A cancer staging client library for Java applications.
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A cancer staging client library for Java applications.

Supported staging algorithms

Starting with version 4.0, the library does not contain any algorithms. The algorithms are maintained in separate repositories which are described below.

EOD (view repository)

Extent of Disease (EOD) is a set of three data items that describe how far a cancer has spread at the time of diagnosis. EOD 2018 is effective for cases diagnosed in 2018 and later.

In each EOD schema, valid values, definitions, and registrar notes are provided for

  • EOD Primary Tumor
  • EOD Lymph Nodes
  • EOD Mets
  • Summary Stage 2018
  • Site-Specific Data Items (SSDIs), including grade, pertinent to the schema

For cancer cases diagnosed January 1, 2018 and later, the NCI SEER program will collect Extent of Disease (EOD) revised for 2018 and Summary Stage 2018. The schemas have been developed to be compatible with the AJCC 8th Edition chapter definitions.

All of the standard setting organizations will collect the predictive and prognostic factors through Site Specific Data Items (SSDIs). Unlike the SSFs, these data items have formats and code structures specific to the data item.

TNM (view repository)

TNM is a widely accepted system of cancer staging. TNM stands for Tumor, Nodes, and Metastasis. T is assigned based on the extent of involvement at the primary tumor site, N for the extent of involvement in regional lymph nodes, and M for distant spread. Clinical TNM is assigned prior to treatment and pathologic TNM is assigned based on clinical information plus information from surgery. The clinical TNM and the pathologic TNM values are summarized as clinical stage group or pathologic stage group.

For each cancer site, or schema, valid values, definitions, and registrar notes are provided for clinical TNM and stage group, pathologic TNM and stage group, and relevant Site-Specific Factors (SSFs).

TNM categories, stage groups, and definitions are based on the Union for International Cancer Control (UICC) TNM 7th edition classification. UICC 7th edition and AJCC 7th edition TNM categories and stage groups are very similar; however, there are some differences.

For diagnosis years 2016-2017, SEER Summary Stage 2000 is required. SEER Summary Stage 2000 should be collected manually unless the registry is collecting Collaborative Stage, which would derive Summary Stage 2000.

Collaborative Staging (view repository)

Collaborative Stage is a unified data collection system designed to provide a common data set to meet the needs of all three staging systems (TNM, SEER EOD, and SEER SS). It provides a comprehensive system to improve data quality by standardizing rules for timing, clinical and pathologic assessments, and compatibility across all of the systems for all cancer sites.


Java 8 is the minimum version required to use the library.

Download the latest JAR or grab via Maven:


or via Gradle:

compile 'com.imsweb.com:staging-client-java:4.3'


More detailed documentation can be found in the Wiki

Get a Staging instance

Everything starts with getting an instance of the Staging object. There are DataProvider objects for each staging algorithm and version. The Staging object is thread safe and cached so subsequent calls to Staging.getInstance() will return the same object.

For example, if you include the collaborative staging algorithm, the call will look like this:

Staging staging = Staging.getInstance(CsDataProvider.getInstance(CsVersion.v020550));

There are some algorithms that are distributed outside of the library. If you need to instantiate a Staging instance with an external algorithm use the ExternalStagingFileDataProvider.

Path path = Paths.get("C:/path/to/algorithm", "external_algorithm.zip");
try (InputStream is = Files.newInputStream(path)) {
    Staging staging = Staging.getInstance(new ExternalStagingFileDataProvider(is));

    // use staging instance


Schemas represent sets of specific staging instructions. Determining the schema to use for staging is based on primary site, histology and sometimes additional discrimator values. Schemas include the following information:

  • schema identifier (i.e. "prostate")
  • algorithm identifier (i.e. "cs")
  • algorithm version (i.e. "02.05.50")
  • name
  • title, subtitle, description and notes
  • schema selection criteria
  • input definitions describing the data needed for staging
  • list of table identifiers involved in the schema
  • a list of initial output values set at the start of staging
  • a list of mappings which represent the logic used to calculate staging output

