: Biomarkers for Oncology regarding coding and billing information.

3.  Melanoma

• BRAF - PRED of response to Vemurafenib
• KIT - PRED of response to Imatinib (TKI)
• NRAS - PROG + PRED for anti-MEK inhibitor
•  
  4.  Uveal Melanoma
• GNAQ – PROG
• GNA11 - PROG 

5.  Brain

• BRAF - PRED
• EGFR - PRED
• MGMT - PRED
• IDH1 - DX + PROG
• IDH2 - DX + PROG
• PIK3CA - PRED
• PTEN - PRED
• CIMP - PRED
• TERT - DX

6.  Thyroid

• BRAF - DX + PRED
• KRAS - PRED for Selumetinib
• HRAS - PRED for Selumetinib
• NRAS - PRED for Selumetinib
• PIK3CA - PRED
• RET - DX
• PAX8/PPARG- DX

ThyraMIR Thyroid miRNA classifier (aPCR based microRNA gene expression classifier) (PRED) evaluates the expression levels of 10miRNA genes within an FNA biopsy: miR-29b-1-5p, miR-31-5p, miR-138-1-3p, miR-139-5p, miR-146b-5p, miR-155, miR-204-5p, miR-222-3p, miR-375, and miR-551b-3p.

Oncology Thyroid, provides gene expression analysis of 142 genes utilizing fine needle aspirate, algorithm reported as a categorical result (Afirma - PRED).

ThyraMIR is used as a companion test to ThyGeNEXT when ThyGeNEXT results are inconclusive.

• ThyraMIR, ThyGeNEXT and Afirma services will be considered reasonable and necessary for patients with any of the following conditions:
  
  
  • An indeterminate pathology on fine needle aspiration
  • Patients with one or more thyroid nodules with a history or characteristics suggesting malignancy such as:
    
    • Nodule growth over time
    • Family history of thyroid cancer
    • Hoarseness, difficulty swallowing or breathing
    • History of exposure to ionizing radiation
    • Hard nodule compared with rest of gland consistency
    • Presence of cervical adenopathy
• RosettaGX Reveal thyroid MicroRNA test, an assay used for the classification of indeterminate thyroid nodules, will be considered reasonable and necessary when the conditions outlined above for ThyraMIR, ThyGeNEXT and Afirma are met.
• ThyroSeq is a test utilized to better define the need for thyroid surgery and the type of such surgery. ThyroSeq will be considered reasonable and necessary when the conditions outlined above for ThyraMir, ThyGeNEXT and Afirma are met.

7.  Ovary/Fallopian Tube/Peritoneum

• AKT1 - PRED for PI3K/AKT/mTOR inhibitors
• BRAF - DX + PROG
• KRAS - DX + PROG
• MLH1 promoter hypermethylation - DX
• MSI by PCR - DX
• PIK3CA - PRED for PI3K/AKT/mTOR inhibitors
• PTEN - PRED for PI3K/AKT/mTOR inhibitors
• TP53 - DX + PROG

8.  Uterus

• AKT1 - PRED for PI3K/AKT/mTOR inhibitors
• BRAF - PRED
• KRAS - PRED
• MLH1 promoter hypermethylation - DX
• MSI by PCR - DX
• PIK3CA - PRED for PI3K/AKT/mTOR inhibitors
• PTEN - PRED for PI3K/AKT/mTOR inhibitors + DX + PROG
• TP53 - DX + PROG

9.  Urinary Tract

• FGFR3 - PROG
• MSI by PCR - DX
• MLH1 promoter hypermethylation - DX

10.  Prostate

• The PROGENSA® PCA3 Assay (PRED) is an FDA-approved, automated molecular test (assay) that helps physicians determine the need for repeat prostate biopsies in men who have had a previous negative biopsy.
• PTEN – PROG and THER
• RB1 – DX and PROG
• TP53 - PROG

11.  Gastrointestinal Stomal Tumor

• KIT - PRED for Sumatinib + DX
• PDGFRA - PRED for Sumatinib + DX

12.  Cancer of Unknown Primary (CUP)

Molecular testing, using the Rosetta Cancer Origin Test™ (PROG), is considered reasonable and necessary in the pathologic diagnoses of CUP when a conventional surgical pathology/imaging work-up is unable to identify a primary neoplastic site. Other applications of this technology are considered not reasonable and necessary and are considered investigational in the use of diagnosis of specific tumor types such as NSCLC and renal cancers.

TUO CTID (Cancer Type ID) (DX) is considered reasonable and necessary in the pathologic diagnoses of CUP when a conventional surgical pathology/imaging work-up is unable to identify a primary neoplastic site. Other applications of this technology are considered not reasonable and necessary and are considered investigational in the use of diagnosis of specific tumor types such as NSCLC and renal cancers.

