cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/s17z-r072
title: A DICOM dataset for evaluation of medical image de-identification (Pseudo-PHI-DICOM-Data)

description: Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain.  The process of image de-identification is time consuming, requires significant human resources, and is prone to human error.  Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted.  This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms.
DOI: 10.7937/tcia.e3sv-re93
title: Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations
title: Duke-Breast-Cancer-MRI

description: Breast MRI is a common image modality to assess extent of disease in breast cancer patients. Recent studies show that MRI has a potential in prognosis of patient sort and long term outcomes as well as predicting pathological and genomic features of the tumors. However, large, well annotated datasets are needed to make further progress in the field. We share such a dataset here.
In this dataset, we share MRI imaging and other data for 922 patients with invasive breast cancer. Their prone position axial breast MRI images were acquired by 1.5T or 3T scanners. Following MRI sequences are shared: a non-fat saturated T1-weighted sequence, a fat-saturated gradient echo T1-weighted pre-contrast sequence, and mostly three to four post-contrast sequences.
The images are associated with a variety of metadata. Specifically, we are sharing:
1.	Demographic, clinical, pathology, treatment, outcomes, and genomic data. This data has been collected from a variety of sources including clinical notes and has served as a source for multiple published papers on radiogenomics, outcomes prediction, and other.
2.	529 imaging features which represent a variety of imaging characteristics including size, shape, texture, and enhancement of both the tumor and the surrounding tissue, which is combined of features commonly published in the literature, as well as the features developed in our lab. These features were used in our previous paper (https://doi.org/10.1016/j.eswa.2017.06.029 )
3.	Annotation boxes provided by radiologists that indicate locations of the lesions in the images.
DOI: 10.7937/tcia.jhqd-fr46
title: Nascent prostate cancer heterogeneity drives evolution and resistance to intense hormonal therapy

description: In order to assess mechanisms predictive of response or resistance to intense neoadjuvant therapy, we performed automated immunohistochemistry on targeted biopsies from patients with intermediate or high risk prostate cancer. All biopsy were stained with H or antibodies against: p53, PTEN, AR, PSA, GR, Ki67, SYP and PIN4-cocktail (p63+CK5+K18+AMACR). We further performed automated immunohistochemistry on whole mount radical prostatectomy tissues containing residual tumor. RP tissues were stained with H or antibodies against: PTEN, AR, AR-V7, PSA, PSMA, GR, Ki67, SYP and PIN4-cocktail (p63+CK5+K18+AMACR), with some slides stained for other markers of residual tumor, to include NKX3.1 and CAM5.2. Slides were scanned at 20x magnification. For each patient, response to intense neoadjuvant therapy is known. As biopsies were targeted to MRI-visible lesions, lesion-level response to therapy is also known. For each biopsy, gene expression profile by RNA-seq and exome sequencing were performed on tumor tissue laser capture microdissected from serial sections. Up to 4 foci were dissected per biopsy. Whole-genome of germline DNA from each patient was also performed. Exome and gene expression data corresponding to pre-treatment biopsy tissues has been deposited in dbGaP and GEO. Genome, exome and expression data from post-treatment radical prostatectomy tissues is underway. As slides were stained using an automated slide stainer, this is a rich resource for performing data mining for assessing histogenomic correlates or signatures of treatment response or resistance. Our goal is also to eventually deposit the pre- and post-treatment mpMRI data from this cohort that matches the tissue, but those data are not ready for deposit and thus cannot be deposited at this time.
DOI: 10.7937/v8h6-8x67
title: Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations
title: BraTS-Africa

description: The BraTS-Africa dataset contains a curated and annotated dataset from Africa. It includes scans of 95 patients diagnosed with glioma and 51 patients diagnosed with other neoplasms of the brain. The dataset is a collection of retrospective pre-operative scans, acquired from six diagnostic centers in Nigeria. The brain scans were multiparametric MR images (mpMRI), specifically T1, T1 CE, T2, and T2 FLAIR,  acquired on 1.5T MRI between January 2010 and December 2022. The MRI data were pre-processed according to the Brain Tumor Segmentation (BraTS) image processing protocol. The expert-annotated tumor subregions for each of the 146 cases are provided along with a metadata (csv file) of study location, scanner type, where available.
DOI: 10.7937/31v8-4a40
title: Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID Open Research Database (RICORD) Release 1b - Chest CT Covid- 
title: MIDRC-RICORD-1B

