Deform-registered rectal contours for prostate cancer patients - 373 contours from 37 patientsdoi:10.18710/DKVPIJDataverseNO2023-11-061Rørtveit, Øyvind Lunde, 2023, "Deform-registered rectal contours for prostate cancer patients - 373 contours from 37 patients", https://doi.org/10.18710/DKVPIJ, DataverseNO, V1Deform-registered rectal contours for prostate cancer patients - 373 contours from 37 patientsdoi:10.18710/DKVPIJRørtveit, Øyvind LundeHysing, Liv BolstadPilskog, SaraEkanger, ChristianThor, MariaHaukeland University Hospital, Bergen, NorwayMatlabMatterhornDataverseNOUniversity of BergenRørtveit, Øyvind LundeRørtveit, Øyvind Lunde2023-10-02Medicine, Health and Life SciencesRadiotherapyProstatic NeoplasmsRectumContoursDeformable registrationContours of the recti of 37 prostate cancer patients from Haukeland University Hospital years 2007-2009. The patients were part of a study on hypofractionated radiotherapy with integrated boost, as desribed in Ekanger et al. (2018). The data is now fully anonymous, as the identification keys have been deleted. Each patient had a planning CT plus 6-10 CT repeat CT scans taken during the course of radiotherapy. The rectum was delineated on every scan by an expert physicist. The contours are represented as polyhedra, where each shape contour consist of a set of of 18063 points, with x, y and z-coordinates, on the organ surface. The points are tied together by 36168 triangular faces. The definition of of the triangular faces are given by an array of indices into the arrays of contour points. All contours have been deformably registered to a reference shape; therefore, there is point-to-point correspondence between all shapes in the dataset. This makes the data ideal for statistical modelling. This data was used in the models of Rørtveit et al. (2021) and Rørtveit et al. (2023). A description of the patients, treatment and imaging is given in Hysing et al. (2018). Two representations of the data are available in this dataset; firstly a Matlab .mat file which contains all the data. For users who do not use Matlab, the data is also made available as text files in the form of comma separated values.2007-01-012009-12-31Clinical data<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Rørtveit ØL, Hysing LB, Stordal AS, Pilskog S. An organ deformation model using Bayesian inference to combine population and patient-specific data. Phys Med Biol. 2023;68(5):055009. doi:10.1088/1361-6560/acb8fc10.1088/1361-6560/acb8fcRørtveit ØL, Hysing LB, Stordal AS, Pilskog S. An organ deformation model using Bayesian inference to combine population and patient-specific data. Phys Med Biol. 2023;68(5):055009. doi:10.1088/1361-6560/acb8fcRørtveit ØL, Hysing LB, Stordal AS, Pilskog S. Reducing systematic errors due to deformation of organs at risk in radiotherapy. Medical Physics. 2021;48(11):6578-6587. doi:10.1002/mp.1526210.1002/mp.15262Rørtveit ØL, Hysing LB, Stordal AS, Pilskog S. Reducing systematic errors due to deformation of organs at risk in radiotherapy. Medical Physics. 2021;48(11):6578-6587. doi:10.1002/mp.15262Hysing LB, Ekanger C, Zolnay Á, et al. Statistical motion modelling for robust evaluation of clinically delivered accumulated dose distributions after curative radiotherapy of locally advanced prostate cancer. Radiother Oncol. 2018;128(2):327-335. doi:10.1016/j.radonc.2018.06.00410.1016/j.radonc.2018.06.004Hysing LB, Ekanger C, Zolnay Á, et al. Statistical motion modelling for robust evaluation of clinically delivered accumulated dose distributions after curative radiotherapy of locally advanced prostate cancer. Radiother Oncol. 2018;128(2):327-335. doi:10.1016/j.radonc.2018.06.004Ekanger, C., Helle, S.I., Heinrich, D., Johannessen, D.C., Karlsdóttir, Á., Nygård, Y., Halvorsen, O.J., Reisæter, L., Kvåle, R., Hysing, L.B., Dahl, O., 2020. Ten-Year Results From a Phase II Study on Image Guided, Intensity Modulated Radiation Therapy With Simultaneous Integrated Boost in High-Risk Prostate Cancer. Adv Radiat Oncol 5, 396–403. https://doi.org/10.1016/j.adro.2019.11.00710.1016/j.adro.2019.11.007Ekanger, C., Helle, S.I., Heinrich, D., Johannessen, D.C., Karlsdóttir, Á., Nygård, Y., Halvorsen, O.J., Reisæter, L., Kvåle, R., Hysing, L.B., Dahl, O., 2020. Ten-Year Results From a Phase II Study on Image Guided, Intensity Modulated Radiation Therapy With Simultaneous Integrated Boost in High-Risk Prostate Cancer. Adv Radiat Oncol 5, 396–403. https://doi.org/10.1016/j.adro.2019.11.007patientdata.matA MATLAB(TM) data-file containing all data. Contains the array "faces" which defines the polyhedron faces by referencing into the column numbers of the connected surface points. The struct array "patientData" contains all shapes for all patients. Each entry in "patientData" contains two fields, "id" which is a number that can be used to refer to individual patients, and a cell-array called contourPoints, which contains the seven to eleven rectum contour shapes for each patient. Each cell in the cell array is a 18063-by-3 matrix, where each row represents a point in 3D, and the columns represent x, y and z-coordinates, respectively. application/matlab-matRectal_contours.zipZip archive containing all shapes stored as CSV-text files. Contains the same data as in the MATLAB file, but in text format (CSV, comma separated values). The zip file contains the file "polyhedron_faces.txt" in the root folder. This file contains an array defining the polyhedron faces. Each row in this array contains three indices (0-based) into the surface point arrays, together, these three points define a triangular face. Furthermore, the zip archive contains 37 folders, one for each patient. The folders are named "Patient <ID>", where <ID> is a number used to refer to the patient. Each folder contains between seven and eleven csv files called "shape <number>", where <number> is between 1 and 11. The first shape (number 1) is from the planning CT; the remaining shapes are recorded during the course of radiotherapy. They are not guaranteed to be in chronological order. The csv files contain 18063 lines, each with three entries. The lines represent points in 3D, and three entries in each line represent x, y and z coordinates, respectively.application/zip00_readme.txtReadme file. Contains documentation and metadata for the data set.text/plain