13.  Leukemias and Lymphomas

• Acute lymphoid leukemia (ALL)
  
  • JAK1
  • JAK2
  • BCR/ABL1 - DX
  • ABL1 (kinase domain) - PROG
  • IGH - DX
  • TCRB - DX
  • TCRG - DX
  • TP53 - PROG
  • MLL/AF4 - DX
  • E2A/PBX1 - DX
  • ETV6/RUNX1 - DX
  • KRAS
  • NRAS
  • NOTCH1
  • FBXW7
• Acute myeloid leukemia (AML, and including acute promyelocytic leukemia): All PROG, except where noted below.
  
  • TP53
  • PML/RARA - DX
  • RUNX1/RUNX1T1 - DX
  • CBFB/MYH11 - DX
  • FLT3 ITD
  • FLT3 D835
  • NPM1
  • KRAS
  • NRAS
  • KIT
  • CEBPA
  • IDH1
  • IDH2
  • DNMT3A
  • JAK2 (p.V617F)
  • JAK2 (exon 12)
  • MPL
  • DEK/CAN - DX
  • ASXL1
  • EZH2
  • TET2
  • PML/RARalpha
  • U2AF1
  • SRSF2
  • ZRSR2
• Hairy cell leukemia
  
  • BRAF
  • IGH somatic hypermutation - PROG
  • IGH - DX
• Aplastic anemia
  
  • TCRB - DX
  • TCRG - DX
• Burkitt’s lymphoma
  
  • IGH - DX
  • TP53 - PROG
• Myeloproliferative diseases (MPD - essential thrombocytosis [ET], myelofibrosis & polycythemia vera [PV])
  
  • KIT
  • TP53
  • BCR/ABL1 - DX
  • JAK2 (p.V617F) - DX
  • JAK2 (exon 12) - DX
  • MPL - DX
  • CALR - DX
  • CSF3R - DX
  • ASXL1 - PROG
  • TET2 - PROG
  • EZH2 - PROG
  • Calr (exon 9)
• Chronic myeloid leukemia (CML) and chronic myelomonocytic leukemia (CMML)
  
  • ABL1 T3151 – CML only
  • KRAS - PROG
  • NRAS - PROG
  • BCR/ABL1 - DX
  • ABL1 (kinase domain) - PRED for Imatinib
  • FLT3 ITD - PROG
  • FLT3 D835 - PROG
  • KIT - PROG
  • JAK2 (p.V617F) - PROG
  • JAK2 (exon 12) - PROG
• Chronic lymphoid leukemia (CLL)
  
  • IGH - DX
  • IGH somatic hypermutation - PROG
  • TP53 - PROG
  • IGH direct probe method
  • BTK
  • PLCG2
  • BIRC3
  • BTK
  • NOTCH1
  • SF3B1
• Follicular lymphoma
  • IGH/BCL2 - DX
• Hypereosinophilia Syndrome (HES)
  
  • KIT (including p.D816V) - PROG + DX
  • FIP1L1/PDGFRA Fusion - DX
• Mantle cell lymphoma
  
  • CCND1/IGH - DX
• Mastocytosis
  
  • KIT (including p.D816V) - PROG + DX
  • FIP1L1/PDGFRA Fusion - DX
  • TCRG - DX
• T-cell prolymphocytic leukemia
  
  • JAK1
  • JAK3
  • TCRB - DX
  • TCRG - DX
• T-cell large granular lymphocytic leukemia(TLGLL)
  • STAT3
  • STAT5B
• Myelodysplastic syndrome (MDS): All below biomarkers are PROG.
  
  • FLT3 ITD
  • FLT3 D835
  • NPM1
  • KRAS
  • NRAS
  • KIT
  • CEBPA
  • IDH1
  • IDH2
  • DNMT3A
  • JAK2 (p.V617F)
  • JAK2 (exon 12)
  • MPL
  • ASXL1
  • EZH2
  • TET2
• Cytogenomic microarray analysis, or alternatively, a single nucleotide polymorphism (SNP) array for the same testing, is covered for the identification of various mutations. These tests are used in the diagnosis/prognosis of various hematological malignancies.
  
  
• Waldenstrom's Lymphoplasmacytic Lymphoma
  • MYD88

14.  Myeloma Gene Expression Profile (MyPRS) (PROG) isolates plasma cells from myeloma patients, extracts DNA, which is then subjected to MicroArray testing and application of validated software programs to identifying patterns of genetic abnormalities. Seventy highly predictive genes have been identified and correlated to myeloma early relapse. MyPRS gives a predictive risk signature as high-risk or low-risk at this time. A high risk score predicts a less than 20% three-year complete remission where as a low-risk predicts a five-year complete remission of greater than 60%. The predictive value for the stratification of therapeutic interventions allows these patients to be treated in a more personalized manner based on their own genetic profile.

This test is considered reasonable and necessary only after the initial diagnosis of multiple myeloma has been made and will be available to be used in the stratification of therapeutic interventions. It would be inappropriate to use this test as a diagnostic tool or as a monitoring device of ongoing therapy. Other testing is available for this function.

