NSCLC
Datasets
All datasets matching “NSCLC”NSCLC-PleuralEffusion
NSCLC-PleuralEffusion (PleThora)
Voxel-level thoracic-cavity and pleural-effusion segmentations on the
NSCLC-Radiomics
CT collection. Published by Kiser et al. (Medical Physics 2020) as PleThora,
"Pleural effusion and thoracic cavity segmentations in diseased lungs for
benchmarking chest CT processing pipelines."
Dataset Details
Field
Value
Modality
CT (chest, contrast and non-contrast mixed)
Body part
Chest — thoracic cavity, pleural effusion
Tasks… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/NSCLC-PleuralEffusion.NSCLC-Radiomics-NIFTI
The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)
The models featured in this repository uses images from the publicly available NSCLC-Radiomics Dataset.
Download the data from TCIA with Classic Directory Name download option.
Converting Format
Convert DICOM images and segmentation to NIFTI format using SimpleITK, pydicom and pydicom-seg. Run:
user@machine:~/NSCLC-Radiomics-NIFTI$ python convert.py
Segmentations
Images will have one of the following… See the full description on the dataset page: https://huggingface.co/datasets/farrell236/NSCLC-Radiomics-NIFTI.NSCLC-Radiomics-Interobserver1
NSCLC-Radiomics-Interobserver1
Multiple-delineation inter-observer / inter-method variability study of
gross-tumour-volume (GTV) contouring on pre-treatment thoracic CT of
non-small-cell lung cancer (NSCLC). For each tumour, five radiation
oncologists independently delineated the GTV twice — once manually
(vis) and once auto-segmentation-assisted then edited (auto) — giving up
to 10 GTV delineations per patient. The collection exists specifically to
quantify contouring… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/NSCLC-Radiomics-Interobserver1.NSCLC-THESIS-WSI-DATASET-EMBEDDING
NSCLC-THESIS-WSI-DATASET-EMBEDDING
Public staging mirror for a thesis pipeline (gated acknowledgment before download).One dataset URL — raw slides under wsi/, precomputed features under embeddings/. Pull only the folder you need.
Repo: thanminh01/NSCLC-THESIS-WSI-DATASET-EMBEDDING
WSI upload of all four cohorts is complete. Integrity checks below passed on 2026-08-14. Embeddings are still empty stubs (thesis Stage 2).
Layout
README.md
manifests/
tooling/… See the full description on the dataset page: https://huggingface.co/datasets/okbro1234/NSCLC-THESIS-WSI-DATASET-EMBEDDING.GAPS-NSCLC-preview
GAPS Medical AI Evaluation Dataset - GAPS-NSCLC-preview
Paper: GAPS: A Clinically Grounded, Automated Benchmark for Evaluating AI Clinicians
Code: https://github.com/AQ-MedAI/MedicalAiBenchEval
Dataset Description
The GAPS Medical AI Evaluation Dataset is a comprehensive evaluation system designed specifically for assessing AI models in clinical scenarios. Based on the GAPS (Grounded, Automated, Personalized, Scalable) methodology, this dataset provides both a curated… See the full description on the dataset page: https://huggingface.co/datasets/AQ-MedAI/GAPS-NSCLC-preview.NSCLC-Radiogenomics
NSCLC-Radiogenomics
Non-small cell lung cancer (NSCLC) radiogenomic dataset on TCIA: pretreatment
CT scans of 211 NSCLC patients with matching gene-expression, clinical, and
mutation data. This HuggingFace mirror contains only the 144 patients with
a DICOM SEG of the primary lung tumor (the segmentation-usable subset).
Dataset Details
Field
Value
Modality
CT (pretreatment, multi-vendor, multi-slice-thickness)
Body part
Lung (primary non-small cell… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/NSCLC-Radiogenomics.