Esophageal Pathology Testing - CAM 242HB

The esophagus is a long tube that serves to connect the mouth to the stomach. Although the esophagus is primarily a connecting organ, it experiences significant chemical and mechanical trauma. The esophagus has mechanisms and structures to withstand this damage, but molecular injury is common (Zhang et al., 2020). Both serological and genetic markers have been suggested to identify, diagnose, or assess risk in the esophagus. 

Eosinophilic esophagitis (EoE) is one such condition, as its nonspecific symptoms (pain, issues swallowing, vomiting, and so on) may be accompanied by inflammatory markers in the esophagus (Bonis & Gupta, 2021, 2023). Similarly, esophageal cancer is characterized by several nonspecific symptoms, while a predecessor condition, Barrett’s esophagus (BE), may have no clinical symptoms at all (Saltzman & Gibson, 2021; Spechler, 2023).

For guidance concerning Tumor Mutational Burden Testing (TMB) and/or Microsatellite instability (MSI) analysis please refer to the CAM 342 Microsatellite Instability and Tumor Mutational Burden Testing policy.

Regulatory Status
A search for “esophagus” on the FDA website on Dec. 16, 2019, yielded 0 relevant results. Additionally, many labs have developed specific tests that they must validate and perform in-house. These laboratory-developed tests (LDTs) are regulated by the Centers for Medicare & Medicaid Services (CMS) as high-complexity tests under the Clinical Laboratory Improvement Amendments of 1988 (CLIA ’88). As an LDT, the U.S. Food and Drug Administration has not approved or cleared this test; however, FDA clearance or approval is not currently required for clinical use.

Application of coverage criteria is dependent upon an individual’s benefit coverage at the time of the request.

  1. For individuals who have been newly diagnosed with cancer of the esophagus or esophagogastric junction (EGJ), mismatch repair (MMR) analysis by immunohistochemistry (IHC) is considered MEDICALLY NECESSARY.
  2. For individuals who have been diagnosed with locally advanced, recurrent, or metastatic cancer of the esophagus or EGJ and for whom PD-1 inhibitor treatment is being considered, tumor analysis of PD-L1 expression by IHC is considered MEDICALLY NECESSARY.
  3. For individuals who have been diagnosed with inoperable locally advanced, recurrent, or metastatic adenocarcinoma of the esophagus or EGJ and for whom trastuzumab or an approved biologic or biosimilar drug to trastuzumab is being considered for first-line therapy, HER2 overexpression testing by IHC, fluorescence in situ hybridization (FISH), or other in situ hybridization (ISH) is considered MEDICALLY NECESSARY.
  4. For individuals diagnosed with unresectable locally advanced, recurrent, or metastatic adenocarcinoma or squamous cell carcinoma of the esophagus or EGJ and for whom one of the following drugs is being considered as a second-line therapy, the corresponding gene testing is considered MEDICALLY NECESSARY:
    1. Larotrectinib or entrectinib: NTRK gene fusion.
    2. Selpercatinib: RET gene fusion.
    3. Dabrafenib or trametinib: BRAF V600E mutation.
  5. The use of genetic testing (e.g., molecular panel tests, gene expression profiling) to diagnose or monitor an individual with eosinophilic esophagitis (EoE) or to assess the risk of an individual developing EoE is considered NOT MEDICALLY NECESSARY.
  6. For the diagnosis and evaluation of Barrett’s esophagus, low-grade esophageal dysplasia, or high-grade esophageal dysplasia, wide-area transepithelial sampling (WATS) is considered NOT MEDICALLY NECESSARY.

The following does not meet coverage criteria due to a lack of available published scientific literature confirming that the test(s) is/are required and beneficial for the diagnosis and treatment of a patient’s illness.

  1. Assessing for risk of Barrett’s esophagus and/or esophageal, including esophagogastric junction, cancer using a molecular classifier (e.g., BarreGEN test) is considered NOT MEDICALLY NECESSARY.
  2. Epigenetic analysis for the likelihood for Barrett’s esophagus, esophageal, or esophagogastric junction cancer (e.g., methylation analysis, EsoGuard) is considered NOT MEDICALLY NECESSARY.
  3. To diagnose, assess, or monitor eosinophilic esophagitis (EoE), the Esophageal String Test is considered NOT MEDICALLY NECESSARY.
  4. For esophageal and esophagogastric junction cancers, cell-free DNA/circulating tumor DNA (cfDNA/ctDNA) testing is considered NOT MEDICALLY NECESSARY.

Note: For 5 or more gene tests being run on the same platform, please refer to Reimbursement Policy, CAM 235.

