This dataset contains 60 computed tomograpy (CT) scans with XXX pulmonary nodules (PN) tributary of surgery. By means of a biopsy, every PN was diagnosed between benign and malign. A respiratory medicine physician with seven years of experience annotated the Region of Interest (ROI) of each PN using the 3D-Slicer software. Scan acquisition parameters in all cases were 120 kv, 100-350 mA (dose modulation range), soft tissue reconstructions, high frequency algorithms. A more detailed description can be found in the article “An Intelligent Radiomic Approach for Lung Cancer Screening”.

Informed consent was obtained from all subjects involved in the study.
The study was conducted according to the guidelines of the Declaration of Helsinki—Fortaleza/Brazil, 2013, and approved by the Institutional Review Board of Hospital Universitari Germans Trias i Pujol (protocol code PI-19-169 and date 6 September 2019). (11,6 GB. hash: xxxxxx)
The CT scans are annomized and in NifTI format. (11 MB. hash: xxxxxx)
Each pulmonary nodule (PN) has an annotated Region of Interest (ROI) that is represented in a file. For example, lets R_1.acsv be a file generated with 3D-Slicer that contains a ROI represented by the following lines:
line 24. # pointColumns = type|x|y|z|sel|vis
line 25. point|82.3079|-85.7626|102.94|1|1
line 26. point|11.9305|16.9547|10.8962|1|1
The line 24 describe the positional items of the next two lines. The ROI is represented with a central point (x, y, z), that is in the line 25, and a shift in both directions (+/-) of each axis by the line 26. It means that the ROI is delimitated by the two points:
Point1 = (71.3774, −102.7173, ) and
Point2 = (95.2384, −68,8079, ).
Point1 and Point2 are delimitating the ROI that contains the pulmonary nodule image. These points are in the world system coordinate with respecto to the scan that must be mapped to the voxel coordinate to be used to extract the ROI nodule. In this way, the affine matrix must be used to mapping from scanner coordinate to voxel coordinate. After this mapping, the bounding box can be used to extract the pulmonary nodule from a CT scan.


If you want to use this dataset in your research, please cite this database as: Torres, G.; Baeza, S.; Sanchez, C.; Guasch, I.; Rosell, A.; Gil D. An Intelligent Radiomic Approach for Lung Cancer Screening. Appl. Sci. 2022.


Computer Science Department, Computer Vision Center (CVC), Universitat Autònoma de Barcelona (UAB), Barcelona, España.
Direcció Clínica de l’Àrea del Tòrax, Germans Trias i Pujol University Hospital, Barcelona, España.


This project is supported by the Ministerio de Ciencia e Innovación (MCI), Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), RTI2018-095209-B-C21 (MCI/AEI/FEDER, UE), Generalitat de Catalunya, 2017-SGR-1624 and CERCA-Programme.
Acadèmia de Ciencies Mèdiques i de Salut de Catalunya i Balears, Barcelona Respiratory Network (BRN), Fundació Ramon Pla i Armengol, Talents Grant – Germans Trias and La Pedrera, Lung Ambition Alliance (LAA).

Get the database

Start Download

Back to Top