DOI: 10.5281/zenodo.18284366
title: NSCLCR-HAID

description: Pre-processed NSCLC-Radiomics Dataset

Resampled CT Scans with 3D Bounding Box Annotations and Clinical Metadata

Description

This dataset provides a pre-processed version of the NSCLC-Radiomics collection, consisting of standardized CT images, 3D bounding box annotations derived from expert tumor segmentations, and harmonized clinical metadata. The original NSCLC-Radiomics dataset includes 422 patients diagnosed with non-small cell lung cancer (NSCLC) and was released through The Cancer Imaging Archive (TCIA).

The goal of this release is to lower the barrier for reproducible medical AI research by offering AI-ready imaging data with consistent spatial resolution, unified annotation formats, and predefined train/validation/test splits.

What’s Included

1. Resampled CT Images (421 patients)





Modality: Computed Tomography (CT)




Format: NIfTI (.nii.gz)




Standardized voxel spacing: 0.703125 × 0.703125 × 1.25 mm³




File naming convention:

NSCLC_{PatientID}_0000.nii.gz




2. 3D Bounding Box Annotations





One bounding box per patient corresponding to the primary tumor (GTV-1)




Derived from expert manual segmentations




Represented in world coordinates (center location and box dimensions in millimeters)




Provided in CSV format for direct use in detection and localization tasks



3. Clinical Metadata and Dataset Splits





Demographic information: age, sex




Clinical variables:





TNM staging




Histological subtype (adenocarcinoma, squamous cell carcinoma, large cell carcinoma, NOS, unknown)






Stratified dataset splits:





Training: 80% (337 patients)




Validation: 10% (42 patients)




Test: 10% (42 patients)





4. Dataset Descriptors





JSON descriptor files for:





3D object detection tasks




Generative and synthesis-based modeling






Annotation-level CSV files for classification and radiomics studies



Processing Pipeline

All CT scans were resampled from heterogeneous native voxel spacings to a uniform resolution of 0.703125 × 0.703125 × 1.25 mm³ using B-spline interpolation. Primary tumor bounding boxes were extracted from expert-annotated GTV-1 segmentations and converted to world-coordinate representations. Clinical metadata were merged with imaging annotations and used to generate reproducible, stratified train/validation/test splits based on histological subtype.

Complete preprocessing scripts and documentation are available in the associated GitHub repository.

Intended Use

This dataset is intended for research and educational purposes only, including:





3D medical image object detection




Lung cancer classification by histological subtype




Radiomics feature extraction and analysis




CT image synthesis and generative modeling




Multi-task and multimodal medical AI research



This resource is not intended for clinical diagnosis or decision-making.

Dataset Statistics





Total patients: 421




Imaging modality: CT




Annotation type: 3D bounding boxes + clinical labels




Training set: 337 patients (80%)




Validation set: 42 patients (10%)




Test set: 42 patients (10%)



File Structure

├── NSCLC-Radiomics-NIFTI.zip/                # Converted NIfTI images
│   ├── LUNG1-001/
│   ├── LUNG1-002/
│   └── ...
├── NSCLC-Radiomics-ct.zip/                   # Original DICOM CONVERTED TO NIFTI CT scans
├── NSCLC-Radiomics-mask.zip/                 # Original DICOM CONVERTED TO NIFTI segmentations
├── vista3Dauto_seg.zip/                      # VISTA3D organ Segmentation NIFTI segmentations├── vista3Dauto_seg_plus_orgGTV.zip/          # VISTA3D organ Segmentation +NSCLC segmentation of abnormalities NIFTI segmentations


Citation

If you use this dataset, please cite both the original NSCLC-Radiomics dataset and publication, as well as this pre-processed version.

Original DatasetAerts, H. J. W. L., et al. (2015).Data From NSCLC-Radiomics.The Cancer Imaging Archive.DOI: 10.7937/K9/TCIA.2015.PF0M9REI

Original PublicationAerts, H. J. W. L., et al. (2014).Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nature Communications, 5, 4006.DOI: 10.1038/ncomms5006

This Pre-processed Dataset[Your Name / Lab]. (2026).Pre-processed NSCLC-Radiomics Dataset with 3D Bounding Box Annotations.Zenodo.DOI: [Zenodo-assigned DOI]

Related Links





Original NSCLC-Radiomics collection:https://www.cancerimagingarchive.net/collection/nsclc-radiomics/




Processing code and documentation:https://github.com/fitushar/HAID




Original publication:https://doi.org/10.1038/ncomms5006



License

This dataset inherits the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license from the original NSCLC-Radiomics collection. All data are de-identified in accordance with HIPAA standards.

Keywords

lung cancer, NSCLC, CT imaging, medical imaging, radiomics, object detection, 3D bounding boxes, deep learning, health AI, clinical datasets