Histogram of number of pathology reports associated with each unique tumor ID

Authors

Shang Gao, Oak Ridge National Laboratory, TN
Mohammed Alawad, Oak Ridge National Laboratory, TN
Noah Schaefferkoetter, Oak Ridge National Laboratory, TN
Lynne Penberthy, National Cancer Institute
Xiao-Cheng Wu, LSU Health Sciences Center - New OrleansFollow
Eric B. Durbin, University of Kentucky
Linda Coyle, Information Management Services Inc
Arvind Ramanathan, Argonne National Laboratory, IL
Georgia Tourassi, Oak Ridge National Laboratory, TN

Document Type

Dataset

Publication Date

2020

Publication Title

PLOS one

Abstract

(a) Histograms of the number of occurrences per label for each of the six classification tasks, arranged from most common to least common. For the site, subsite, and histology tasks, we only show the 50 most common labels. Detailed information about each label can be found online in the SEER coding manual at https://seer.cancer.gov/tools/codingmanuals/. (b) Histograms of the number of occurrences per label for each of the six classification tasks, arranged from most common to least common. For the site, subsite, and histology tasks, we only show the 50 most common labels. Detailed information about each label can be found online in the SEER coding manual at https://seer.cancer.gov/tools/codingmanuals/.

DOI

10.1371/journal.pone.0232840.s002

Comments

Original Article: https://doi.org/10.1371/journal.pone.0232840

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Gao, Shang; Alawad, Mohammed; Schaefferkoetter, Noah; Penberthy, Lynne; Wu, Xiao-Cheng; Durbin, Eric B.; Coyle, Linda; Ramanathan, Arvind; and Tourassi, Georgia, "Histogram of number of pathology reports associated with each unique tumor ID" (2020). Data Sets. 89.
https://digitalscholar.lsuhsc.edu/datasets/89
10.1371/journal.pone.0232840.s002