description: The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose: To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.
DOI: 10.7937/3fyc-ac78
title: Multimodal imaging of ductal carcinoma in situ with microinvasion (HE-vs-MPM)

description: Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. The dataset contains data from 12 breast cancer patients, including 10 cases of DCISM, 1 case of DCIS, and 1 case of invasive breast cancer. It consists of H&E-stained histopathology images and CK5/6 immunohistochemical staining images of breast cancer pathological slides for the 12 patients. Additionally, there are 79 multiphoton images capturing suspicious microinvasive cancer nest regions. It can provide multi-perspective and multimodal imaging support for diagnostic research on DCISM.
DOI: 10.7937/3ppx-7s22
title: CT Ventilation as a functional imaging modality for lung cancer radiotherapy (CT-vs-PET-Ventilation-Imaging)
title: CT-vs-PET-Ventilation-Imaging

description: For this study, 20 lung cancer patients underwent exhale/inhale breath hold CT (BHCT), free-breathing four-dimensional CT (4DCT) and Galligas PET ventilation scans in a single session on a combined 4DPET/CT scanner. The purpose of the study was to enable comparisons between: (i) CT ventilation images derived from exhale/inhale BHCT scans, (ii) CT ventilation images derived from free-breathing 4DCT scans, and (iii) Galligas PET (nuclear medicine) ventilation scans. This dataset can build the international capacity for prototyping and evaluating new CT ventilation imaging technologies.

All image acquisitions were performed on a Siemens Biograph mCT.S/64 PET/CT scanner (Siemens, Knoxville, USA) at the Royal North Shore Hospital between 2013 and 2015. A total of 20 4DCT scans, 20 inhale/exhale BHCT scans, 20 Galligas PET scans and 19 attenuation CT scans (missing for CT-PET-VI-07) were successfully acquired for the 20 patients and included in this dataset.

For the exhale/inhale BHCT scans, patients were instructed to hold their breath at approximately 80% of maximum inhalation and exhalation, with Audiovisual Biofeedback used to help guide the breath hold procedure. Settings for the BHCT image acquisitions were: 120 kVp, 120 mAs, 0.8 pitch with a breath-hold time of 10s. The field of view for the CT images was approximately 50 cm from the pharynx to the stomach. Meanwhile the 4DCT scans were acquired with the use of a respiratory motion sensor, the Anzai AZ-733V system (Anzai Medical Co., Tokyo, Japan) for retrospective sorting of CT slices into 10 respiratory phase bins; the exhale and inhale phase bins are provided with the present dataset. 4DCT scans were performed using a helical acquisition and tube settings 120 kVp, 80-200 mA, with 0.5s gantry rotation and 0.09 pitch. The Galligas PET scans (and corresponding attenuation correction CT) were acquired under free-breathing using a standard non-gated protocol. Galligas PET scans were acquired at 2 bed positions of 5 min each, with attenuation correction using a low dose CT (120 kVp; 0.8 pitch, 50 mAs).
DOI: 10.7937/7k9k-3c83
title: University of Missouri Post-operative Glioma Dataset

description: This dataset includes MR imaging from 203 glioma patients with 617 different post-treatment MR time points, and tumor segmentations. Clinical data includes patient demographics, genomics, and treatment details. Preprocessing of MR images followed a standardized pipeline with automatic tumor segmentation based on nnUNet deep learning approach. The automatic tumor segmentations were manually validated and refined by neuroradiologists.