The coverage is set to include only two clinical settings:

• Once after initial diagnosis is made. In the event MyPRS was not tested at diagnosis of myeloma and there is ongoing initial therapy with persistent disease, MyPRS can be done still as an initial test.
  
  OR
  
  
• If relapse has occurred and a change in the therapeutic modalities is contemplated.

Please refer to the limitations section of this policy for frequency limitations.

15.  Hereditary neuroendocrine tumor disorders - Must include 6 genes with genomic sequence analysis NEX GEN including:

• • MAX
  • SDHB
  • SDHC
  • SDHD
  • TMEM127
  • VHL

Please refer to the limitations section of this policy for frequency limitations.

16.  Hereditary neuroendocrine tumor disorders; duplication/deletion analysis panel - must include analysis for:

• • SDHB
  • SDHC
  • SDHD
  • VHL

17. Neuroendocrine Tumors

• • MGMT - PROG
  • PTEN – PROG and THER
  • RB1 – DX and PROG
  • TP53 – DX and PROG
  • TSC2 - PROG

18.  Prosigna breast cancer gene signature assay (PROG)

Background

Women with early breast cancer and up to 3 locally positive lymph nodes whose tumor is estrogen-receptor positive will usually receive anti-hormonal therapy such as tamoxifen or aromatase inhibitors. U.S. (NCCN) and international (St. Gallen) guidelines predicate the decision for adjuvant chemotherapy on the size and grade of the breast cancer and other factors including genomic assays that provide additional information on risk of recurrence (Hernandez-Ava et al., 2013). According to a 2014 review, “Prognostic factors provide an indication of whether a patient needs subsequent therapy.” (Paoletti & Hayes, 2014). Similarly, another 2014 review article states, “Efforts should be focused on reducing chemotherapy in patients unlikely to benefit.” (Rampurwala et al., 2014).

The PAM50 breast cancer gene signature test was developed in the late 1990s and initial studies showed a strong correlation with breast cancer recurrence and with complete pathologic response to neoadjuvant chemotherapy (Parker et al., 2009). While test results are reported on a scale of 1-100 as a Risk of Recurrence (ROR) score, the underlying algorithm is also able to classify cases into the luminal A and B, Her2neu, and triple-negative subtype classifications.

The Nanostring nCounter® nucleic acid analysis system replicates the PAM50 algorithm, as an FDA cleared kit, the Prosigna Breast Cancer Gene Signature Assay (FDA, 2013). The Prosigna package insert was most recently updated in January, 2015 (FDA, 2015) reflecting additional studies (Sestak et al., 2014). Notably, the Prosigna platform and the original PAM50 platform have a 0.997 correlation (Dowsett et al., 2013).

For the FDA, the Prosigna test was validated in a large population of post-menopausal, estrogen-receptor positive women based on 1,017 cases of the TransATAC study (Dowsett et al., 2013). The study showed a strong correlation with long-term breast cancer recurrence and added substantial additional prognostic information over a clinical treatment score based on standard clinical variables. This study was replicated in an independent population, also on the Prosigna test, using 1,620 samples from the ABCSG8 trial (Gnant, 2014). A separate analysis of these trials validated prediction of distant recurrence in years 5-10 after initial diagnosis (Sestak et al., 2014) and has been incorporated in the FDA labeling (FDA, 2015). The Prosigna test is issued as separate reports, consistent with FDA review and labeling, for node-negative and node-positive (1-3 node) populations. Analytic performance, precision, reproducibility, and analysis of the clinical validations are provided in the FDA labeling (https://www.accessdata.fda.gov/cdrh_docs/reviews/K130010.pdf).

Clinical utility of this breast cancer gene signature has also been assessed. The study of Martin et al. (2015) showed a 20% decision impact on decisions for or against adjuvant chemotherapy in an all-comers population of 200 new cases of incident breast cancer, when Prosigna test information became available after all other clinical information had been considered. The net rates of selecting adjuvant chemotherapy for low, intermediate, and high risk cases was similar to that observed in a meta-analysis of Oncotype DX decision data (Carlson & Roth, 2013). Additional support for the use of these test results in treatment decisions comes from Parker et al. (2009), in which there was a strong association with neoadjuvant chemotherapy response. Low-scoring cases have a very low chance of complete pathological response to neoadjuvant chemotherapy, while high-scoring cases approach a 50% chance of complete pathological response. The same findings have been observed for other breast cancer gene signatures based on prognostic algorithms (Chang et al., 2008). 

The Prosigna test is reasonable and necessary when performed according to the FDA label (https://www.accessdata.fda.gov/cdrh_docs/reviews/K130010.pdf).