Table of Terminology 




American College of Gastroenterology


American Foregut Society


Alpha-methylacyl-CoA racemase


Adenomatous polyposis coli


AT-rich interactive domain-containing protein 1A 


AT-rich interactive domain 2


American Society for Gastrointestinal Endoscopy  


Bethesda marker


Barrett’s esophagus


Bloom syndrome protein


British Medical Journal


Bloom syndrome


Calpain 14


C-C motif chemokine ligand 26


Cyclin A1


Cell-free tumor DNA

CLIA ’88

Clinical Laboratory Improvement Amendments of 1988


Familial cutaneous malignant melanoma-1


Centers for Medicare & Medicaid Services


Cyclooxygenase 2


Combined Positive Score


Chinese Society of Clinical Oncology


Circulating tumor cells


Circulating tumor DNA


Deleted in colorectal carcinoma


Deoxyribonucleic acid


Dedicator of cytokinesis 2


European Academy of Allergy and Clinical Immunology


Esophageal adenocarcinoma


Esophageal dysplasia


Eosinophilic esophagitis diagnostic panel


Epidermal growth factor receptor


Esophagogastric junction


Enzyme-linked immunoassay


Engulfment and cell motility protein 1


Eosinophilic esophagitis


European Society for Medical Oncology  


European Society of Pediatric Gastroenterology, Hepatology and Nutrition


Esophageal string test


European Society of Eosinophilic Oesophagitis


Fanconi anemia 


FA complementation group A


Forceps biopsy


Familial Barrett’s esophagus


Food and Drug Administration


Fluorescence in situ hybridization


Gastroesophageal reflux disease


Human epidermal growth factor receptor 2


High-grade dysplasia


High-grade dysplasia/esophageal adenocarcinoma


Hypoxia-inducible factor 1-alpha


8-oxoguanine DNA glycosylase


Incremental cost-effectiveness ratio


Immunoglobulin E




Indefinite for dysplasia


Japanese Society of Medical Oncology


Potassium oxide


Korean Society of Medical Oncology


Laboratory developed tests


Low-grade dysplasia


Major basic protein 1


Colorectal mutant cancer protein


Mutational load


Mismatch repair


Microsatellite instability


Max-interacting protein 1


Non-dysplastic intestinal metaplasia


National Comprehensive Cancer Network  


Baseline nondysplastic BE


Neurofibromatosis type 2


Nucleoside Diphosphate Kinase 1


Number needed to test


Notch receptor 3


Neurotrophic tyrosine receptor kinase


Polymerase chain reaction


Programmed death-1


Programmed death-ligand 1


Palmoplantar keratoderma


Proteoglycan 2, pro eosinophil major basic protein


Presenilin 2


Phosphatase and TENsin homolog


Quality-adjusted life-year


Retinoblastoma protein


Rhomboid 5 homolog 2


Ring finger protein 43


Society of American Gastrointestinal and Endoscopic Surgeons


Squamous cell carcinomas


SMA- and MAD-related protein 4


Matrix associated, actin dependent regulator of chromatin, subfamily a


Standard of care


Spastic paraplegia 20


Sequence-specific oligonucleotide


Stathmin 1


Technology And Value Assessment Committee  


Trefoil factor 1


Tumor mutational load


TNF alpha induced protein 8


Thoracic outlet syndromes


Tumor protein 53


Tropomyosin receptor kinase


Thymic stromal lymphopoietin


Technology And Value Assessment Committee  


United European Gastroenterology


Von hippel-lindau syndrome




Wide-Area Transepithelial Sampling


Wide-Area Transepithelial Sampling with Computer-Assisted 3-Dimensional Analysis

The esophagus is a long tube that connects the mouth to the stomach. Its primary function is to transport food from the mouth to the stomach. However, this organ is often exposed to difficult conditions, from abrasive food to the acidic conditions of the stomach. Although mechanisms are in place to protect against injury (namely the tough squamous cells), it is common to see injury or disease in the esophagus (Zhang et al., 2020). 

Many serological and genetic markers have been proposed as tools to assist in evaluation of esophageal pathology. Eosinophilic esophagitis (EoE), Barrett’s esophagus (BE), and esophageal cancer are typically diagnosed with histological analysis from endoscopic biopsy (Bonis & Gupta, 2021; Saltzman & Gibson, 2021, 2023; Spechler, 2023), but biopsies frequently require careful consideration and resources to perform properly (NCCN, 2020, 2022b). For these reasons, serum and genetic markers have been suggested as noninvasive markers for esophageal pathologies.

Eosinophilic Esophagitis (EoE)
Eosinophilic esophagitis (EoE) marked by the presence of eosinophils in the esophagus. Eosinophils are typically associated with mitigating inflammation but are not normally found in the esophagus. EoE is represented by a broad set of clinical symptoms, such as difficulty swallowing, chest, or abdominal pain, and feeding dysfunction. Diagnosis is established through endoscopy with biopsies to confirm eosinophilia. The current diagnostic criteria set the cutoff for eosinophilia at ≥ 15 eosinophils per high power field, (60 eosinophils per mm2) although this figure has been heavily discussed (Bonis & Gupta, 2021; Dellon et al., 2018).

Proprietary Testing — EoE
Laboratory tests have been suggested as a noninvasive adjunct for EoE. Serum IgE will be elevated in up to 60% of EoE patients, as allergy has a strong association with EoE. Many other markers, such as eotaxin-3, major basic protein-1, tryptase, chemokines, and serum eosinophil count, have all been suggested to assist in evaluation of EoE (Bonis & Gupta, 2021; Dellon et al., 2018). Immune system factors may also contribute to pathology. Since eosinophils are not normally found in the esophagus, their presence in the esophagus may suggest an underlying issue with the immune system. Various interleukins, mast cells, and T cells have all been proposed as contributing to pathogenesis, but the exact pathway and mechanisms are not completely understood (Rothenberg, 2023). Genetic features have also been used for EoE evaluation. Twin studies and family histories have indicated a role for genetics in EoE. Several genes have also been identified as potential risk factors, such as CAPN14 (an interleukin-13 regulator), TSLP (a basophil regulator), and CCL26 (promotes eosinophil movement into esophagus) (Sherrill & Rothenberg, 2014).

Wen et al. (2013) developed a diagnostic gene expression panel (“EDP”) for EoE. The authors identified candidate genes using two cohorts of EoE and control patients, then validated these genes with a separate cohort of 194 patients (91 active EoE, 57 control, 34 ambiguous, 12 reflux). The panel was found to identify EoE patients at 96% sensitivity and 98% specificity. The authors also noted that the panel could separate patients in remission from unaffected patients (Wen et al., 2013).

Shoda et al. (2018) used an “EoE Diagnostic Panel” (EDP) to further classify EoE cases by histologic, endoscopic, and molecular features. The EDP consisted of 95 esophageal transcripts purported to identify EoE among both unaffected patients and patients with other conditions. 185 biopsies were studied. The authors identified three clear subtypes of EoE; subtype 1 with a normal-appearing esophagus and mild molecular changes, subtype 2 with an inflammatory and steroid-responsive phenotype, and subtype 3 with a “narrow-caliber” esophagus and severe molecular alterations. These findings were replicated in a 100-biopsy sample (Shoda et al., 2018). 

Tests are commercially available for EoE. Noninvasive tests (as an alternative to endoscopy) have been recently popular. The Esophageal String Test (Testa et al.) is one such alternative. The patient swallows a gelatin-coated capsule with a string wrapped inside. Once the capsule is in the patient’s stomach, the gelatin dissolves, allowing the capsule to pass through. The string itself is used to collect samples from the patient’s esophagus and is easily removed from the patient. From there, the sample is analyzed for several biomarkers (major basic protein-1, eotaxins 2 and 3, and so on) to provide a probability% (a trademarked “EoEscore”) of esophageal inflammation (Ackerman et al., 2019; EnteroTrack, 2019).

Barrett’s Esophagus (BE) 
Barrett’s esophagus (BE) is a condition in which the normal squamous tissue lining the esophagus is replaced by metaplastic columnar epithelium. This new epithelium contains gastric features and is typically caused by chronic gastroesophageal reflux disease (GERD). This condition predisposes to esophageal cancer. When noxious substances (gastric acid, bile, et al.) are exposed to the squamous esophageal tissue, the damage is usually repaired through regeneration of these squamous cells. In BE cases, this damage is repaired not through creation of new squamous cells, but through metaplastic columnar cells. The exact reason for this is unknown. Although these metaplastic cells are more resistant to reflux-based damage than the normal squamous cells, these cells frequently show the oxidative DNA damage that is typical of cancer. Mutations in the p53 tumor suppressor gene appear to be the catalyst for cancers, as acquisition of this mutation in conjunction with the replication of the genome is conducive to carcinogenesis (Spechler, 2023).

Vollmer (2019) performed a review assessing incidence of adenocarcinoma detected during surveillance of BE. The author identified 55 studies encompassing 61371 total patients. Of the 61371 total patients, 1106 developed adenocarcinoma. Overall, the author found that the model created from the studies “predicted the per-person probability of developing cancer in 5 years of complete follow-up is approximately 0.0012." Variables affecting this probability included mean time of follow-up, definition of Barrett metaplasia, and fraction of patients followed up for at least 5 years (Vollmer, 2019).

Proprietary Testing — BE
Proprietary tests are commercially available for assessment of BE, usually to evaluate risk (BE progression to cancer, risk of BE itself, and such). For example, BarreGen, offered by Interpace Diagnostics, uses tumor mutational load (a measure intended to capture total genomic instability of a sample) to calculate risk of progression. Although many ways can estimate mutational load, BarreGen tests 10 key genomic loci which are as follows: “1p (CMM1, L-myc), 3p (VHL, HoGG1), 5q (MCC, APC), 9p (CDKN2A), 10q (PTEN, MXI1), 17p (TP53), 17q (RNF43, NME1), 18q (SMAD4, DCC), 21q (TFF1, PSEN2) and 22q (NF2)." These loci encompass integral tumor suppressors and are proposed to provide an accurate picture of genomic instability (Interpace, 2019; Trindade et al., 2019). 