To get a list of all schema identifiers,

Set<String> schemaIds = staging.getSchemaIds();

To get a single schema by identifer,

StagingSchema schema = staging.getSchema("prostate");


Tables represent the building blocks of the staging instructions specified in schemas. Tables are used to define schema selection criteria, input validation and staging logic. Tables include the following information:

  • table identifier (i.e. "ajcc7_stage")
  • algorithm identifier (i.e. "cs")
  • algorithm version (i.e. "02.05.50")
  • name
  • title, subtitle, description, notes and footnotes
  • list of column definitions
  • list of table data

To get a list of all table identifiers,

Set<String> tableIds = staging.getTableIds();

That list will be quite large. To get a list of table indentifiers involved in a particular schema,

Set<String> tableIds = staging.getInvolvedTables("prostate");

To get a single table by identifer,

StagingTable table = staging.getTable("ajcc7_stage");

Lookup a schema

A common operation is to look up a schema based on primary site, histology and optionally one or more discriminators. Each staging algorithm has a SchemaLookup object customized for the specific inputs needed to lookup a schema.

For Collaborative Staging, use the CsSchemaLookup object. Here is a lookup based on site and histology.

List<StagingSchema> lookup = staging.lookupSchema(new CsSchemaLookup("C629", "9231"));
assertEquals(1, lookup.size());
assertEquals("testis", lookup.get(0).getId());

If the call returns a single result, then it was successful. If it returns more than one result, then it needs a discriminator. Information about the required discriminator is included in the list of results. In the Collaborative Staging example, the field ssf25 is always used as the discriminator. Other staging algorithms may use different sets of discriminators that can be determined based on the result.

// do not supply a discriminator
List<StagingSchema> lookup = staging.lookupSchema(new CsSchemaLookup("C111", "8200"));
assertEquals(2, lookup.size());
for (StagingSchema schema : lookup)

// supply a discriminator
lookup = staging.lookupSchema(new CsSchemaLookup("C111", "8200", "010"));
assertEquals(1, lookup.size());
assertEquals("nasopharynx", lookup.get(0).getId());
assertEquals(Integer.valueOf(34), lookup.get(0).getSchemaNum());

Calculate stage

Staging a case requires first knowing which schema you are working with. Once you have the schema, you can tell which fields (keys) need to be collected and supplied to the stage method call.

A StagingData object is used to make staging calls. All inputs to staging should be set on the StagingData object and the staging call will add the results. The results include:

  • output - all output values resulting from the calculation
  • errors - a list of errors and their descriptions
  • path - an ordered list of the tables that were used in the calculation

Each algorithm has a specific StagingData entity which helps with preparing and evaluating staging calls. For Collaborative Staging, use CsStagingData. One difference between this library and the original Collaborative Stage library is that you do not have pass all 25 site-specific factors for every staging call. Only include the ones that are used in the schema being staged.

CsStagingData data = new CsStagingData();
data.setInput(CsInput.PRIMARY_SITE, "C680");
data.setInput(CsInput.HISTOLOGY, "8000");
data.setInput(CsInput.BEHAVIOR, "3");
data.setInput(CsInput.GRADE, "9");
data.setInput(CsInput.DX_YEAR, "2013");
data.setInput(CsInput.CS_VERSION_ORIGINAL, "020550");
data.setInput(CsInput.TUMOR_SIZE, "075");
data.setInput(CsInput.EXTENSION, "100");
data.setInput(CsInput.EXTENSION_EVAL, "9");
data.setInput(CsInput.LYMPH_NODES, "100");
data.setInput(CsInput.LYMPH_NODES_EVAL, "9");
data.setInput(CsInput.REGIONAL_NODES_POSITIVE, "99");
data.setInput(CsInput.REGIONAL_NODES_EXAMINED, "99");
data.setInput(CsInput.METS_AT_DX, "10");
data.setInput(CsInput.METS_EVAL, "9");
data.setInput(CsInput.LVI, "9");
data.setInput(CsInput.AGE_AT_DX, "060");
data.setSsf(1, "020");

