This data set includes low-dose whole body CT images and tissue segmentations of thirty healthy adult research participants who underwent PET/CT imaging on the uEXPLORER total-body PET/CT system at UC Davis. Participants included in this study were healthy adults, 18 years of age or older, who were able to provide informed written consent. The participants' age, sex, weight, height, and body mass index are also provided.

Fifteen participants underwent PET/CT imaging at three timepoints during a 3-hour period (0 minutes, 90 minutes, and 180 minutes) after PET radiotracer injection, while the remaining 15 participants were imaged at six timepoints during a 12-hour period (additionally at 360 minutes, 540 minutes, and 720 minutes). The imaging timepoint is indicated in the Series Description DICOM tag, with a value of either 'dyn', '90min', '3hr', '6hr', '9hr', or '12hr', corresponding to the delay after PET tracer injection. CT images were acquired immediately before PET image acquisition. Currently, only CT images are included in the data set from either three or six timepoints. The tissue segmentations include 37 tissues consisting of 13 abdominal organs, 20 different bones, subcutaneous and visceral fat, skeletal and psoas muscle. Segmentations were automatically generated at the 90 minute timepoint for each participant using MOOSE, an AI segmentation tool for whole body data. The segmentations are provided in NIFTI format and may need to be re-oriented to correctly match the CT image data in DICOM format.

The uEXPLORER CT scanner is an 80-row, 160 slice CT scanner typically used for anatomical imaging and attenuation correction for PET/CT. The CT scan obtained at 90 minutes was performed with 140 kVp and an average of 50 mAs for all subjects. At all other time-points (0 minutes, 180 minutes, etc.) the CT scan was obtained with 140 kVp and an average of 5 mAs. CT images were reconstructed into a 512x512x828 image matrix with 0.9766x0.9766x2.344 mm3 voxel size.

A key is provided along with the segmentations download in the Data Access table which details the organ values.

We provide a large, annotated dataset of 597 whole-body PSMA-PET/CT studies from 378 male patients with suspected or diagnosed prostate carcinoma. Scans were acquired at LMU University Hospital, LMU Munich (2014–2022) on three clinical PET/CT scanners, jointly operated by the Departments of Nuclear Medicine and Radiology. All PSMA-avid tumor lesions were manually segmented on the PET images in 3D space using a dedicated software solution. 537 studies contain at least one lesion, while 60 show no lesions. The dataset includes anonymized DICOM files, DICOM segmentation masks, and a TSV file with patient age at imaging, PET/CT manufacturer and model name, PET radionuclide, and use of CT contrast agent. This dataset was used in the autoPET III and IV Grand Challenges to enable the development of machine-learning models for automated lesion segmentation in whole-body PET/CT.

Introduction

We provide a large, annotated dataset of whole-body PSMA-PET/CT studies from patients with suspected or diagnosed prostate cancer to support developing and benchmarking machine learning (ML) models for automated quantitative PET/CT analysis. Alongside the FDG-PET/CT dataset on TCIA , this dataset addresses the scarcity of publicly available, high-quality annotated PET/CT data. The FDG and PSMA-PET/CT datasets were jointly provided as training data for developing ML models in the autoPET III and autoPET IV Grand challenges for automated lesion segmentation in whole-body PET/CT.

Methods

Subject Inclusion and Exclusion Criteria

The institutional review board (Ethics Committee, Medical Faculty, LMU Munich), as well as the institutional data security and privacy review board, approved the publication of anonymized data. This retrospective dataset comprises 597 whole-body PSMA-PET/CT studies from 378 male patients (ages 48–92*) with suspected or diagnosed prostate carcinoma.
*Due to PHI criteria, all ages above 89 years in the metadata and the spreadsheet are set to 90 years, regardless of actual age.

Data Acquisition

Scans were conducted at LMU University Hospital, LMU Munich, between 2014 and 2022 using three clinical PET/CT scanners: Siemens Biograph mCT Flow 20, Siemens Biograph 64-4R TruePoint, and GE Discovery 690. 537 studies contain at least one PSMA-avid tumor lesion, 60 studies do not contain any PSMA-avid tumor lesion. The imaging protocol consisted of a diagnostic CT scan usually from the skull base to the mid-thigh with the following scan parameters: reference tube current exposure time product of 143 mAs (mean); tube voltage of 120 kV or 100 kV for most cases (range: [80, 140] kV), slice thickness of 2.5 - 5.0 mm (mean: 2.82 mm), and x-y resolution of mainly 0.98 mm. Intravenous contrast enhancement was used in most studies, except for patients with contraindications (26 studies). The whole-body PSMA-PET scan was acquired on average 74 minutes after intravenous injection of 246 MBq 18F-PSMA (mean, 369 studies) or 214 MBq 68Ga-PSMA (mean, 228 studies), respectively. The PET data was reconstructed with attenuation correction derived from corresponding CT data using standard, vendor-provided image reconstruction algorithms with a slice thickness ranging from 3.0 - 5.0 mm (mean: 3.49 mm) and x-y resolution ranging from 2.73 - 4.07 mm (mean: 3.56 mm).

