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Cancer

Presentation

We provide the CancerFromICD10 class to extract visits or patients with cancer related ICD10 code

Available cancer types
  • Anus
  • Biliary_duct
  • Bladder
  • Bowel
  • Breast
  • CNS
  • CUP
  • Cervix
  • Colon
  • Endometrium
  • Eye
  • Gastric
  • Head_neck
  • Hodgkin_lymphoma
  • Kidney
  • Leukemia
  • Liver
  • Lung
  • Melanoma
  • Mesothelioma
  • Myeloma
  • Nonhodgkin_lymphoma
  • Oesophagus
  • Osteosarcoma
  • Other_digestive
  • Other_endocrinial
  • Other_gynecology
  • Other_hematologic_malignancies
  • Other_pneumology
  • Other_skin
  • Other_urothelial
  • Ovary
  • PNS
  • Pancreas
  • Prostate
  • Rectum
  • Soft_tissue
  • Testis
  • Thyroid

How it works

The algorithm works by looking for either DP ou DR ICD10 codes associated with cancer. The codes terminology comes from this article1 and is available under CancerFromICD10.ICD10_CODES

Usage

By default, all cancer types mentionned above are extracted

from eds_scikit.io import HiveData
data = HiveData(DBNAME)

from eds_scikit.phenotype import CancerFromICD10

cancer = CancerFromICD10(data)
data = cancer.to_data()

To choose a subset of cancer types, use the cancer_types argument:

cancer = CancerFromICD10(
    data,
    cancer_types=[
        "Eye",
        "Liver",
        "Leukemia",
    ],
)

The final phenotype DataFrame is then available at data.computed["CancerFromICD10"]

Optional parameters

PARAMETER DESCRIPTION
data

A BaseData object

TYPE: BaseData

cancer_types

Optional list of cancer types to use for phenotyping

TYPE: Optional[List[str]] DEFAULT: None

level

On which level to do the aggregation, either "patient" or "visit"

TYPE: str DEFAULT: 'patient'

subphenotype

Whether the threshold should apply to the phenotype ("phenotype" column) of the subphenotype ("subphenotype" column)

TYPE: bool DEFAULT: True

threshold

Minimal number of events (which definition depends on the level value)

TYPE: int DEFAULT: 1

Citation

You can get the BibTex of the corresponding article1 by calling

cancer.cite()

@article{kempf2022impact,
  title={Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals},
  author={Kempf, Emmanuelle and Priou, Sonia and Lam{\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others},
  journal={International Journal of Cancer},
  volume={150},
  number={10},
  pages={1609--1618},
  year={2022},
  publisher={Wiley Online Library}
}

Reference

Check the code reference here for a more detailled look.

  1. Emmanuelle Kempf, Sonia Priou, Guillaume Lamé, Christel Daniel, Ali Bellamine, Daniele Sommacale, Yazid Belkacemi, Romain Bey, Gilles Galula, Namik Taright, and others. Impact of two waves of sars-cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: a french multicentric cohort study from a large group of university hospitals. International Journal of Cancer, 150(10):1609–1618, 2022. ↩↩

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