19.  Desmoid Fibromatosi

• • CTNNB1 – DX and PROG

20. Hepatic Adenoma

• • CTNNB1 – DX and PROG

21.  Bladder

• • CDKN2A – PROG
  • FGFR1 – PROG
  • FGFR3 – PROG
  • MTOR – PROG
  • PIK3CA – DX and PROG
  • PTEN – PROG
  • RB1 – PROG
  • TP53 - PROG

NON-MOLECULAR ASSAYS

1. The VeriStrat® assay is a mass spectrophotometric, serum-based predictive proteomics assay for NSCLC patients, where “first line” EGFR mutation testing is either wild-type or not able to be tested (e.g., if tissue might not be available). This test is a driver of therapy, most notably EGFR inhibitors such as erlotinib, and it has been validated by randomized controlled studies (Carbone et al. and Stinchecomb et al.) and physician uptake data (Akerley et al.) to support this particular coverage niche.
   
   
2. This LCD does not address ALK and ROS1 FISH assays, which are indicated as predictive biomarkers for Crizotinib therapy, since they are currently covered assays. However, it is expected that non-molecular testing for these two biomarkers should provide adequate predictive information.
   
   
3. FISH tests for bladder cancer are complex tests based on precision reagents, controls, and mathematical algorithms, all of which must be validated in clinical trials in order to support cutoff points for critical patient care decisions. Therefore, in each local physician’s office or laboratory, this category of testing is not easily replicated by miscellaneous research use or ASR reagents. Novitas will consider the coverage of FISH test kits based on peer-reviewed literature and approved manufacturer claims.
   
   
4. Although multiple bladder cancer FISH tests may be covered according to the above general criteria, UroVysion^TM Bladder Cancer Kit (UroVysion™ Kit) will be considered medically reasonable and necessary only when performed according to the FDA label (https://www.accessdata.fda.gov/cdrh_docs/pdf3/P030052b.pdf) as follows:
   
   The UroVysion Bladder Cancer Kit (UroVysion™ Kit) is designed to detect aneuploidy for chromosomes 3, 7, 17, and loss of the 9p2l locus via fluorescence in situ hybridization (FISH) in urine specimens from persons with hematuria suspected of having bladder cancer. Results from the UroVysion Kit are intended for use, in conjunction with and not in lieu of current standard diagnostic procedures, as an aid for initial diagnosis of bladder carcinoma in patients with hematuria and subsequent monitoring for tumor recurrence in patients previously diagnosed with bladder cancer.
   
   
5.  The OVA1™ proteomic assay (PROG) will be considered reasonable and necessary when performed according to the FDA label (https://www.accessdata.fda.gov/cdrh_docs/pdf15/K150588.pdf).
   
   
6. The Risk of Ovarian Malignancy Algorithm (ROMA™) is a qualitative serum test (PROG) that combines the results of HE4 EIA, ARCHITECT CA 125 II ™ and menopausal status into a numerical score. ROMA™ is intended (per FDA clearance) to aid in assessing whether a premenopausal or postmenopausal woman who presents with an ovarian adnexal mass is at high or low likelihood of finding malignancy at surgery. ROMA™ will be considered reasonable and necessary for women who meet the FDA labeling criteria (https://www.accessdata.fda.gov/cdrh_docs/pdf10/K103358.pdf).
   
   

Limitations

Note: Please refer to the indications for any restrictions specific to the various assays. Please see NCD 90.2 for coverage details related to Next Generation Sequencing (NGS) for Patients with Advanced Cancer.

1. Most genomic testing should be a once in a lifetime test. Documentation in the medical record should clearly support the need for repeat testing to include the following: recurrence of disease, change in behavior of disease, etc.
   
   
2. The following tests will all be covered once per lifetime per beneficiary:
   • Brain Molecular Biomarkers
   • Hereditary neuroendocrine tumor disorders
   • Hereditary neuroendocrine tumor disorders; duplication/deletion analysis
   • ThyraMIR, Afirma, ThyGeNEXT, RosettaGX Reveal and ThyroSeq tests
     • Should the unlikely situation of a second, unrelated thyroid nodule with indetermindate pathology occur, coverage may be considered upon appeal with supporting documentation
   • TUO CTID (Caner TYPE ID)
     
     
3. While some biomarkers have utility for testing once per lifetime, there are some tumor specific scenarios where repeat testing would be needed for assessment of response to therapy or to identify basis of disease progression. In cases with metastatic or recurrent tumors, repeat testing may be useful in determining further clinical management. Also, biomarkers such as BCR-ABL1 fusion, PML-RARA fusion are useful in monitoring response to therapy and predict a response up to four times per annum.

Notice: Services performed for any given diagnosis must meet all of the indications and limitations stated in this policy, the general requirements for medical necessity as stated in CMS payment policy manuals, any and all existing CMS national coverage determinations, and all Medicare payment rules.

Please refer to the “History/Background and/or General Information” section for general information on biomarkers.

