README.md

---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100B<n<1T
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
pretty_name: ColonCancerCTDataset
tags:
- colon cancer
- medical
- cancer
dataset_info:
  features:
  - name: image
    dtype: image
  - name: ImageType
    sequence: string
  - name: StudyDate
    dtype: string
  - name: SeriesDate
    dtype: string
  - name: Manufacturer
    dtype: string
  - name: StudyDescription
    dtype: string
  - name: SeriesDescription
    dtype: string
  - name: PatientSex
    dtype: string
  - name: PatientAge
    dtype: string
  - name: PregnancyStatus
    dtype: string
  - name: BodyPartExamined
    dtype: string
  splits:
  - name: train
    num_bytes: 3537157.0
    num_examples: 30
  download_size: 3538117
  dataset_size: 3537157.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Dataset Card Creation Guide

## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [Personal and Sensitive Information](#personal-and-sensitive-information)
  - [Considerations for Using the Data](#considerations-for-using-the-data)
    - [Social Impact of Dataset](#social-impact-of-dataset)
    - [Discussion of Biases](#discussion-of-biases)
    - [Other Known Limitations](#other-known-limitations)
  - [Additional Information](#additional-information)
    - [Dataset Curators](#dataset-curators)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://portal.imaging.datacommons.cancer.gov]()
- **Repository:** [https://aws.amazon.com/marketplace/pp/prodview-3bcx7vcebfi2i#resources]()
- **Paper:** [https://aacrjournals.org/cancerres/article/81/16/4188/670283/NCI-Imaging-Data-CommonsNCI-Imaging-Data-Commons]()

### Dataset Summary

The dataset in the focus of this project is a curated subset of the National Cancer Institute Imaging Data Commons (IDC), specifically highlighting CT Colonography images. This specialized dataset will encompass a targeted collection from the broader IDC repository hosted on the AWS Marketplace, which includes diverse cancer imaging data. The images included are sourced from clinical studies worldwide and encompass modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET).

In addition to the clinical images, essential metadata that contains patient demographics (sex and pregnancy status) and detailed study descriptions are also included in this dataset, enabling nuanced analysis and interpretation of the imaging data.


### Supported Tasks

The dataset can be utilized for several tasks:
- Developing machine learning models to differentiate between benign and malignant colonic lesions.
- Developing algorithms for Creating precise algorithms for segmenting polyps and other colonic structures.
- Conducting longitudinal studies on cancer progression.
- Assessing the diagnostic accuracy of CT Colonography compared to other imaging modalities in colorectal conditions.

### Languages

English is used for text data like labels and imaging study descriptions.

## Dataset Structure

### Data Instances

The data will follow the structure below:

'''
{
  "image": image.png # A CT image,
  "ImageType": ['ORIGINAL', 'PRIMARY', 'AXIAL', 'CT_SOM5 SPI'] # A list containing the info of the image,
  "StudyDate": "20000101" # Date of the case study,
  "SeriesDate": 20000101" # Date of the series,
  "Manufacturer": "SIEMENS" # Manufacturer of the device used for imaging,
  "StudyDescription": "Abdomen^24ACRIN_Colo_IRB2415-04 (Adult)" # Description of the study,
  "SeriesDescription": "Colo_prone  1.0  B30f" # Description of the series,
  "PatientSex": "F" # Patient's sex,
  "PatientAge": "059Y" # Patient's age,
  "PregnancyStatus": "None" # Patient's pregnancy status,
  "BodyPartExamined": "COLON" # Body part examined
}
'''

### Data Fields

- image (PIL.PngImagePlugin.PngImageFile): The CT image in PNG format
- ImageType (List(String)): A list containing the info of the image
- StudyDate (String): Date of the case study
- SeriesDate (String): Date of the series study
- Manufacturer (String): Manufacturer of the device used for imaging
- StudyDescription (String): Description of the study
- SeriesDescription (String): Description of the series
- PatientSex (String): Patient's sex
- PatientAge (String): Patient's age
- PregnancyStatus (String): Patient's pregnancy status
- BodyPartExamined (String): The body part examined

### Data Splits

|                         | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Average Sentence Length |       |            |      |

## Dataset Creation

### Curation Rationale

The dataset is conceived from the necessity to streamline a vast collection of heterogeneous cancer imaging data to facilitate focused research on colon cancer. By distilling the dataset to specifically include CT Colonography, it addresses the challenge of data accessibility for researchers and healthcare professionals interested in colon cancer. This refinement simplifies the task of obtaining relevant data for developing diagnostic models and potentially improving patient outcomes through early detection. The curation of this focused dataset aims to make data more open and usable for specialists and academics in the field of colon cancer research.

### Source Data

According to [IDC](https://portal.imaging.datacommons.cancer.gov/about/), data are submitted from NCI-funded driving projects and other special selected projects.

### Personal and Sensitive Information

According to [IDC](https://portal.imaging.datacommons.cancer.gov/about/), submitters of data to IDC must ensure that the data have been de-identified for protected health information (PHI).

## Considerations for Using the Data

### Social Impact of Dataset

The dataset tailored for CT Colonography aims to enhance medical research and potentially aid in early detection and treatment of colon cancer. Providing high-quality imaging data empowers the development of diagnostic AI tools, contributing to improved patient care and outcomes. This can have a profound social impact, as timely diagnosis is crucial in treating cancer effectively.

### Discussion of Biases

Given the dataset's focus on CT Colonography, biases may arise from the population demographics represented or the prevalence of certain conditions within the dataset. It is crucial to ensure that the dataset includes diverse cases to mitigate biases in model development and to ensure that AI tools developed using this data are generalizable and equitable in their application.

### Other Known Limitations

The dataset may have limitations in terms of variability and scope, as it focuses solely on CT Colonography. Other modalities and cancer types are not represented, which could limit the breadth of research.

### Licensing Information

https://fairsharing.org/FAIRsharing.0b5a1d

### Citation Information

Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{fedorov2021nci,
  title={NCI imaging data commons},
  author={Fedorov, Andrey and Longabaugh, William JR and Pot, David
  and Clunie, David A and Pieper, Steve and Aerts, Hugo JWL and
  Homeyer, Andr{\'e} and Lewis, Rob and Akbarzadeh, Afshin and
  Bontempi, Dennis and others},
  journal={Cancer research},
  volume={81},
  number={16},
  pages={4188--4193},
  year={2021},
  publisher={AACR}
}
```

[DOI](https://doi.org/10.1158/0008-5472.CAN-21-0950)