// perform the staging

assertEquals(Result.STAGED, data.getResult());
assertEquals("urethra", data.getSchemaId());
assertEquals(0, data.getErrors().size());
assertEquals(37, data.getPath().size());

// check output
assertEquals("129", data.getOutput(CsOutput.SCHEMA_NUMBER));
assertEquals("020550", data.getOutput(CsOutput.CSVER_DERIVED));

// AJCC 6
assertEquals("T1", data.getOutput(CsOutput.AJCC6_T));
assertEquals("c", data.getOutput(CsOutput.AJCC6_TDESCRIPTOR));
assertEquals("N1", data.getOutput(CsOutput.AJCC6_N));
assertEquals("c", data.getOutput(CsOutput.AJCC6_NDESCRIPTOR));
assertEquals("M1", data.getOutput(CsOutput.AJCC6_M));
assertEquals("c", data.getOutput(CsOutput.AJCC6_MDESCRIPTOR));
assertEquals("IV", data.getOutput(CsOutput.AJCC6_STAGE));
assertEquals("10", data.getOutput(CsOutput.STOR_AJCC6_T));
assertEquals("c", data.getOutput(CsOutput.STOR_AJCC6_TDESCRIPTOR));
assertEquals("10", data.getOutput(CsOutput.STOR_AJCC6_N));
assertEquals("c", data.getOutput(CsOutput.STOR_AJCC6_NDESCRIPTOR));
assertEquals("10", data.getOutput(CsOutput.STOR_AJCC6_M));
assertEquals("c", data.getOutput(CsOutput.STOR_AJCC6_MDESCRIPTOR));
assertEquals("70", data.getOutput(CsOutput.STOR_AJCC6_STAGE));

// AJCC 7
assertEquals("T1", data.getOutput(CsOutput.AJCC7_T));
assertEquals("c", data.getOutput(CsOutput.AJCC7_TDESCRIPTOR));
assertEquals("N1", data.getOutput(CsOutput.AJCC7_N));
assertEquals("c", data.getOutput(CsOutput.AJCC7_NDESCRIPTOR));
assertEquals("M1", data.getOutput(CsOutput.AJCC7_M));
assertEquals("c", data.getOutput(CsOutput.AJCC7_MDESCRIPTOR));
assertEquals("IV", data.getOutput(CsOutput.AJCC7_STAGE));
assertEquals("100", data.getOutput(CsOutput.STOR_AJCC7_T));
assertEquals("c", data.getOutput(CsOutput.STOR_AJCC6_TDESCRIPTOR));
assertEquals("100", data.getOutput(CsOutput.STOR_AJCC7_N));
assertEquals("c", data.getOutput(CsOutput.STOR_AJCC7_NDESCRIPTOR));
assertEquals("100", data.getOutput(CsOutput.STOR_AJCC7_M));
assertEquals("c", data.getOutput(CsOutput.STOR_AJCC7_MDESCRIPTOR));
assertEquals("700", data.getOutput(CsOutput.STOR_AJCC7_STAGE));

// Summary Stage
assertEquals("L", data.getOutput(CsOutput.SS1977_T));
assertEquals("RN", data.getOutput(CsOutput.SS1977_N));
assertEquals("D", data.getOutput(CsOutput.SS1977_M));
assertEquals("D", data.getOutput(CsOutput.SS1977_STAGE));
assertEquals("L", data.getOutput(CsOutput.SS2000_T));
assertEquals("RN", data.getOutput(CsOutput.SS2000_N));
assertEquals("D", data.getOutput(CsOutput.SS2000_M));
assertEquals("D", data.getOutput(CsOutput.SS2000_STAGE));
assertEquals("7", data.getOutput(CsOutput.STOR_SS1977_STAGE));
assertEquals("7", data.getOutput(CsOutput.STOR_SS2000_STAGE));

About SEER

The Surveillance, Epidemiology and End Results (SEER) Program is a premier source for cancer statistics in the United States. The SEER Program collects information on incidence, prevalence and survival from specific geographic areas representing 28 percent of the US population and reports on all these data plus cancer mortality data for the entire country.