When you have 3.5 million cancer cases to study, you can draw certain types of inferences that could not possibly be made with the data accumulated at a single medical institution.
Today, we'll look at the neoplasms that occur in appendix of the colon.
Here are the SEER listings. The left-hand column is the average age of occurrence of each neoplasm. The middle column is the number of cases in the SEER collection (neoplasms with fewer than 20 SEER cases were considered un-informative and were omitted from the list). The column on the right is the ICD-O term.
Age of Neoplasm name
039 022 carcinoid tumor, argentaffin, malignant
040 434 carcinoid tumor, malignant
050 217 adenocarcinoid tumor
054 224 mucocarcinoid tumor, malignant
055 038 composite carcinoid
058 022 carcinoma nos
058 138 signet ring cell carcinoma
059 186 mucin-producing adenocarcinoma
060 640 mucinous adenocarcinoma
061 175 mucinous cystadenocarcinoma nos
063 649 adenocarcinoma nos
065 033 adenocarcinoma in tubulovillous adenoma
067 063 adenocarcinoma in villous adenoma
Though many different kinds of malignant neoplasms can occur in the appendix (and can be found in the SEER data), only carcinoids and adenocarcinomas occur frequently.
All of the carcinoid tumors cluster within a younger average age of occurrence than the adenocarcinomas.
This tells us a few things:
1. All of the carcinoids are biologically related to each other.
2. The carcinoids have a different developmental history than the adenocarcinomas.
3. When a pathologist sees a focus of adenocarcinoma in an appendiceal tumor, particularly in a young or middle-aged patient, he or she should carefully look for a focus of carcinoid, because the tumor might be a mixed adenocarcinoid tumor.
This is an example of how to use the SEER data to examine and test existing hypotheses and to develop new hypotheses. It took under a minute to generate the table, using a Perl script that parsed through 3.5 million SEER records.
In a prior blog, I discussed some of the simple Perl routines used in the SEER-data parsing algorithms, and these are available from my web site.
If you want to do creative data mining, you will need to learn a little computer programming.
For Perl and Ruby programmers, methods and scripts for using SEER and other publicly available biomedical databases, are described in detail in my prior books:
Perl Programming for Medicine and Biology
Ruby Programming for Medicine and Biology
An overview of the many uses of biomedical information is available in my book,
More information on cancer is available in my recently published book, Neoplasms.
© 2008 Jules Berman
key words: neoplasms, cancer, neoplasia, precancer, tumor, tumour, tumors, tumours, neoplasm, carcinogenesis, carcinogens, tumor genetics, myelodysplastic syndromes, IEN, pre-cancer, preneoplastic lesions, preneoplasia
As specified in the SEER Data Agreement, the citation for the SEER data is as follows:
"Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Limited-Use Data (1973-2005), National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch, released April 2008, based on the November 2007 submission."
As with all of my scripts, lists, web sites, and blog entries, the following disclaimer applies. This material is provided by its creator, Jules J. Berman, "as is", without warranty of any kind, expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. in no event shall the author or copyright holder be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the material or the use or other dealings in the material.