cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/tcia.5fna-0924
title: Multimodality annotated HCC cases with and without advanced imaging segmentation
title: HCC-TACE-Seg

description: Hepatocellular carcinoma (HCC) is the most common primary liver with incidences doubled over the past two decades due to increasing risk factors. Despite surveillance, the majority of HCC cases are diagnosed at advanced stages that can be treated only using (Transarterial chemoembolization) TACE, or systemic therapy. TACE failure can occur to 60% of patients receiving the procedure, with subsequent financial and emotional burden. Radiomics have emerged as a new tool capable of predicting tumor response to TACE from pre-procedural CT study.

This retrospectively acquired data collection includes pre- and post-procedure CT imaging studies of 105 confirmed HCC patients who underwent TACE between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. Baseline imaging includes multiphasic contrast-enhanced CT with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first TACE session. Semiautomatic segmentation of liver, tumor, and blood vessels created using AMIRA was manually clinically curated. These segmentations of each pre-procedural CT study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (NIfTI converted to DICOM-SEG format).
DOI: 10.7937/tcia.985g-ey35
title: A dataset of histopathological whole slide images for classification of Treatment effectiveness to ovarian cancer (Ovarian Bevacizumab Response)

description: The dataset consists of de-identified 288 hematoxylin and eosin (H&E) stained whole slides with clinical information of HGSOC patients from 288 patients. The slides were collected from the tissue bank of the Tri-Service General Hospital and the National Defense Medical Center, Taipei, Taiwan. WSIs were acquired with a digital slide scanner (Leica AT2) with a 20x objective lens. The dimension of the ovarian cancer slides is 54342x41048 in pixels and 27.34 x 20.66mm on average. The Bevacizumab treatment is effective in 162 and invalid in 126 of the dataset.  Ethical approvals have been obtained from the research ethics committee of the Tri-Service General Hospital (TSGHIRB No.1-107-05-171 and No.B202005070), and the data were de-identified and used for a retrospective study without impacting patient care.

The clinicopathologic characteristics of patients were recorded by the data managers of the Gynecologic Oncology Center. Age, pre- and post-treatment serum CA-125 concentrations, histologic subtype, and recurrence, and survival status were recorded. A tumor, which is resistant to bevacizumab therapy, is defined as a measurable regrowth of the tumor or as a serum CA-125 concentration more than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy (i.e., the patient had the detectable disease or elevated CA-125 level following cytoreductive surgery combine with carboplatin/paclitaxel plus bevacizumab). A tumor, which is sensitive to bevacizumab therapy, is defined as no measurable regrowth of the tumor or as a serum CA-125 concentration under than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy.
DOI: 10.7937/tcia.9ytj-5q73
title: Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm (Vestibular-Schwannoma-SEG)

description: This collection contains a labelled dataset of MRI images collected on 242 consecutive patients with vestibular schwannoma (VS) undergoing Gamma Knife stereotactic radiosurgery (GK SRS). The structural images included contrast-enhanced T1-weighted (ceT1) images and high-resolution T2-weighted (hrT2) images. Each imaging dataset is accompanied by the patient’s radiation therapy (RT) dataset including the RTDose, RTStructures and RTPlan.
DOI: 10.7937/tcia.kk02-6d95
title: ACRIN 6698/I-SPY2 Breast DWI

description: The American College of Radiology Imaging Network (ACRIN) trial 6698 was a multi-center study to evaluate the effectiveness of quantitative diffusion weighted imaging (DWI) for assessing breast cancer response to neoadjuvant chemotherapy (NAC). ACRIN 6698 was performed as a sub-study of the ongoing I-SPY 2 TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2), an adaptive, multi-agent phase II trial designed to quickly identify new agents for breast cancer. This collection includes serial MRI studies with DWI, DCE and T2w acquisitions on women undergoing NAC for invasive breast cancer, with single-visit test/retest DWI acquisitions on a subset of the trial subjects.
DOI: 10.7937/tcia.zs4a-jd58
title: The Clinical Proteomic Tumor Analysis Consortium Ovarian Serous Cystadenocarcinoma Collection (CPTAC-OV)