Another test, TissueCypher, also proposes to predict likelihood of progression from BE to esophageal cancer. The test measures 9 protein biomarkers that represent morphological and cellular changes (p53, p16, AMACR, CD68, COX2, HER2, K20, HIF1-alpha, CD45RO). These biomarkers are quantified and converted to a risk score (1 – 10) and probability of progression (Cernostics, 2021). 

Esoguard, by Lucid Diagnostics, is an esophageal DNA test which analyzes 31 methylated biomarkers in the diagnosis of non-dysplastic Barrett’s esophagus and adenocarcinoma. The assay uses next generation sequencing to examine individual DNA molecules for the presence or absence of cytosine methylation with a 90% specificity and 90% sensitivity (Lucid_Diagnostics, 2022).

Finally, a proprietary imaging system, WATS3D, is commercially available. This imaging system samples from a wider area, as opposed to only taking focal samples in a traditional biopsy. This technology also provides a 3-dimensional image of the sampled area. This technology purports to provide more precise sampling than the traditional 4-quadrant biopsies, claiming an increased detection rate of BE and other dysplasias (Diagnostics, 2023).

Esophageal Cancer
Esophageal cancers are largely divided into two groups: squamous cell carcinomas (SCCs) and adenocarcinomas (EAC). SCCs usually begin in the middle of the esophagus, whereas EACs often originate near the gastroesophageal junction. Both share several risk factors, such as smoking. Due to the numerous environmental risk factors for both types of cancer, it is difficult to ascertain the true impact of genetic factors (Gibson, 2023). These cancers are primarily diagnosed through histologic examination, usually obtained through endoscopy (Saltzman & Gibson, 2021, 2023).

Advancements have been in the molecular characterization of both types of cancer. TP53 mutations are the most common mutation seen in both types of cancer. Other frequently mutated genes in adenocarcinoma include ELMO1 and DOCK2 (enhance cell motility), ARID1A, SMARCA4 and ARID2 (chromatin remodelers), and SPG20 (traffics growth factor receptors). BE, as the precursor to adenocarcinomas, includes certain similarities in genetic mutations but at a less severe rate. Further, the rate of overlap tended to increase with higher degree of dysplasia (Testa et al., 2017).

SCC mutations tend to be in genes associated with specific cellular pathways. Genes in ubiquitous pathways, such as EGFR, NOTCH3, and RB, are frequently mutated in SCC. The molecular profile of esophageal SCC tends to align more with other squamous cell cancers (such as head and neck cancers) rather than EAC (Testa et al., 2017). Numerous gene expression studies have been performed to further classify molecular subtypes of esophageal cancer (Gonzaga et al., 2017; McLaren et al., 2017; Visser et al., 2017). Gene expression profiles may have utility in assessing response to treatment, prognosis, or risk assessment. 

Historically, Carcinoembryonic Antigen (CEA) has been used as the serum cancer marker in the diagnosis of esophageal cancer, as CEA levels have been shown to be significantly higher in these patients. The sensitivity (8% – 70%), specificity (57% – 100%), and positive likelihood ratio (5.94) of CEA means that patients with EC have a 6-fold higher chance of having higher CEA levels. Other markers include squamous cell cancer antigen (SCC-Ag) and cytokeratin 21-1 fragment (CYFRA21-1). The sensitivity and specificity Cyfra21-1 ranged from 36% to 63% and from 89% to 100%, respectively, with patients having a 12-fold higher chance of having EC. The sensitivity and specificity of SCC-Ag ranged from 13% to 64% and from 91% to 100%, respectively, whereas its PLR was 7.66 (Visaggi et al., 2021).

Li et al. (2019) investigated potential biomarkers for lymph node metastasis for esophageal squamous cell carcinoma. 6 studies encompassing 70 patients were included. The authors identified 9 biomarkers and 4 cellular mechanisms that influence lymph node metastasis. From there, they identified three biomarkers with broader influence on prognosis of disease, PTEN, STMN1, and TNFAIP8. The authors suggested that those three biomarkers should be researched further (Li et al., 2019).

Plum et al. (2019) evaluated HER2 overexpression’s impact on prognosis of esophageal adenocarcinoma (EAC). 428 EAC patients that underwent a “transthoracic thoraco-abdominal esophagectomy” were included. The authors identified 44 patients with HER2 positivity (IHC score 3+ or 2+ with gene amplification). This cohort was found to have a better overall survival (OS, 70.1 months vs 24.6 months), along with better histology, absence of lymphatic metastases, and lower tumor stages. The authors also noted a similarity in results to a large 2012 study (Plum et al., 2019).

Frankell et al. (2019) examined the molecular landscape of esophageal adenocarcinoma (EAC). The authors assessed 551 genomically characterized EACs. A total of 77 driver genes and “21 non-coding driver elements” were identified. The authors also found an average of 4.4 driver events per tumor. A three-way association was found, between hyper-mutation, Wnt signaling, and loss of immune signaling genes. Finally, the authors also identified “sensitizing events” (events causing a tumor to be more susceptible to a therapy) to CD4/6 inhibitors in over half of the EAC cases studied (Frankell et al., 2019).

Clinical Validity and Utility
Ackerman et al. (2019) evaluated the ability of the 1-hour Esophageal String Test (Testa et al.) to distinguish between active eosinophilic esophagitis (EoE), inactive eosinophilic esophagitis, and normal esophagi. 134 patients (62 active EoE, 37 inactive EoE, 35 normal) were included. The authors found that eotaxin 3 measured from both EST samples and the control biopsy extracts to be the best marker for distinguishing active EoE from inactive EoE (by both sensitivity and specificity). Addition of major basic protein 1 (MBP-1) improved sensitivity by 0.039 (0.652 to 0.693) and specificity by 0.014 (0.261 to 0.275) across all patients (Ackerman et al., 2019).

Hao et al. (2019) performed a cost-effectiveness analysis of an “adenocarcinoma risk prediction multi-biomarker assay” (TissueCypher’s Barrett’s Esophagus Assay). A hypothetical cohort of 10,000 patients with BE diagnoses (including non-dysplastic intestinal metaplasia [NBDE], indefinite for dysplasia [IND], and low-grade dysplasia [LGD]) was created. A Markov decision model was used to compare BE management costs between assay use and the standard of care (SOC). A surveillance interval of 5 years was used. Low-risk patients were found to have a 16.6% reduction in endoscopies. High-risk patients were found to have a 58.4% increase in endoscopic treatments (compared to the SOC arm), leading to a death total of 111 for the assay arm compared to 204 in the SOC arm (a 45.6% reduction). Overall, the authors calculated the incremental cost-effectiveness ratio (ICER) to be $52,483/quality-adjusted life-year (QALY), and they found that “the probability of the Assay being cost-effective compared to the SOC was 57.3% at the $100,000/QALY acceptability threshold” (Hao et al., 2019). 

