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Publicly Available Datasets

Dataset by Clinical Trial

Link to Dataset	Description
http://petiv.utu.fi/improd	IMPROD clinical trial, public access to all imaging data sets, scanned whole mount prostatectomy sections
http://petiv.utu.fi/multiimprod	MULTI-IMPROD clinical trial, public access to all imaging data sets, scanned whole mount prostatectomy sections
http://petiv.utu.fi/flucipro	FLUCIPRO clinical trial, public access to all imaging data sets, scanned whole mount prostatectomy sections

Dataset by Publication Title

Quicksort leave-pair-out cross-validation for ROC curve analysis

Files	Description
voxel_data.csv	Voxel-wise features using DWI decay modelling parameters: ADCm, ADCk and K, and corresponding Gabor textures from each parametric map.

Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer

Files	Description
Split_2_Basic_Kallikreins_180.csv	Basic clinical and kallikreins data for split 2 training set.
Split_2_ADC_T2W_WG_Radiomics_363_692.csv	ADC and T2W whole gland radiomics for 363 cases in data split 2.
Split_2_ADC_T2W_Lesion_Radiomics_292_1797.csv	ADC and T2W lesion radiomics for 292 lesions in data split 2.

How to read biparametric MRI in men with a clinical suspicious of prostate cancer: Pictorial review for beginners with public access to imaging, clinical and histopathological database

Files	Description
bpMRI_Imaging_Review_SUPP_material.zip	Supporting material Figures.

Can the positive predictive value of prostate MRI in correlation with biopsy findings be interpreted without diving into details?

Files	Description
PPV_Radiology_Letter_SUPP_2020_05_19.pdf	Supporting material as pdf file.
PROMOD_DataDictionary_2020-05-19.xlsx	PROMOD data dictionary as excel file.

Prediction of prostate cancer aggressiveness using 18F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI

Files	Description
link to the article	Open access to the article at nature.com
FLUCIPRO_mpMRI.pdf	mpMRI acquisition protocol
FLUCIPRO_PET_MRI.ExamCard	PET/MRI acquisition protocol
Supporting_Table_S1_FLUCIPRO_All_used_Variables.xlsx	All region-of-interest variables used in the analyses
http://petiv.utu.fi/flucipro	Free public access to all imaging data sets, scanned whole mount prostatectomy sections

Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score

Files	Description
mpMRI_reporting_schemes_Reader_B.zip	Reporting schema Reader B
mpMRI_reports.zip	Report for each case
Supporting materials	Link to supporting materials

Radiomics and machine learning of multisequence multiparametric prostate MRI: towards improved non-invasive prostate cancer characterization

Files	Description
link to the article	Open access to the article at journals.plos.org
supporting_figures.zip	Files DWI-Mono-ADCm-xxx.png, T2-fitted-xxx.png, and T2w-std-xxx.png correspond to ADCm and T2 parametric maps, and T2-weighted images of each patient, respectively. On the first row of slices they show positions of regions of interest placed on the prostate cancer lesions (red, yellow) and around whole prostate (white). The prostate mask is on the second row, while the remaining rows are lesion masks. Files histology-xx.jpg contain the whole mount prostatectomy sections of each patient, with tumor outlines in green. Please note that identical MRI acquisition protocol has been used on all patients, including slice thickness. Here all prostate cancer masks are show with corresponding whole mount prostatectomy sections.
lesion_radiomics.zip	This file contains 7105 radiomics calculated for each 100 lesion from 62 patients in CSV format (comma-separated values). The first column contains the patient ID number. The second column contains the Gleason score group used for classifying: -1 for low score group, 1 for high score group. The rest of the columns are the calculated feature averages over each lesion. The first row with column description contains the feature names.

Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

Files	Description
README.txt
https://github.com/haanme/ProstateFeatures	Feature extraction algorithms
Scan_1_QC	Quality control files for 1st repetition
Scan_2_QC	Quality control files for 2nd repetition
Features_ADCk_N78_rep1.txt	Most relevant features of 1st repetition of ADCk in training/validation set
Features_ADCk_N78_rep2.txt	Most relevant features of 2nd repetition of ADCk in training/validation set
Features_K_N78_rep1.txt	Most relevant features of 1st repetition of K in training/validation set
Features_K_N78_rep2.txt	Most relevant features of 2nd repetition of K in training/validation set