Multiple sources of literature were submitted for consideration. The literature consisted of various investigational, observational, and experimental studies, as well as some letters to the editor in support of the leukemia biomarkers expansion, and ThyGeNEXT panel. The literature was reviewed; the following is a summary of the evidence submitted:

Numerous articles were submitted in support of the Leukemia biomarker expansion request. Taking into consideration the independent, objective evaluation and systematic literature review, which substantively contributed to the evidence base of the requested leukemia biomarkers and subsequently approved by the Molecular Testing Evaluation Committee (MTEC), all the requested biomarkers are considered reasonable and necessary for the listed disease states below:

• Acute Lymphoblastic Leukemia
  • JAK1
  • JAK2
  • KRAS
  • NRAS
  • NOTCH1
  • FBXW7
• Acute Myeloid Leukemia
  • TP53
  • U2AF1
  • SRSF2
  • ZRSR2
• Chronic Lymphocytic Leukemia
  • BIRC3
  • BTK
  • PLCG2
  • NOTCH1
  • SF3B1
• Chronic Myeloid Leukemia
  • ABL1 T315I
• Hairy Cell Leukemia
  • BRAF
• Myeloproliferative Diseases
  • KIT
  • TP53
• Prolymphocytic Leukemia
  • JAK1
  • JAK3
• T-cell Large Granular Lymphocytic Leukemia
  • STAT3
  • STAT5B

Consistent with the literature review, NCCN rating and quality of the evidence submitted, the following Biomarker Panel will be considered medically reasonable and necessary:

• ThyGeNEXT
  
  
  • Xing, et al (2014),¹ is a retrospective multicenter study that reviewed all known fusion and their prevalence in Papillary, poorly differentiated anaplastic, follicular, and medullary carcinomas. The study was a review and no new data was presented. The study conclusion demonstrates the prognostic value and perspectives of the utilization of gene fusions as therapeutic targets. The study conclusion is limited due to clinical utility not being achieved in reporting statistical findings, no available conflict of interest and no patient inhomogeneity. The quality of evidence for this study is moderate due to lack of peer review and the strength was conditional for the same reason.
    
    
  • Xing, et al (2015),² is a retrospective study to investigate the prognostic value of BRAF V600E mutation for the recurrence of papillary thyroid cancer in 2099 patients. The study conclusion demonstrates the overall BRAF V600E mutation prevalence was 48.5%. BRAF mutation was associated with poorer recurrence-free probability in Kaplan-Meier survival analyses in various clinicopathologic categories. The quality of evidence is high and the strength of recommendation is conditional for the population tested.
    
    
  • Labourier, et al (2015),³ is a cross-sectional cohort study conducted at 12 endocrinology centers across the United States. The study results found that mutations were detected with malignant outcome. Among mutation negative specimens, miRNA testing correctly identified 64% of malignant cases and 98% of benign cases. The diagnostic sensitivity and specificity of the combined algorithm was 89% (95% confidence intervals (CI): 73 – 97%) and 85% (95% CI: 75 – 92%), respectively. At 32% cancer prevalence, 61% of the molecular results were benign with a negative predictive value of 94% (95% CI: 85 – 98%). Independently of variations in cancer prevalence, the test increased the yield of true benign results by 65% relative to mRNA-based gene expression classification and decreased the rate of avoidable diagnostic surgeries by 69%. This was purely supposition. The authors concluded: multi-platform testing for DNA, mRNA and miRNA can accurately classify benign and malignant thyroid nodules, increase diagnostic yield of molecular cytology, and further improve the preoperative risk-based management of benign nodules with AUS/FLUS or FN/SFN cytology. The quality of evidence is moderate as this was not peer reviewed, a conflict of interest was present in that one of the authors was employed by the company, and the clinical utility is implied but not proven.
    
    
  • Giordano, et al (2014),⁴ is a case-control study conducted in 413 surgical cases comprising 17 distinct histopathologic categories. The study results found that, in the authors opinion, “standardized and validated multianalyte molecular panels can complement the preoperative and postoperative assessment of thyroid nodules and support a growing number of clinical and translational applications with potential diagnostic, prognostic, or theranostic utility.” The quality of evidence is moderate as this was a validation study only and the clinical utility is not addressed. There is an obvious conflict of interest in that the laboratory represented in authors of this study and the correspondence is through the laboratory.
    
    
  • Landa, et al (2013),⁵ is a retrospective study. The objectives of the study were: 1) to determine the prevalence of TERT promoter mutations C228T and C250T in different thyroid cancer histological types and cell lines; and 2) to establish the possible association of TERT mutations with mutations of BRAF, RAS, or RET/PTC. The study results found that TERT promoter mutations were found in 98 of 225 (44%) of specimens. TERT promoters C228T and C250T were mutually exclusive. The study conclusion demonstrates potential diagnostic, prognostic and therapeutic are suggested. TERT promoter mutations are highly prevalent in advanced thyroid cancers, particularly those harboring BRAF or RAS mutations which are most often TERT promoter wild type. Acquisition of a TERT promoter mutation could extend survival of BRAF- or RAS- driven clones and enable accumulation of additional genetic defects leading to disease progression. The quality of evidence is moderate as this is retrospective of variable tumor types and the clinical utility is only inferred.