Eluri et al. (2018) aimed to validate a genomic panel intended to represent tumor mutational load (TML). Previously, the authors evaluated a panel of 10 genomic loci from which a TML score was calculated. This mean TML was found to be significantly higher in 23 BE patients that had progressed to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC) as compared to 46 that had not progressed. The area under the curve in this prior study was found to be 0.95 at a mutational load (ML) cutoff of 1 (on a scale of 1 – 10). In the present study, 159 subjects were included. Cases had “baseline nondysplastic BE (NDBE) and developed HGD/EAC ≥ 2 years later.” 58 subjects were progressors and 101 were nonprogressors. The authors identified no difference in mean ML in pre-progression tissue in both cohorts (“ML = 0.73 ± 0.69 vs. ML = 0.74 ± 0.61”). The area under the curve at the cutoff of ML 1 was only 0.50, and the authors concluded that the “utility of the ML to stratify BE patients for risk of progression was not confirmed in this study” (Eluri et al., 2018).

Trindade et al. (2019) evaluated tumor mutational load’s (ML) ability to “risk-stratify those that may progress from non-dysplastic BE to dysplastic disease." 28 patients were included, and ML levels were compared between those that progressed to dysplasia and those who had not. 8 total patients progressed to dysplasia (6 low-grade, 2 high-grade), and 7 of these patients had “some level” of genomic stability detected (ML ≥ .5 on a scale of 1 to 10). 10 of the 20 patients that did not progress to dysplasia had “no” ML level. The authors also noted that at an ML of ≥ 1.5, the risk of progression to high-grade dysplasia was 33%, with a sensitivity of 100% and specificity of 85%. The authors concluded “that ML may be able to risk-stratify progression to high-grade dysplasia in BE-IND. Larger studies are needed to confirm these findings” (Trindade et al., 2019).
Moinova et al. (2018) evaluated the ability of two DNA methylation signatures to detect BE. Methylation signatures of the VIM and CCNA1 loci were evaluated in 173 patients with or without BE. CCNA1 methylation was found to have an area under the curve of 0.95 for distinguishing BE-related dysplasia compared to normal esophagi. When the data for VIM methylation was added, the resulting sensitivity was 95%, and the resulting specificity was 91%. These findings were replicated in a validation cohort of 86 patients, with the combination of methylation markers detecting BE metaplasia at 90.3% sensitivity and 91.7% specificity (Moinova et al., 2018).

Critchley-Thorne et al. (2016) validated a pathology panel to predict progression of BE to esophageal cancer. The authors identified 15 potential biomarkers, which were evaluated in both training and validation sets. This “classifier” separated patients into three different risk classes: low, intermediate, and high in the training set of 183. The authors calculated the hazard ratio of intermediate to low risk at 4.19 and high to low at 14.73. In the validation set (n = 183), the concordance index (an estimation of area under the curve) of the 15-factor classifier was 0.772, the best of the amounts tested (3, 6, 9, 12, 15, 17). The authors also noted that this classifier provided independent prognostic information that were outperformed predictions based on other clinicopathological factors, such as segment length, age, and p53 overexpression (Critchley-Thorne et al., 2016).

Another multicenter study investigated the use of WATS3D with either random or targeted FB in the detection of esophageal dysplasia (ED). 12,899 patients were enrolled in the study, and WATS3D detected an additional 213 cases of ED beyond the initial 88 cases identified by FB, representing an increase of 242%. Regarding screening for BE, WATS increased the overall detection by 153% (from 13.1% to 33% of the individuals enrolled). The authors noted that the order of testing (e.g., FB or WATS) did not impact the results. The authors conclude, “In this study, comprised of the largest series of patients evaluated with WATS, adjunctive use of the technique with targeted and random FB markedly improved the detection of both ED and BE. These results underscore the shortcomings of FB in detecting BE-associated neoplasia, which can potentially impact the management and clinical outcomes of these patients” (Smith et al., 2019).

A study into the cost-effectiveness of WATS3D testing as an adjunct to the standard-of-care forceps biopsy (FB) used a reference case of a 60-year-old white male with gastroesophageal reflux disease (GERD) to see the number of screens needed to avert one cancer and one cancer-related death as well as to calculate the quality-adjusted life years (QALYs) as measured in 2019 U.S. dollars. With this as a reference case, 320 – 337 individuals would need to be screened using WATS3D to avert one cancer, and 328 – 367 individuals would be required to avert one death. The additional cost associated with WATS3D was $1219, but an additional 0.017 QALYs were produced, resulting in an ICER of $71,395/QALY. The authors conclude, “Screening for BE in 60-year-old white male GERD patients is more cost-effective when WATS3D is used adjunctively to the Seattle protocol than with the Seattle protocol alone” (Singer & Smith, 2020).

One study compared the use of the WATS3D technology to standard forceps biopsy. 117 individuals with a history of Barrett’s esophagus with dysplasia had both techniques performed. For the biopsy, a four-quadrant biopsy quadrant protocol was performed every 1 – 2 cm. Evaluation of the biopsy and the WATS3D technique was performed by separate pathologists, blinded to each other’s results. “Brush biopsy [WATS3D] added an additional 16 position cases increasing the yield of dysplasia detection by 42% (95% CI: 20.7 – 72.7). The number needed to test (NNT) to detect one additional case of dysplasia was 9.4 (95% CI: 6.4 – 17.7).” The authors of the study noted that no statistical difference was evident between medical centers, the type of forceps used, or between sampling every 1 cm versus every 2 cm. They conclude, “These data suggest that computer-assisted brush biopsy is a useful adjunct to standard endoscopic surveillance regimens for the identification of dysplasia in Barrett’s esophagus” (Anandasabapathy et al., 2011).

Another multicenter prospective trial of 4203 patients studied the use of WATS3D as an adjunct to four-quadrant random forceps biopsy (FB) in detecting Barrett’s esophagus (BE) and esophageal dysplasia (ED). FB alone detected 594 cases of BE, and the addition of WATS3D detected an additional 493 cases, an increase of 83%. Likewise, WATS3D detected an increase of 88.5% of low-grade dysplasia (LGD). The authors conclude, “Adjunctive use of WATS to FB significantly improves the detection of both BE and ED. Sampling effort, an inherent limitation associated with screening and surveillance, can be improved with WATS allowing better informed decisions to be made about the management and subsequent treatment of these patients (Gross et al., 2018).” These findings support the earlier study by Johanson and colleagues. In their study of 1266 patients being screened for BE and ED, they noted an overall increase of 39.8% in the detection of BE when WATS3D (brush biopsy or BB) was used as an adjunct to FB. They also report that the number of patients needed to test (NNT) to obtain a positive BE result was 8.7. Interestingly, specifically for patients with gastroesophageal reflux disease (GERD), the addition of WATS3D resulted in an even higher increase in the detection of BE (by 70.5%) (Johanson et al., 2011).

Another study published in 2018 of a randomized trial at 16 different medical centers (n = 160 patients) compared the order of testing (WATS3D followed by biopsy sampling versus biopsy sampling followed by WATS3D) to detect high-grade dysplasia/esophageal adenocarcinoma (HGD/EAC). The authors also stated secondary aims of determining the amount of additional time required for WATS3D and the ability of each procedure to separately detect neoplasia. The order of the procedures was not statistically relevant. The use of WATS3D as an adjunct to biopsy did result in a 14.4% absolute increase in the number of HGD/EAC cases detected. The authors noted that WATS3D, on average, adds 4.5 minutes to the total procedure time. They conclude, “Results of this multicenter, prospective, randomized trial demonstrate that the use of WATS in a referral BE population increases the detection of HGD/EAC” (Vennalaganti et al., 2018).