The heterogeneity of glioma imaging characteristics and management strategies contributes to a lack of reliable findings when evaluating treatment outcomes with conventional MRI, and the overlapping imaging features of radiation necrosis and tumor progression post-treatment can be particularly challenging for radiologists. This robust dataset should contribute to the development of AI models to improve evaluation of treatment outcomes.
DOI: 10.7937/9jer-g980
title: Annotations for Rituximab and Combination Chemotherapy in Treating Patients With Diffuse Large B-Cell Non-Hodgkin's Lymphoma (CALGB50303-Tumor-Annotations)
title: CALGB50303-Tumor-Annotations

description: This dataset contains image annotations derived from the NCI Clinical Trial "Rituximab and Combination Chemotherapy in Treating Patients With Diffuse Large B-Cell Non-Hodgkin's Lymphoma (CALGB50303)”.  The key objective of this project is to generate a large and highly curated imaging dataset of pediatric Hodgkin lymphoma patients with annotations suitable for cancer researchers and AI developers.

Annotation Protocol
For each patient, CapeStart reviewed every DICOM Study and DICOM Series to identify and annotate the clinically relevant time points and sequences. In a typical patient all available time points will be annotated:

In a typical patient the following annotation rules were followed:

a.    PERCIST criteria was followed. Specifically, the lesions estimated to have the most elevated SUVmax were annotated. 
b.    Lesions were annotated in the axial plane. If no axial plane were available, lesions were annotated in the coronal plane. 
c.    Lesions were annotated on the attenuation corrected PET series as well as the diagnostic contrast enhanced CT. If no diagnostic contrast enhanced CT was available for that timepoint, then the non contrast CT portion of the PET/CT was annotated. 
d.    A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. For the purposes of this project, the lymph nodes constitute 1 organ, while other lymphatic structures such as the spleen, salivary glands, and Waldeyer’s ring structures constitute separate organs.  The same 5 lesions were annotated at each time point.  RECIST 1.1 principles were followed. Specifically, lymph nodes were annotated if > 1.5 cm in short axis. Other lesions were annotated if > 1 cm.  
e.    Lesions were labeled separately.
f.    Seed points were automatically generated and reviewed by a radiologist.

At each time point:

A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file. 
SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
“Tracking ID” and “Tracking UID” tags were inserted for each segmented structure to enable longitudinal lesion tracking.
Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol. 
“Clinical Trial Time Point ID” was used to encode time point type using one of the following strings as applicable: “pre-dose” or “post-chemotherapy”
Content Item in “Acquisition Context Sequence” was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
(255235001, SCT, “Pre-dose”)
(262502001, SCT, "Post-chemotherapy")
DOI: 10.7937/9wkk-q141
title: A Curated Benchmark Dataset for Ultrasound Based Breast Lesion Analysis (Breast-Lesions-USG)

description: This dataset consists of 256 breast ultrasound scans collected from 256 patients and 266 benign and malignant segmented lesions.  It includes patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or biopsy result. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image via a freehand annotation and labeled according to BIRADS features. The tumor histopathological classification is stated for patients who underwent a biopsy. Patient-level labels include clinical data such as age, breast tissue composition, signs and symptoms. Image-level freehand annotations identify the tumor and other abnormal areas in the image. The tumor and image are labeled with BIRADS category, 7 BIRADS descriptors, and interpretation of critical findings as presence of breast diseases. Additional labels include the method of verification, tumor classification and histopathological diagnosis.
DOI: 10.7937/bw9v-bx61
title: Annotations for The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA-Tumor-Annotations)

description: This dataset contains image annotations derived from "The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA)”.  This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with annotations that will improve their value for cancer researchers and artificial intelligence experts.
For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. In a typical patient all available time points were annotated.
DOI: 10.7937/qxk2-qg03
title: Preoperative CT and Survival Data for Patients Undergoing Resection of Colorectal Liver Metastases (Colorectal-Liver-Metastases)
title: Colorectal-Liver-Metastases

description: This collection consists of DICOM images and DICOM Segmentation Objects (DSOs) for 197 patients with Colorectal Liver Metastases (CRLM). The collection consists of a large, single-institution consecutive series of patients that underwent resection of CRLM and matched preoperative computed tomography (CT) scans for quantitative image analysis. Inclusion criteria were (a) pathologically confirmed resected CRLM, (b) available data from pathologic analysis of the underlying non-tumoral liver parenchyma and hepatic tumor, (c) available preoperative conventional portal venous contrast-enhanced multi-detector computed tomography (MDCT) performed within 6 weeks of hepatic resection. Patients with 90-day mortality or that had less than 24 months of follow-up were excluded.
DOI: 10.7937/rrse-w278
title: MRI Dataset of Metastatic Breast Cancer to the Brain with Expert-reviewed Segmentations and Tumor-derived Radiomic Features (BCBM-RadioGenomics)