Leukemia

The literature submitted for the addition of several biomarkers for various leukemia disease states was carefully reviewed. The literature submitted was also reviewed and approved by the Molecular Testing Evaluation Committee (MTEC). The MTEC reviews and votes on clinical requests for molecular testing based upon levels of evidence, including publications in the medical literature, and need for biomarkers in integral marker clinical trials. The MTEC is charged with establishing evidence-based standard-of-care testing, monitoring physician ordering of molecular tests, assuring documentation of medical necessity, analyzing utilization data, and reviewing outcomes data related to the use of molecular biomarkers. Taking into consideration the MTEC approval and taking into account item 4 of the General Information section of this policy, which allows for consideration of other independent, objective evaluations or systematic literature reviews, the requested leukemia biomarkers are considered reasonable and necessary.

ThyGeNEXT

The literature submitted for the requested addition of the ThyGeNEXT panel was carefully reviewed. After consideration of the literature, NCCN rating, and relevance to the Medicare population, the coverage of the ThyGeNEXT panel will be added in replace of the ThyGenX panel. The coverage of this panel is being added as there were enough genes requested in the panel achieving an NCCN 2A rating that when combined with the genes covered in the ThyGenX panel with a 2A rating, a minimum total of 51 genes achieved the NCCN rating of 2A. Further, the literature supported the clinical utility and clinical validity, and was relevant to the Medicare population.

Refer to the Local Coverage Article: Billing and Coding: Biomarkers for Oncology, (DA52986) for documentation requirements, utilization parameters and all coding information.

Contractor is not responsible for the continued availability of websites listed.

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Noridian Local Coverage Determination (LCD), DL36380 - MolDX: Breast Cancer Assay: Prosigna