Diehl studied the impact of TissueCypher Barrett’s esophagus (BE) assay on clinical decisions in the management of BE patients. TissueCypher was ordered for 60 patients with BE and the impact of the test was assessed. TissueCypher results impacted 55.0 % of management decisions, resulting in either upstaging or downstaging of treatment. "In 21.7% of patients, the test upstaged the management approach, resulting in endoscopic eradication therapy (Wechsler et al.) or shorter surveillance interval. The test downstaged the management approach in 33.4 % of patients, leading to surveillance rather than EET. In the subset of patients whose management plan was changed, upstaging was associated with a high-risk TissueCypher result, and downstaging was associated with a low-risk result" (Diehl et al., 2021). The authors conclude that TissueCypher will help target EET for high risk patients and reduce unneeded procedures in low risk patients (Diehl et al., 2021).

Wechsler studied the clinical utility of noninvasive biomarkers to identify EoE in children and predict esophageal eosinophilia. Blood/urine was collected from 183 children and several biomarkers were measured including Absolute eosinophil count (AEC), plasma eosinophil-derived neurotoxin (EDN), eosinophil cationic protein (ECP), major basic protein-1 (MBP-1), galectin-10 (CLC/GAL-10), Eotaxin-2 and Eotaxin-3, and urine osteopontin (OPN) and matrix metalloproteinase-9 (MMP-9). According to the results, all plasma and urine biomarkers were in increased in EoE. A panel that included all the other biomarkers was superior to measuring only AEC alone. AEC, CLC/GAL-10, ECP, and MBP-1 were significantly decreased in patients with esophageal eosinophil counts < 15/hpf in response to treatment. AEC combined with MBP-1 best predicted the esophageal eosinophil counts. The authors conclude that eosinophil-associated proteins along with AEC are superior to AEC alone in distinguishing EoE and predicting eosinophil counts (Wechsler et al., 2021).

United European Gastroenterology (UEG), The European Society of Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN), the European Academy of Allergy and Clinical Immunology (EAACI), and the European Society of Eosinophilic Oesophagitis (EUREOS) 
These joint guidelines were published by a task force of 21 physicians and researchers for eosinophilic esophagitis (EoE). In it, they note that noninvasive biomarkers (inflammatory factors, total IgE, chemokines, tryptase, et al.) are “not accurate” to diagnose or monitor EoE. They remark that absolute serum eosinophil count fared best in correlating with severity of disease but had a diagnostic accuracy of 0.754. The guidelines state that histology is necessary for monitoring. The String Test was also mentioned as having good preliminary results but required further corroboration (Lucendo et al., 2017).

Updated International Consensus Diagnostic Criteria for Eosinophilic Esophagitis: Proceedings of the AGREE Conference 
These newly published international diagnostic criteria primarily include endoscopic findings. Although the guidelines emphasize ruling out other diagnoses (in which biomarkers may be useful), it does not mention any serum or genetic factors for EoE itself (Dellon et al., 2018).

National Comprehensive Cancer Network (NCCN)
The NCCN notes four syndromes that predispose to an increased risk for esophageal and esophagogastric junction (EGJ) cancers; tylosis with non-epidermolytic palmoplantar keratoderma (PPK) with esophageal cancer (including Howel-Evans syndrome), familial Barrett esophagus (FBE), Bloom Syndrome (BS, BLM gene), and Fanconi Anemia (FA, FANC A-E genes). The RHBDF2 gene has been associated with tylosis (with non-epidermolytic palmoplantar keratosis) for genetic risk assessment. Though FBE may be associated with “one or more autosomally inherited dominant susceptibility alleles,” no gene has been validated. With regards to next-generation sequencing, the NCCN concludes that “when limited tissue is available for testing, sequential testing of single biomarkers or use of limited molecular diagnostic panels may quickly exhaust the sample. In these scenarios, comprehensive genomic profiling via a validated NGS assay performed in a CLIA-approved laboratory may be used for the identification of HER2 amplification, MSI [microsatellite instability], and NTRK gene fusions. It should be noted that NGS has several inherent limitations and thus whenever possible, the use of gold-standard assays (IHC [immunohistochemistry]/FISH [fluorescence in situ hybridization]/targeted PCR [polymerase chain reaction]) should be performed”(NCCN, 2022a).

Liquid biopsy aids in identifying genetic mutations in solid cancers by looking at circulating tumor DNA (ctDNA) in blood and can be used in those with advanced disease and cannot undergo clinical biopsies for disease surveillance and management. Detecting mutations in DNA from esophageal and EGJ carcinomas “can identify targetable alterations or the evolution of clones with altered treatment response profiles.” The NCCN has also stated that “a negative result should be interpreted with caution, as this does not exclude the presence of tumor mutations or amplifications” (NCCN, 2022a).

The NCCN notes that “testing for MSI by polymerase chain reaction (PCR) or MMR [mismatch repair] by IHC should be considered on locally advanced, recurrent, or metastatic esophageal and EGJ cancers in patients who are candidates for treatment with PD-1 inhibitors.” The NCCN also identifies several targeted therapeutic agents currently approved by the FDA; trastuzumab, pembrolizumab/nivolumab, and entrectinib/larotrectinib. Trastuzumab is based on HER2 overexpression and pembrolizumab is based on “testing for MSI by PCR or NGS/MMR by IHC or PD-LA immunohistochemical expression by CPS or high mutational burden (TMB).” Select TRK inhibitors have also been FDA-approved for NTRK gene fusion-positive tumors (NCCN, 2022a).

Genetic biomarkers such as aneuploidy and loss of p53 heterozygosity have been proposed as useful for identifying increased risk of progression in BE patients, but the NCCN remarks that these biomarkers require “further prospective evaluation as predictors of risk for the development of HGD [high-grade dysplasia] and adenocarcinoma of the esophagus in patients with Barrett esophagus” (NCCN, 2022a).

The NCCN notes that wide-area transepithelial sampling (WATS) has been used to detect esophageal carcinomas in BE patients. They state, “The use of wide-area transepithelial sampling with computer-assisted 3-dimensional analysis (WATS3D), a relatively new sampling technique combining an abrasive brush biopsy of the Barrett esophagus mucosa with computer-assisted pathology analysis to highlight abnormal cells, may help increase the detection of esophageal dysplasia in patients with Barrett esophagus.” They go on to cite the 2017 study by Vennalaganti and colleagues that shows a 14.4% increase in the number of additional cases of HGD/esophageal adenocarcinoma captured by using WATS. However, the NCCN remarks that the “utility and accuracy of WATS for detecting HGD/adenocarcinoma in patients with Barrett esophagus needs to be evaluated in larger phase III randomized trials” (NCCN, 2022a).

For squamous cell carcinoma, the NCCN recommends performing microsatellite and PD-L1 testing (if not done previously) if metastatic cancer is suspected. NGS may be considered via validated assay (NCCN, 2022a). 