description: This dataset consists of  268 T1-post contrast Magnetic Resonance Images (MRIs) in NIfTI format from 165 patients with histologically confirmed metastatic breast cancer to the brain. A brain extraction tool was applied to remove the skull and face. We also performed intensity inhomogeneity correction using N4 bias correction. 

The dataset includes segmented masks for tumor, postoperative tumor bed/resection cavity, and neighboring structures of interest as well as a comprehensive catalog of tumor-derived radiomic features. In addition, we provide patient age, gender, genetic status for progesterone Receptor (PR), estrogen receptor (ER), and Human Epidermal Growth Factor Receptor 2 (HER2). 

This collection provides a platform for diverse applications ranging from the development of machine learning models in computer vision and radiogenomics to educational uses for neurosurgery, radiation oncology, and neuroradiology trainees.
DOI: 10.7937/tcia.2m93-fx66
title: CAnine CuTaneous Cancer Histology Dataset

description: We present a large-scale dataset of 350 histologic samples of seven different canine cutaneous tumors. All samples were obtained through surgical resection due to neoplastic indicators and were selected retrospectively from the biopsy archive of the Institute for Veterinary Pathology of the Freie Universität Berlin according to sufficient tissue preservation and presence of characteristic histologic features for the corresponding tumor subtypes. Samples were stained with a routine Hematoxylin & Eosin dye and digitized with two Leica linear scanning systems at a resolution of 0.25 um/pixel. Together with the 350 whole slide images, we provide a database consisting of 12,424 polygon annotations for six non-neoplastic tissue classes (epidermis, dermis, subcutis, bone, cartilage, and a joint class of inflammation and necrosis) and seven tumor classes (melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma).
DOI: 10.7937/tcia.9cjf-0127
title: Digital pathological slides from Hungarian (Europe) colorectal cancer screening

description: In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. This dataset contains the raw MIRAX (mrxs) formatted data. The samples were selected from the archives of the 2nd Department of Pathology of Semmelweis University, Budapest and were scanned with a 3DHistech Pannoramic 1000 Digital Slide Scanner at the highest available, 40x magnification. This is a single center dataset ensuring consequent and homogeneous data processing and patient handling. The related publication shows, how these data can be utilized for train an artificial neural network in order to detect pathological conditions.
DOI: 10.7937/tcia.bdgf-8v37
title: The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM)
title: UCSF-PDGM

description: The publicly available University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset consists of 501 patients with histopathologically-proven World Health Organization (WHO) grade 2-4 diffuse gliomas who were imaged with a standardized 3T preoperative brain tumor MRI protocol featuring predominantly three-dimensional (3D) imaging and including advanced imaging techniques such as high angular resolution diffusion imaging and arterial spin labeling perfusion imaging. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all patients and O[6]-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for patients with WHO grade 3 and 4 gliomas. Basic clinical details are also provided, including extent of resection and overall survival. The UCSF-PDGM adds to on an existing body of publicly available diffuse glioma MRI datasets that can be used in image-based brain tumor research.
DOI: 10.7937/tcia.hmq8-j677
title: Data from the National Lung Screening Trial (NLST)

description: Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.
Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009.  This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.
Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).
Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).
DOI: 10.7937/vtw4-x588
title: Medical Imaging Data Resource Center - RSNA International COVID Radiology Database Release 1a - Chest CT Covid+ (MIDRC-RICORD-1a)

description: The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models. Purpose The Radiological Society of North America (RSNA) set out to create the RSNA International COVID-19 Open Radiology Database (RICORD), the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 imaging. RICORD data will be published as part of the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health. Materials and Methods MIDRC-RICORD dataset 1a was created through a collaboration between the RSNA and the Society of Thoracic Radiology (STR).