Contractor Medical Directors

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2. Xing M, Alzahrani AS, Carson KA, et al. Association Between BRAF V600E Mutation and Recurrence of Papillary Thyroid Cancer. J Clin Oncol. 2015;33(1):42-50. doi:10.1200/JCO.2014.56.8253.
3. Labourier E, Shifrin A, Busseniers AE, et al. Molecular testing for miRNA, mRNA and DNA on fine needle aspiration improves the preoperative diagnosis of thyroid nodules with indeterminate cytology. J Clin Endocrinol Metab. 2015;100(7):2743-2750. doi:10.1210/jc.2015-1158.
4. Giordano TJ, Beaudenon-Huibregtse S, Shinde R, et al. Molecular testing for oncogenic gene mutations in thyroid lesions: a case-control validation study in 413 postsurgical specimens. Hum Pathol. 2014;45(7):1339-1347.
5. Landa I, Ganly I, Chan TA, et al. Frequent Somatic TERT Promoter Mutations in Thyroid Cancer: Higher Prevalence in Advanced Forms of the Disease. J Clin Endocrinol Metab. 2013;98(9):E1562-E1566. doi10.1210/jc.2013-2383.
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7. National Comprehensive Cancer Network (NCCN). NCCN Guidelines® & Clinical Resources: NCCN Categories of Evidence and Consensus. https://www.nccn.org/professionals/physician gls/categories of consensus.aspx. Accessed August 12, 2019.
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9. JAK1 NCCN 2A rating page for ALL.
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18. NCCN Biomarkers Compendium – TP53 for Acute Myeloid Leukemia
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29. NCCN ABL1 T315I for Chronic Myeloid Leukemia
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40. NCCN BRAF for Hairy Cell Leukemia
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42. Arcaini L, Zibellini S, Boveri E, et al. The BRAF V600E mutation in hairy cell leukemia and other mature B-cell neoplasms. Blood. 2016;119(1):188-191. doi:10.1182/blood-2011-08-368209.
43. Boyd EM, Bench AJ, van ‘t Veer MB, et al. High resolutions melting analysis for detection of BRAF exon 15 mutations in hairy cell leukaemia and other lymphoid malignancies. Br J Haematol. 2011;155(5):609-612. doi:10.1111/j.1365-2141.2011.08868.x.
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45. Arora N, Nair S, Pai R, et al. V-raf murine sarcoma viral oncogene homolog B (BRAF) mutations in hairy cell leukemia. Indian J Pathol Microbiol. 2015;58(1):62-65. Doi:10.4103/0733-4929.151190.
46. Rider T, Powell R, Gover R, et al. Molecular detection of BRAF-V600E is superior to flow cytometry for disease evaluation in hairy cell leukaemia. Hematol Oncol. 2014;32(3);158-161.doi:10.1002/hon.2097.
47. Shao H, Calvo KR, Gronborg M, et al. Distinguishing hairy cell leukemia variant from hairy cell leukemia: Development and validation of diagnostic criteria. Leuk Res. 2013;37(4):401-409. doi:10.1016/j.leukres.2012.11.021.
48. Dietrich S, Pircher A, Endris V, et al. BRAF inhibition in hairy cell leukemia with low-dose vemurafenib. Blood. 2016;127(23):2847-2855. doi:10.1182/blood-2015-11-680074.
49. Tiacci E, Park JH, De Carolis, L, et al. Targeting Mutant BRAF with Vemurafenib in Relapsed or Refractory Hairy Cell Leukemia. N Engl J Med. 2015;33(18):1733-1747. doi:10.1056/JEJMoal506583.
50. Vergote V, Dierickx D, Janssens A, et al. Rapid and complete hematological response or refractory hairy cell leukemia to the BRAF inhibitor dabrafenib. Ann Hematol. 2014;93(12):2087-2089. doi:10.1007/s00277-014-2104-2.
51. Pettirossi V, Santi A, Imperi E, et al. BRAF inhibitors reverse the unique molecular signature and phenotype of hairy cell leukemia and exert potent antileukemic activity. Blood. 2015;125(8):1207-1216. doi:10.1182/blood-2014-10-603100.
52. NCCN Biomarkers Compendium, 2A Rating for KIT and Myeloproliferative Neoplasms (MPN)
53. NCCN Biomarkers Compendium, 2A Rating for TP53 and Myeloproliferative Neoplasms (MPN)
54. Fontalba A, Real PJ, Fernandez-Luna JL, et al. Identification of c-Kit gene mutations in patients with polycythemia vera. Leuk Res. 2006;30(10):1325-1326. doi:10.1016/j.leukres.2005.12.020.
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57. Siitonen T, Savolainen ER, Koistlinen P. Expression of the C-Kit Proto-Oncogene in Myeloproliferative Disorders and Myelodysplastic Syndromes. Leukemia. 1994;8(4):631-637.
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59. Lundberg P, Karow A, Nienhold R, et al. Clonal evolution and clinical correlates of somatic mutations in myeloproliferative neoplasms. Blood. 2014;123(14):2220-2228. doi:10.1182/blood-2013-11-537167.
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63. NCCN Biomarkers Compendium, 2A Rating for STAT3 and T-cell Large Granular Lymphocytic Leukemia
64. NCCN Biomarkers Compendium, 2A Rating for STAT5B and T-cell Large Granular Lymphocytic Leukemia
65. Andersson E, Kuusanmäki H, Bortoluzzi S, et al. Activating somatic mutations outside the SH2-domain of STAT3 in LGL leukemia. Leukemia. 2016;30(5):1204-1208. doi:10.1038/leu.2015.263.
66. Bilori B, Thota S. Clemente MJ, et al. Tofacitinib as a novel salvage therapy for refractory T-cell large granular lymphocytic leukemia. Leukemia. 2015;29(12):2427-2429. doi:10.1038/leu.2015.280.
67. Fasan A, Kern W, Grossman V, et al. STAT3 mutations are highly specific for large granular lymphocytic leukemia. Leukemia. 2013;27(7):1598-1600. doi:10.1038/leu.2012.350.
68. Koskela HL, Eldfors S, Ellonen P, et al. Somatic STAT3 Mutations in Large Granular Lymphocytic Leukemia. N Engl J Med. 2012;366(20):1905-1913.
69. Kristensen T, Larsen M, Rewes A, et al. Clinical relevance of sensitive and quantitative STAT3 mutation analysis using next-generation sequencing in T-cell large granular lymphocytic leukemia. J Mol Diagn. 2014;16(4):382-392. doi:10.1016/j.jmoldx.2014.02.005.