American Society for Gastrointestinal Endoscopy 
The ASGE recommends the use of WATS3D as an adjunct to “Seattle protocol biopsy sampling” in patients with known or suspected BE (conditional recommendation, low quality of evidence). The society stated that they had downrated the certainty of the recommendation due to possible risk bios, insistency, and indirectness of the studies that were available at the time of publication since some of the studies had included LGD (whereas others had not) and many of the studies had been sponsored by the test’s manufacturer. The society also had noted that, as of the date of publication, no studies addressing the cost-effectiveness of WATS-3D had been published. (Qumseya et al., 2019) It should be noted that since the publication of these guidelines the 2020 cost-effectiveness study by Singer and Smith (2020) has been published.


  1. Ackerman, S. J., Kagalwalla, A. F., Hirano, I., Gonsalves, N., Katcher, P. M., Gupta, S., Wechsler, J. B., Grozdanovic, M., Pan, Z., Masterson, J. C., Du, J., Fantus, R. J., Alumkal, P., Lee, J. J., Ochkur, S., Ahmed, F., Capocelli, K., Melin-Aldana, H., Biette, K., . . . Furuta, G. T. (2019). One-Hour Esophageal String Test: A Nonendoscopic Minimally Invasive Test That Accurately Detects Disease Activity in Eosinophilic Esophagitis. Am J Gastroenterol, 114(10), 1614-1625. 
  2. AFS. (2021). Wide Area Transepithelial Sampling with Computer Assisted 3D Analysis (WATS3D). American Foregut Society. Retrieved 07/31/2020 from
  3. Anandasabapathy, S., Sontag, S., Graham, D. Y., Frist, S., Bratton, J., Harpaz, N., & Waye, J. D. (2011). Computer-assisted brush-biopsy analysis for the detection of dysplasia in a high-risk Barrett's esophagus surveillance population. Dig Dis Sci, 56(3), 761-766. 
  4. Bonis, P. A. L., & Gupta, S. K. (2021, February 17). Clinical manifestations and diagnosis of eosinophilic esophagitis.
  5. Bonis, P. A. L., & Gupta, S. K. (2023, February 17). Clinical manifestations and diagnosis of eosinophilic esophagitis.
  6. Cernostics. (2021). What is the TissueCypher® Barrett’s Esophagus Assay? Retrieved 1/28/21 from
  7. Costa-Barbosa, F. A., Balasubramanian, R., Keefe, K. W., Shaw, N. D., Al-Tassan, N., Plummer, L., Dwyer, A. A., Buck, C. L., Choi, J. H., Seminara, S. B., Quinton, R., Monies, D., Meyer, B., Hall, J. E., Pitteloud, N., & Crowley, W. F., Jr. (2013). Prioritizing genetic testing in patients with Kallmann syndrome using clinical phenotypes. J Clin Endocrinol Metab, 98(5), E943-953. 
  8. Critchley-Thorne, R. J., Duits, L. C., Prichard, J. W., Davison, J. M., Jobe, B. A., Campbell, B. B., Zhang, Y., Repa, K. A., Reese, L. M., Li, J., Diehl, D. L., Jhala, N. C., Ginsberg, G., DeMarshall, M., Foxwell, T., Zaidi, A. H., Lansing Taylor, D., Rustgi, A. K., Bergman, J. J., & Falk, G. W. (2016). A Tissue Systems Pathology Assay for High-Risk Barrett's Esophagus. Cancer Epidemiol Biomarkers Prev, 25(6), 958-968. 
  9. Dellon, E. S., Liacouras, C. A., Molina-Infante, J., Furuta, G. T., Spergel, J. M., Zevit, N., Spechler, S. J., Attwood, S. E., Straumann, A., Aceves, S. S., Alexander, J. A., Atkins, D., Arva, N. C., Blanchard, C., Bonis, P. A., Book, W. M., Capocelli, K. E., Chehade, M., Cheng, E., . . . Bredenoord, A. J. (2018). Updated International Consensus Diagnostic Criteria for Eosinophilic Esophagitis: Proceedings of the AGREE Conference. Gastroenterology, 155(4), 1022-1033.e1010. 
  10. Diagnostics, C. (2023). WATS3D empowers physicians to preempt cancer. Retrieved 1/28/21 from
  11. Diehl, D. L., Khara, H. S., Akhtar, N., & Critchley-Thorne, R. J. (2021). TissueCypher Barrett's esophagus assay impacts clinical decisions in the management of patients with Barrett's esophagus. Endosc Int Open, 9(3), E348-e355. 
  12. Docimo, S., Jr., Al-Mansour, M., & Tsuda, S. (2020). SAGES TAVAC safety and efficacy analysis WATS(3D) (CDx Diagnostics, Suffern, NY). Surg Endosc. 
  13. Eluri, S., Klaver, E., Duits, L. C., Jackson, S. A., Bergman, J. J., & Shaheen, N. J. (2018). Validation of a biomarker panel in Barrett's esophagus to predict progression to esophageal adenocarcinoma. Dis Esophagus, 31(11). 
  14. EnteroTrack. (2019). The EnteroTracker®.
  15. Frankell, A. M., Jammula, S., Li, X., Contino, G., Killcoyne, S., Abbas, S., Perner, J., Bower, L., Devonshire, G., Ococks, E., Grehan, N., Mok, J., O'Donovan, M., MacRae, S., Eldridge, M. D., Tavaré, S., & Fitzgerald, R. C. (2019). The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic. Nat Genet, 51(3), 506-516. 
  16. Gibson, M. (2023, April 14). Epidemiology and pathobiology of esophageal cancer.
  17. Gonzaga, I. M., Soares Lima, S. C., Nicolau, M. C., Nicolau-Neto, P., da Costa, N. M., de Almeida Simao, T., Hernandez-Vargas, H., Herceg, Z., & Ribeiro Pinto, L. F. (2017). TFF1 hypermethylation and decreased expression in esophageal squamous cell carcinoma and histologically normal tumor surrounding esophageal cells. Clin Epigenetics, 9, 130. 
  18. Gross, S. A., Smith, M. S., & Kaul, V. (2018). Increased detection of Barrett's esophagus and esophageal dysplasia with adjunctive use of wide-area transepithelial sample with three-dimensional computer-assisted analysis (WATS). United European Gastroenterol J, 6(4), 529-535. 
  19. Hao, J., Critchley-Thorne, R., Diehl, D. L., & Snyder, S. R. (2019). A Cost-Effectiveness Analysis Of An Adenocarcinoma Risk Prediction Multi-Biomarker Assay For Patients With Barrett's Esophagus. Clinicoecon Outcomes Res, 11, 623-635. 
  20. Interpace. (2019). An Innovative Diagnostic Tool for Barrett’s Esophagus Patients. Retrieved 1/28/21 from
  21. Johanson, J. F., Frakes, J., & Eisen, D. (2011). Computer-assisted analysis of abrasive transepithelial brush biopsies increases the effectiveness of esophageal screening: a multicenter prospective clinical trial by the EndoCDx Collaborative Group. Dig Dis Sci, 56(3), 767-772. 
  22. Li, J., Qi, Z., Hu, Y. P., & Wang, Y. X. (2019). Possible biomarkers for predicting lymph node metastasis of esophageal squamous cell carcinoma: a review. J Int Med Res, 47(2), 544-556. 
  23. Lordick, F., Mariette, C., Haustermans, K., Obermannová, R., Arnold, D., & on behalf of the, E. G. C. (2016). Oesophageal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Annals of Oncology, 27(suppl_5), v50-v57. 
  24. Lucendo, A. J., Molina-Infante, J., Arias, A., von Arnim, U., Bredenoord, A. J., Bussmann, C., Amil Dias, J., Bove, M., Gonzalez-Cervera, J., Larsson, H., Miehlke, S., Papadopoulou, A., Rodriguez-Sanchez, J., Ravelli, A., Ronkainen, J., Santander, C., Schoepfer, A. M., Storr, M. A., Terreehorst, I., . . . Attwood, S. E. (2017). Guidelines on eosinophilic esophagitis: evidence-based statements and recommendations for diagnosis and management in children and adults. United European Gastroenterol J, 5(3), 335-358. 
  25. Lucid_Diagnostics. (2022). Esoguard. 