Data Analysis

All PSMA-avid tumor lesions, including the primary tumor and/or all metastases, were manually segmented on the PET images by a single reader with 3 years of experience in hybrid imaging using dedicated software (mint Medical, Heidelberg, Germany) and validated by board-certified medical imaging experts with 4 years and >10 years of experience in hybrid imaging. Tumor lesions with significantly increased PSMA expression were segmented in 3D space by drawing circular VOIs, in which voxels with uptake values above a user-defined threshold were pre-segmented automatically and then manually corrected slice by slice, resulting in 3D binary segmentation masks and saved as NRRD files by the software. These files were exported and combined to a single segmentation mask per study and converted to DICOM SEG using the highdicom package v0.22.0 in Python v3.8.13. In addition, patient metadata was extracted from imaging DICOM tags: patient age at imaging (in years), PET/CT manufacturer and model name, PET radionuclide, and use of CT contrast agent. Information on radionuclides and the use of CT contrast agents was visually reviewed and validated by a radiologist with 10 years of experience in hybrid imaging.

Usage Notes

For each of the 597 PSMA PET/CT studies, we provide the anonymized original PET and CT DICOM files, and the corresponding segmentation mask as DICOM SEG. To view the DICOM data, we recommend open-source medical image data viewers such as 3D Slicer or the Medical Imaging Interaction Toolkit. For computational analysis, e.g. in Python, 3D image volumes can be read using open-source libraries such as pydicom, nibabel, or SimpleITK.

The patient metadata extracted from DICOM tags is shared in a TSV file. Each row contains the information on one study. Each study is uniquely identified by a case identifier number and the study date.

This dataset contains images of the head which, in theory, could pose re-identification risks using advanced image processing techniques. For this dataset TCIA implemented a “de-facing” pipeline to generate a version of this dataset without identifiable facial features which are published under an open-access license on TCIA. The original unaltered data will be made available at https://general.datacommons.cancer.gov/.

This dataset consists of CT and PET-CT DICOM images of lung cancer subjects with XML Annotation files that indicate tumor location with bounding boxes. The images were retrospectively acquired from patients with suspicion of lung cancer, and who underwent standard-of-care lung biopsy and PET/CT. Subjects were grouped according to a tissue histopathological diagnosis. Patients with Names/IDs containing the letter 'A' were diagnosed with Adenocarcinoma, 'B' with Small Cell Carcinoma, 'E' with Large Cell Carcinoma, and 'G' with Squamous Cell Carcinoma.

The images were analyzed on the mediastinum (window width, 350 HU; level, 40 HU) and lung (window width, 1,400 HU; level, –700 HU) settings. The reconstructions were made in 2mm-slice-thick and lung settings. The CT slice interval varies from 0.625 mm to 5 mm. Scanning mode includes plain, contrast and 3D reconstruction.

Before the examination, the patient underwent fasting for at least 6 hours, and the blood glucose of each patient was less than 11 mmol/L. Whole-body emission scans were acquired 60 minutes after the intravenous injection of 18F-FDG (4.44MBq/kg, 0.12mCi/kg), with patients in the supine position in the PET scanner. FDG doses and uptake times were 168.72-468.79MBq (295.8±64.8MBq) and 27-171min (70.4±24.9 minutes), respectively. 18F-FDG with a radiochemical purity of 95% was provided. Patients were allowed to breathe normally during PET and CT acquisitions. Attenuation correction of PET images was performed using CT data with the hybrid segmentation method. Attenuation corrections were performed using a CT protocol (180mAs,120kV,1.0pitch). Each study comprised one CT volume, one PET volume and fused PET and CT images: the CT resolution was 512 × 512 pixels at 1mm × 1mm, the PET resolution was 200 × 200 pixels at 4.07mm × 4.07mm, with a slice thickness and an interslice distance of 1mm. Both volumes were reconstructed with the same number of slices. Three-dimensional (3D) emission and transmission scanning were acquired from the base of the skull to mid femur. The PET images were reconstructed via the TrueX TOF method with a slice thickness of 1mm.

The location of each tumor was annotated by five academic thoracic radiologists with expertise in lung cancer to make this dataset a useful tool and resource for developing algorithms for medical diagnosis. Two of the radiologists had more than 15 years of experience and the others had more than 5 years of experience. After one of the radiologists labeled each subject the other four radiologists performed a verification, resulting in all five radiologists reviewing each annotation file in the dataset. Annotations were captured using Labellmg. The image annotations are saved as XML files in PASCAL VOC format, which can be parsed using the PASCAL Development Toolkit: https://pypi.org/project/pascal-voc-tools/. Python code to visualize the annotation boxes on top of the DICOM images can be downloaded here.