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72. Qui ZY, Fan L, Wang R, et al. Methotrexate therapy of T-cell large granular lymphocytic leukemia impact of STAT3 mutation. Oncotarget. 2016;7(38):61419-61425.
73. Rajala HL, Olson T, Clemente MJ, et al. The analysis of clonal diversity and therapy responses using STAT3 mutations as a molecular marker in large granular lymphocytic leukemia. Haematologica. 2015;100(1):91-99. doi:10.3324/haematol.2014.113142.
74. Rajala HL, Porkka K, Maciejewski JP, et al. Uncovering the pathogenesis of large granular lymphocytic leukemia-novel STAT3 and STAT5b mutations. Ann Med. 2014;46(3):144-122. doi:10.3109/07853890.2014.882105.
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76. Shi M, He R, Feldman AL, et al. STAT3 mutation and its clinical and histopathologic correlation in T-cell large granular lymphocytic leukemia. Hum Pathol. Mar 2018;73:74-81. doi:10.1016/j.humpath.2017.12.014.
77. Andersson EI, Tanahashi T, Sekiguchi N, et al. High incidence of activating STAT5B mutations in CD4-positive T-cell large granular lymphocyte leukemia. Blood. 2016;128(20):2465-2468. doi:10.1182/blood-2016-06-724856.
78. Rajala HL, Eldfors S, Kuusanmaki H, et al. Discovery of somatic STAT5b mutations in large granular lymphocytic leukemia. Blood. 2013;121(22):4541-4550. doi:10.1182/blood-2012-12-474577.
79. Thol F, Kade S, Schlarmann C, et al. Frequency and prognostic impact of mutations in SRSF2, U2AF1 and ZRSR2 in patients with myelodysplastic syndromes. Blood. 2012;119(15):3578-3584. doi:10.1182/blood-2011-12-399337.
80. Hou HA, Liu CY, Kuo YY, et al. Splicing after mutations predict poor prognosis in patients with de novo acute myeloid leukemia. Oncotarget. 2016;7(8):9084-9101.
81. Damm F, Kosmider O, Gelsi-Boyer V, et al. Mutations affecting mRNA splicing define distinct clinical phenotypes and correlate with patient outcome in myelodysplastic syndromes. Blood. 2012;119(14):3211-3218. doi:10.1182/blood-2011-12-400994.
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83. Jones D, Woyach JA, Zhao W, et al. PLCG2 C2 domain mutations co-occur with BTK and PLCG2 resistance mutations in chronic lymphocytic leukemia undergoing ibrutinib treatment. Leukemia. 2017;31(7):1645-1647.
84. Trinquand A, Tanguy-Schmidt A, Ben Abdelali R, et al. Toward a NOTCH1/FBXW7/RAS/PTEN-based oncogenetic risk classification of adult T-cell acute lymphoblastic leukemia: a Group for Research in Adult Acute Lymphoblastic Leukemia study. J Clin Oncol. 2013 Dec 1;31(34):4333-42.
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88. NCCN Guidelines 4.2019 MS-13 Bladder Cancer
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90. Konety B, Shore N, Kader AK, et al. Evaluation of Cxbladder and Adjudication of Atypical Cytology and Equivocal Cystoscopy. European Urology 76. 2019:238-243. doi: 10.1016/j.eururo.2019.04.035.
91. Irving J, Matheson E, Minto L, et al. Ras pathway mutations are prevalent in relapsed childhood acute lymphoblastic leukemia and confer sensitivity to MEK inhibition. Blood. 2014;124(23):3420-3430. doi:10.1182/blood-2014-04-531871.
92. Oshima K, Khiabanian H, da Silva-Almeida AC, et al. Mutational landscape, clonal evolution patterns, and role of RAS mutations in relapsed acute lymphoblastic leukemia. PNAS. 2016;113(40):11306-11311. doi:10.1073/pnas.1608420113.
93. Trinquand A, Tanguy-Schmidt A, Abdelali, RB, et al. Toward a NOTCH1/FBXW7/RAS/PTEN-Based Oncogenetic Risk Classification of Adult T-Cell Acute Lymphoblastic Leukemia: A Group for Research in Adult Acute Lymphoblastic Leukemia Study. J Clin Oncol. 2013;31(34):4333-4342. doi:10.1200/JCO.2012.48.5292.
94. Irving JA, Enshaei A, Parker CA, et al. Integration of genetic and clinical risk factors improves prognostication in relapsed childhood B-cell precursor acute lymphoblastic leukemia. Blood. 2016;128(7):911-922. doi:10.1182/blood-2016-03-704973.
95. Asnafi V, Buzyn A, LeNoir S, et al. NOTCH1/FBXW7 mutation identifies a large subgroup with favorable outcome in adult T-cell acute lymphoblastic leukemia (T-ALL): a Group for Research on Adult Acute Lymphoblastic Leukemia (GRAALL) study. Blood. 2009;113(17):3918-3924. doi:10.1182/blood-2008-10-184069.
96. Abdelali RB, Asnafi V, Leguay T, et al. Pediatric-inspired intensified therapy of adult T-ALL reveals the favorable outcome of NOTCH/FBXW7 mutations, but not of low ERG/BAALC expression: a GRAALL study. Blood. 2011;118(19):5099-5107. doi:10.1182/blood-2011-02-334219.
97. Baldus CD, Thibaut J, Goekbuget, N, et al. Prognostic implications of NOTCH1 and FBXW7 mutations in adult acute T-lymphoblastic leukemia. Haematologica. 2009;94(10):1383-1390. doi:10.3324/haematol.2008.005272.
98. Breit S, Stanulla M, Flohr T, et al. Activating NOTCH1 mutations predict favorable early treatment response and long-term outcome in childhood precursor T-cell lymphoblastic leukemia. Blood. 2006;108(4):1151-1157. doi:10.1182/blood-2005-12-4956.
99. Jenkinson S, Koo K, Mansour MR, et al. Impact of NOTCH1/FBXW7 mutations on outcome in pediatric T-cell acute lymphoblastic leukemia patients treated on the MRC UKALL 2003 trial. Leukemia. 2013;27:41-47. doi:10.1038/leu.2012.176.
100. Liu RB, Guo JG, Liu TZ, et al. Meta-analysis of the clinical characteristics and prognostic relevance of NOTCH1 and FBXW7 mutation in T-cell acute lymphoblastic leukemia. Oncotarget. 2017;8(39):66360-66370.
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There were extensive in-person consultations with both CAC representatives and nationally-recognized experts in order to assist with the above medical necessity language and procedure-to-diagnosis code pairings.