  26. McLaren, P. J., Barnes, A. P., Terrell, W. Z., Vaccaro, G. M., Wiedrick, J., Hunter, J. G., & Dolan, J. P. (2017). Specific gene expression profiles are associated with a pathologic complete response to neoadjuvant therapy in esophageal adenocarcinoma. Am J Surg, 213(5), 915-920. 
  27. Moinova, H. R., LaFramboise, T., Lutterbaugh, J. D., Chandar, A. K., Dumot, J., Faulx, A., Brock, W., De la Cruz Cabrera, O., Guda, K., Barnholtz-Sloan, J. S., Iyer, P. G., Canto, M. I., Wang, J. S., Shaheen, N. J., Thota, P. N., Willis, J. E., Chak, A., & Markowitz, S. D. (2018). Identifying DNA methylation biomarkers for non-endoscopic detection of Barrett's esophagus. Sci Transl Med, 10(424). 
  28. Muro, K., Lordick, F., Tsushima, T., Pentheroudakis, G., Baba, E., Lu, Z., Cho, B. C., Nor, I. M., Ng, M., Chen, L. T., Kato, K., Li, J., Ryu, M. H., Zamaniah, W. I. W., Yong, W. P., Yeh, K. H., Nakajima, T. E., Shitara, K., Kawakami, H., . . . Douillard, J. Y. (2019). Pan-Asian adapted ESMO Clinical Practice Guidelines for the management of patients with metastatic oesophageal cancer: a JSMO-ESMO initiative endorsed by CSCO, KSMO, MOS, SSO and TOS. Ann Oncol, 30(1), 34-43. 
  29. NCCN. (2020). Esophageal and Esphagogastric Junction Cancers - Version 5.2020 - December 23, 2020. 
  30. NCCN. (2022a). Esophageal and Esophagogastric Cancers. 
  31. NCCN. (2022b). Esophageal and Esophagogastric Junction Cancers.
  32. Plum, P. S., Gebauer, F., Krämer, M., Alakus, H., Berlth, F., Chon, S. H., Schiffmann, L., Zander, T., Büttner, R., Hölscher, A. H., Bruns, C. J., Quaas, A., & Loeser, H. (2019). HER2/neu (ERBB2) expression and gene amplification correlates with better survival in esophageal adenocarcinoma. BMC Cancer, 19(1), 38. 
  33. Qumseya, B., Sultan, S., Bain, P., Jamil, L., Jacobson, B., Anandasabapathy, S., Agrawal, D., Buxbaum, J. L., Fishman, D. S., Gurudu, S. R., Jue, T. L., Kripalani, S., Lee, J. K., Khashab, M. A., Naveed, M., Thosani, N. C., Yang, J., DeWitt, J., & Wani, S. (2019). ASGE guideline on screening and surveillance of Barrett's esophagus. Gastrointestinal Endoscopy, 90(3), 335-359.e332. 
  34. Rocha-Filho, D. R., Peixoto, R. D., Weschenfelder, R. F., Rego, J. F. M., Riechelmann, R., Coutinho, A. K., Fernandes, G. S., Jacome, A. A., Andrade, A. C., Murad, A. M., Mello, C. A. L., Miguel, D., Gomes, D. B. D., Racy, D. J., Moraes, E. D., Akaishi, E. H., Carvalho, E. S., Mello, E. S., Filho, F. M., . . . Prolla, G. (2021). Brazilian Group of Gastrointestinal Tumours' consensus guidelines for the management of oesophageal cancer. Ecancermedicalscience, 15, 1195. 
  35. Rothenberg, M. E. (2023). Eosinophilic esophagitis (EoE): Genetics and immunopathogenesis. Retrieved 1/28/2021 from
  36. Saltzman, J. R., & Gibson, M. K. (2021). Clinical manifestations, diagnosis, and staging of esophageal cancer. Retrieved 1/28/21 from
  37. Saltzman, J. R., & Gibson, M. K. (2023, October 22). Clinical manifestations, diagnosis, and staging of esophageal cancer.
  38. Shaheen, N. J., Falk, G. W., Iyer, P. G., & Gerson, L. B. (2016). ACG Clinical Guideline: Diagnosis and Management of Barrett’s Esophagus. 111(1), 30-50. 
  39. Sherrill, J. D., & Rothenberg, M. E. (2014). Genetic and epigenetic underpinnings of eosinophilic esophagitis. Gastroenterol Clin North Am, 43(2), 269-280. 
  40. Shoda, T., Wen, T., Aceves, S. S., Abonia, J. P., Atkins, D., Bonis, P. A., Caldwell, J. M., Capocelli, K. E., Carpenter, C. L., Collins, M. H., Dellon, E. S., Eby, M. D., Gonsalves, N., Gupta, S. K., Falk, G. W., Hirano, I., Menard-Katcher, P., Kuhl, J. T., Krischer, J. P., . . . Rothenberg, M. E. (2018). Eosinophilic oesophagitis endotype classification by molecular, clinical, and histopathological analyses: a cross-sectional study. Lancet Gastroenterol Hepatol, 3(7), 477-488. 
  41. Singer, M. E., & Smith, M. S. (2020). Wide Area Transepithelial Sampling with Computer-Assisted Analysis (WATS(3D)) Is Cost-Effective in Barrett's Esophagus Screening. Dig Dis Sci. 
  42. Smith, M. S., Ikonomi, E., Bhuta, R., Iorio, N., Kataria, R. D., Kaul, V., & Gross, S. A. (2019). Wide-area transepithelial sampling with computer-assisted 3-dimensional analysis (WATS) markedly improves detection of esophageal dysplasia and Barrett's esophagus: analysis from a prospective multicenter community-based study. Dis Esophagus, 32(3). 
  43. Spechler, S. (2023, September 8). Barrett's esophagus: Epidemiology, clinical manifestations, and diagnosis.
  44. Testa, U., Castelli, G., & Pelosi, E. (2017). Esophageal Cancer: Genomic and Molecular Characterization, Stem Cell Compartment and Clonal Evolution. Medicines (Basel), 4(3). 
  45. Trindade, A. J., McKinley, M. J., Alshelleh, M., Levi, G., Stewart, M., Quinn, K. J., & Thomas, R. M. (2019). Mutational load may predict risk of progression in patients with Barrett’s oesophagus and indefinite for dysplasia: a pilot study. BMJ Open Gastroenterology, 6(1), e000268. 
  46. Vennalaganti, P. R., Kaul, V., Wang, K. K., Falk, G. W., Shaheen, N. J., Infantolino, A., Johnson, D. A., Eisen, G., Gerson, L. B., Smith, M. S., Iyer, P. G., Lightdale, C. J., Schnoll-Sussman, F., Gupta, N., Gross, S. A., Abrams, J., Haber, G. B., Chuttani, R., Pleskow, D. K., . . . Sharma, P. (2018). Increased detection of Barrett's esophagus-associated neoplasia using wide-area trans-epithelial sampling: a multicenter, prospective, randomized trial. Gastrointest Endosc, 87(2), 348-355. 
  47. Visaggi, P., Barberio, B., Ghisa, M., Ribolsi, M., Savarino, V., Fassan, M., Valmasoni, M., Marchi, S., de Bortoli, N., & Savarino, E. (2021). Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence. Cancers (Basel), 13(13). 
  48. Visser, E., Franken, I. A., Brosens, L. A., Ruurda, J. P., & van Hillegersberg, R. (2017). Prognostic gene expression profiling in esophageal cancer: a systematic review. Oncotarget, 8(3), 5566-5577. 
  49. Vollmer, R. T. (2019). A review of the incidence of adenocarcinoma detected during surveillance for Barrett's esophagus. Hum Pathol, 84, 150-154. 
  50. Wechsler, J. B., Ackerman, S. J., Chehade, M., Amsden, K., Riffle, M. E., Wang, M.-Y., Du, J., Kleinjan, M. L., Alumkal, P., Gray, E., Kim, K.-Y. A., Wershil, B. K., & Kagalwalla, A. F. (2021). Noninvasive biomarkers identify eosinophilic esophagitis: A prospective longitudinal study in children. Allergy, 76(12), 3755-3765. 
  51. Wen, T., Stucke, E. M., Grotjan, T. M., Kemme, K. A., Abonia, J. P., Putnam, P. E., Franciosi, J. P., Garza, J. M., Kaul, A., King, E. C., Collins, M. H., Kushner, J. P., & Rothenberg, M. E. (2013). Molecular diagnosis of eosinophilic esophagitis by gene expression profiling. Gastroenterology, 145(6), 1289-1299. 
  52. Zhang, X., Patil, D., Odze, R. D., Zhao, L., Lisovsky, M., Guindi, M., Riddell, R., Bellizzi, A., Yantiss, R. K., Nalbantoglu, I., & Appelman, H. D. (2020). The microscopic anatomy of the esophagus including the individual layers, specialized tissues, and unique components and their responses to injury. Ann N Y Acad Sci, 1434(1), 304-318.