Two deep learning researchers used the images and the corresponding annotation files to train several well-known detection models which resulted in a maximum a posteriori probability (MAP) of around 0.87 on the validation set.

This study describes a subset of the HNSCC collection on TCIA.

PURPOSE:

Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Publicly shared RT data is scarce due to high complexity of RT structure data and the need for registration in time, space, and across planning sets. We here introduce an open access imaging database for patients treated with radiotherapy for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS:

2840 consecutive patients with HNSCC treated with curative-intent RT at MD Anderson Cancer Center from 2003 to 2013 were screened. Patients with whole-body PET-CT or abdominal CT scans both before and after RT were included (n=215). Clinical data were retrieved from the MD Anderson Cancer Center custom electronic medical record system, ClinicStation. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. All files were de-identified and transferred to The Cancer Imaging Archive servers using the RSNA Clinical Trial Processor program. Files were screened for errors or residual PHI using TagSniffer and Posda Tools software, reviewed by TCIA curators, then confirmed at the parent institution.

RESULTS:

The HNSCC collection is a dataset consisting of 433,384 DICOM files from 3,225 series and 765 studies collected from 215 patients, which includes de-identified diagnostic imaging, radiation treatment planning, and follow up imaging. All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival.

CONCLUSION:

Recent advances in data archiving, patient de-identification, and image registration have allowed for the creation of a high quality RT-enriched imaging database within TCIA. Open access to these data allows for interinstitutional comparisons of complete RT details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing. A related dataset describing the other component of the HNSCC collection is here: Radiomics outcome prediction in Oropharyngeal cancer DOI: 10.7937/TCIA.2020.2vx6-fy46

Positron Emission Tomography Pre- and Post-treatment Assessment for Locally Advanced Non-small Cell Lung Carcinoma

This was a multicenter clinical trial by the ACRIN Cooperative Group (now part of ECOG-ACRIN) and the RTOG Cooperative Group (now part of NRG) using FDG-PET imaging both pre- and post-chemoradiotherapy. The objective of the ACRIN 6668 multi-center clinical trial was to determine if the PET standardized uptake value (SUV) measurement from FDG-PET imaging shortly after treatment is a useful predictor of long-term clinical outcome (survival) after definitive chemoradiotherapy. Eligible patients were those older than 18 years with AJCC-criteria clinical stage IIB/III non-small cell lung carcinoma who were being planned for definitive concurrent chemoradiotherapy (inoperable disease). Additional information about the trial is available in the Study Protocol and Case Report Forms.

Primary Aim Findings:

Higher post-treatment tumor SUV (SUVpeak, SUVmax) is associated with worse survival in stage III NSCLC, although a clear SUV cutoff value for routine clinical use as a prognostic factor was uncertain. Later analyses found that larger pre-treatment metabolic tumor volumes (MTVs) were associated with significantly worse overall survival. Other secondary analyses found potentially predictive image texture biomarkers.

Study Design Summary:

Patients received conventional concurrent platinum-based chemoradiotherapy without surgery; post-radiotherapy consolidation chemotherapy was allowed. A baseline whole-body FDG-PET scan was performed prior to therapy. A second post-treatment whole-body FDG-PET scan occurred approximately 14 weeks after radiotherapy (at least 4 weeks after adjuvant chemotherapy). Pre-treatment FDG-PET scans were performed on ACRIN-qualified scanners. Post-treatment FDG-PET scans were required to be performed within 12–16 weeks after completion of therapy, using the same scanner as that used for the pre-treatment scans.

Two sets of XLS spreadsheets (file set 1 and file set 2) are needed in order to obtain the entire clinical data set for this collection. The file sets are a random sample of ACRIN 6668 participants divided into 2 groups. Group 1/file set 1: a 75% random sample; Group 2: a 25% random sample initially held for testing/validating algorithms trained on the 75% sample.

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The RIDER Phantom MRI data set contains repeat phantom studies. The phantom used for all data acquisitions was a version of the EuroSpin II Test Object 5 as distributed by Diagnostic Sonar, Ltd (Livingston, West Lothian, Scotland). The phantom was comprised of 18 25-mm doped gel filled tubes and 1 20-mm tube containing 0.25 mM GdDTPA.

Scanners evaluated:

• Scanner A – 1.5T GE 8-channel HD with BRM gradient subsystem (33 mT/m amplitude; 120 T/m-s)
• Scanner B – 1.5T GE 8-channel HD with CRM gradient subsystem (50 mT/m amplitude; 150 T/m-s)
• Scanner C – 1.5T Siemens Espree (VB13) with 33 mT/m amplitude, 100 T/m-s gradient subsystem
• Scanner D – 3.0T GE 8-channel HD with TwinSpeed gradients (40 mT/m; 150 T/m-s in zoom mode) For all measurements, an 8-channel phased array head coil was used.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

• RIDER White Paper: Combined contracts report ( Sept 2008) PDF

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

• RIDER White Paper: Executive Summary PDF
• RIDER White Paper: Editorial in Nature.com