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	Thoracic-cavity 3D segmentation; pleural-effusion 3D segmentation
CT patients	402 (plus 3 extras for mask-coverage: 405 total CT folders)
Thoracic-cavity masks	402 patients (primary reviewer) + 86 (secondary reviewer)
Pleural-effusion masks	78 patients (first + second reviewer) + 16 (third reviewer)
DICOM slices	~48,568
Mask format	NIfTI (.nii.gz)
CT format	DICOM
License	CC BY-NC 3.0 (NSCLC-Radiomics CTs); PleThora masks released under CC BY 3.0. The most restrictive of these (CC BY-NC 3.0) governs the combined upload.

Subsets and Recommended Ground Truth

PleThora ships multiple reviewer tiers for both subsets. The paper's benchmark splits and recommended references are:

Thoracic Cavity

Whole-cavity volume — includes lung, tumor, atelectasis, adhesions, and any effusion inside the cavity. Not a lung-parenchyma mask.

Reviewer	Cases	Role
primary_reviewer	402	Medical-student manual correction of an initial U-Net pre-segmentation. Used as training-set GT in the paper's baseline.
secondary_reviewer	86	Radiologist/expert revision. Recommended GT where available.
primary_reviewer_with_nodal_conglomerate	1	Special variant for a single case where nodal conglomerate was treated separately.

Recommended dataloader behavior: use secondary_reviewer when present (86 paper test cases); fall back to primary_reviewer for the remaining 316.

Pleural Effusion (binary effusion mask)

Reviewer	Cases	Role
first_reviewer	78	Medical-student manual delineation.
second_reviewer	78	Radiologist revision. Recommended GT.
third_reviewer	16	Second radiologist for inter-observer agreement.

Recommended dataloader behavior: always use second_reviewer.

Splits

TCIA does not publish a fixed train/val/test split. The paper's baseline uses the 316 cases with only primary_reviewer thoracic masks for training and the 86 cases with secondary_reviewer thoracic masks for testing. We do not re-shard the data; all patients are placed under images/ and consumers can follow the paper's split using the reviewer tiers above.

Structure

images/<PatientID>/*.dcm                                 # CT DICOM series, one folder per patient (405 folders)
masks/thoracic_cavity/<PatientID>/<PatientID>_<tier>.nii.gz
masks/pleural_effusion/<PatientID>/<PatientID>_<tier>.nii.gz
manifest.json                                            # Per-patient summary: CT slice count, mask tier availability
NSCLC-Radiomics-OriginalCTs.tcia                         # Original PleThora .tcia manifest (provenance)

<tier> is one of thor_cav_primary_reviewer, thor_cav_secondary_reviewer, thor_cav_primary_reviewer_with_nodal_conglomerate, effusion_first_reviewer, effusion_second_reviewer, or effusion_third_reviewer.

Known Mask/CT Mismatches

Three patients in the PleThora mask zips are NOT in the PleThora .tcia CT manifest: LUNG1-083, LUNG1-095, LUNG1-246. We located the matching CT series for each in the broader NSCLC-Radiomics collection and added them to images/ so all mask patients have a paired CT. Three patients are in the CT manifest but have no thoracic-cavity mask: LUNG1-198, LUNG1-203, LUNG1-204. The dataloader should iterate the mask folders, not the CT folders, to skip orphan CTs.

Sources

• TCIA analysis-result page: https://www.cancerimagingarchive.net/analysis-result/plethora/
• Parent CT collection: https://www.cancerimagingarchive.net/collection/nsclc-radiomics/
• TCIA DOI (PleThora): https://doi.org/10.7937/tcia.2020.6c7y-gq39

Citation

@article{kiser2020plethora,
  author  = {Kiser, Kendall J. and Ahmed, Sara and Stieb, Sonja and
             Mohamed, Abdallah S. R. and Elhalawani, Hesham and
             Park, Peter Y. S. and Doyle, Nicolette S. and Wang, Brian J. and
             Barman, Arpan and Li, Zhenyu and Cheng, Wesley and
             Anderjaska, James and Fuller, Clifton D. and Frank, Steven J. and
             Lai, Stephen Y. and Marai, G. Elisabeta and Gunn, G. Brandon and
             Garden, Adam S. and Rosenthal, David I. and Jaffray, David A. and
             Court, Laurence E. and Aerts, Hugo J. W. L.},
  title   = {PleThora: Pleural effusion and thoracic cavity segmentations in
             diseased lungs for benchmarking chest CT processing pipelines},
  journal = {Medical Physics},
  volume  = {47},
  number  = {11},
  pages   = {5941--5952},
  year    = {2020},
  doi     = {10.1002/mp.14424}
}

@misc{plethora2020tcia,
  author    = {Kiser, K. J. and Ahmed, S. and Stieb, S. and others},
  title     = {PleThora: Pleural effusion and thoracic cavity segmentations
               in diseased lungs for benchmarking chest CT processing pipelines
               [Dataset]},
  year      = {2020},
  publisher = {The Cancer Imaging Archive},
  doi       = {10.7937/tcia.2020.6c7y-gq39}
}

@article{aerts2014decoding,
  author  = {Aerts, Hugo J. W. L. and Velazquez, Emmanuel Rios and
             Leijenaar, Ralph T. H. and Parmar, Chintan and Grossmann, Patrick
             and Carvalho, Sara and Bussink, Johan and Monshouwer, Rene and
             Haibe-Kains, Benjamin and Rietveld, Derek and Hoebers, Frank and
             Rietbergen, Michelle M. and Leemans, C. Rene and Dekker, Andre and
             Quackenbush, John and Gillies, Robert J. and Lambin, Philippe},
  title   = {Decoding tumour phenotype by noninvasive imaging using a
             quantitative radiomics approach},
  journal = {Nature Communications},
  volume  = {5},
  pages   = {4006},
  year    = {2014},
  doi     = {10.1038/ncomms5006}
}