Coding Section 






Microsatellite instability analysis (e.g., hereditary non-polyposis colorectal cancer, Lynch syndrome) of markers for mismatch repair deficiency (e.g., BAT25, BAT26), includes comparison of neoplastic and normal tissue, if performed



Unlisted molecular pathology procedure



Cytopathology, fluids, washings or brushings, except cervical or vaginal; smears with interpretation



Molecular cytogenetics; DNA probe, each (e.g., FISH)



Molecular cytogenetics; chromosomal in situ hybridization, analyze 3 – 5 cells (e.g., for derivatives and markers)



Molecular cytogenetics; chromosomal in situ hybridization, analyze 10 – 30 cells (e.g., for microdeletions)



Molecular cytogenetics; interphase in situ hybridization, analyze 25 – 99 cells



Molecular cytogenetics; interphase in situ hybridization, analyze 100 – 300 cells



Immunohistochemistry or immunocytochemistry, per specimen; each additional single antibody stain procedure (List separately in addition to code for primary procedure)



Immunohistochemistry or immunocytochemistry, per specimen; initial single antibody stain procedure



Immunohistochemistry or immunocytochemistry, per specimen; each multiplex antibody stain procedure



Morphometric analysis, tumor immunohistochemistry (e.g., Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semiquantitative, per specimen, each single antibody stain procedure; manual



Morphometric analysis, tumor immunohistochemistry (e.g., Her-2/neu, estrogen receptor/progesterone receptor), quantitative or semiquantitative, per specimen, each single antibody stain procedure; using computer-assisted technology



Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), using computer-assisted technology, per specimen; initial single probe stain procedure



Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), manual, per specimen; initial single probe stain procedure



Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), manual, per specimen; each additional single probe stain procedure (List separately in addition to code for primary procedure)



Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), using computer-assisted technology, per specimen; each additional single probe stain procedure (List separately in addition to code for primary procedure)



Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), using computer-assisted technology, per specimen; each multiplex probe stain procedure



Morphometric analysis, in situ hybridization (quantitative or semi-quantitative), manual, per specimen; each multiplex probe stain procedure



Inflammation (eosinophilic esophagitis), ELISA analysis of eotaxin-3 (CCL26 [C-C motif chemokine ligand 26]) and major basic protein (PRG2 [proteoglycan 2, pro eosinophil major basic protein]), specimen obtained by swallowed nylon string, algorithm reported as predictive probability index for active eosinophilic esophagitis
Proprietary test: Esophageal String Test™ (EST)
Lab/Manufacturer: Cambridge Biomedical, Inc.



Gastroenterology (Barrett’s esophagus), whole slide-digital imaging, including morphometric analysis, computer-assisted quantitative immunolabeling of 9 protein biomarkers (p16, AMACR, p53, CD68, COX-2, CD45RO, HIF1a, HER-2, K20) and morphology, formalin-fixed paraffin-embedded tissue, algorithm reported as risk of progression to high-grade dysplasia or cancer
Proprietary test: TissueCypher® Barrett's Esophagus Assay
Lab/Manufacturer: Cernostics



Gastroenterology (Barrett’s esophagus), VIM and CCNA1 methylation analysis, esophageal cells, algorithm reported as likelihood for Barrett’s esophagus
Proprietary test: EsoGuard™
Lab/Manufacturer: Lucid Diagnostics

  0386U Gastroenterology (Barrett's esophagus), P16, RUNX3, HPP1, and FBN1 methylation analysis, prognostic and predictive algorithm reported as a risk score for progression to high-grade dysplasia or esophageal cancer
Proprietary test: Envisage
Lab/Manufacturer: Capsulomics, Inc
  0398U (effective 07/01/2023) Gastroenterology (Barrett esophagus), P16, RUNX3, HPP1, and FBN1 DNA methylation analysis using PCR, formalin-fixed paraffin-embedded (FFPE) tissue, algorithm reported as risk score for progression to high-grade dysplasia or cancer

Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each policy. They may not be all-inclusive. 

This medical policy was developed through consideration of peer-reviewed medical literature generally recognized by the relevant medical community, U.S. FDA approval status, nationally accepted standards of medical practice and accepted standards of medical practice in this community, Blue Cross Blue Shield Association technology assessment program (TEC) and other nonaffiliated technology evaluation centers, reference to federal regulations, other plan medical policies, and accredited national guidelines.

"Current Procedural Terminology © American Medical Association. All Rights Reserved" 

History From 2024 Forward     

01012024  NEW POLICY 

05/06/2024 Annual review, policy updated for clarity and consistency. Criteria #2 has been expanded to include specifics about the tumor type and size 

Complementary Content