description: This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium CPTAC Ovarian Serous Cystadenocarcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.
Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype", and is being made available on a release schedule that is coordinated with the CPTAC program releases of proteomic and genomic data.  A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page. 
Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.  For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.
DOI: 10.7937/wezw-bh22
title: TOMPEI-CMMD Dataset

description: The original CMMD was published in the Cancer Imaging Archive (TCIA) and consists of 3,728 mammograms from 1,775 patients examined between July 2012 and January 2016. Annotations in this dataset include biopsy-confirmed diagnoses that categorize tumors as benign or malignant; molecular subtypes of tumors are also included for certain patients. The absence of segmentation information in the original dataset limits detailed anatomical analysis because it does not provide data on tumor location, size, shape, and boundary characteristics. The TOMPEI-CMMD dataset, which extends the original CMMD, integrates additional detailed labels and segmentation masks for radiological findings provided by the Department of Diagnostic Imaging at Tohoku University Graduate School of Medicine. Sharing this enhanced dataset is expected to substantially benefit the development of breast cancer computer-aided detection (CAD) systems using deep learning.
DOI: 10.15468/dl.4wx242
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argentina Linnaeus, 1758",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.9648 10.8496,35.9648 10.8496,35.9648 1.8496,44.9648 1.8496,44.9648 10.8496))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012842-250525065834625/datasets/export for details.
DOI: 10.25737/sz96-zg60
title: SAROS - A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data (SAROS)

description: The Sparsely Annotated Region and Organ Segmentation (SAROS) collection contributes a large heterogeneous semantic segmentation annotation dataset for building body composition analysis tools with existing CT imaging cases on TCIA. 

Existing in-house segmentation models were employed to generate annotation candidates on 900 randomly selected cases (450 male, 450 female) from 29 TCIA collections. All generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label. In total, 13 semantic body regions (Subcutaneous Tissue, Muscle, Abdominal Cavity, Thoracic Cavity, Bones, Parotid Glands, Pericardium, Breast Implant, Mediastinum, Brain, Spinal Cord, Thyroid Glands, Submandibular Glands) and 6 body part labels (Torso, Head & Neck, Right Leg, Left Leg, Arm Right, Arm Left) were annotated.
DOI: 10.7937/cv5m-1h59
title: Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma (AHOD0831)

description: This collection contains data from the Children’s Oncology Group (COG) Clinical Trial NCT01026220, “Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma". Principle Investigator: Kara Kelly, MD (pediatric oncologist and the Chair of Roswell Park Oishei Children’s Cancer and Blood Disorders Program, Buffalo, NY.). This phase III trial is studying how well giving combination chemotherapy together with radiation therapy works in treating young patients with newly diagnosed Hodgkin lymphoma. Therapeutic agents used on trial include 1 or more of the following: Biological: bleomycin sulfate; Drug: doxorubicin hydrochloride; Drug: liposomal vincristine sulfate; Drug: vinorelbine tartrate; Drug: cyclophosphamide; Drug: etoposide phosphate; Drug: prednisone; Biological: filgrastimDrug: ifosfamide.
DOI: 10.7937/e4wt-cd02
title: Breast Cancer Screening - Digital Breast Tomosynthesis (BCS-DBT)
title: Breast-Cancer-Screening-DBT
title: BCS-DBT

description: Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.
Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.  The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to, and (3) annotation boxes.
DOI: 10.7937/kh36-ds04
title: Spine metastatic bone cancer: pre and post radiotherapy CT (Spine-Mets-CT-SEG)
title: Spine-Mets-CT-SEG

description: This collection contains a dataset of 55 CT scans collected on patients with a large range of primary cancers and corresponding bone metastatic lesions obtained for patients with metastatic spine disease.  It includes longitudinal CT imaging data set of patients' metastatic spines planned for and treated with radiotherapy and associated radiological delineations of lesion and vertebral features. In addition to the imaging, we provide demographic data, lesion, fracture annotations, and manually and semi-automated vertebral segmentation labels.   This dataset may serve to promote automatic analysis of pathologic vertebrae from computed tomography (CT) scans to improve diagnostic management, and  the advancement of machine-learning methods to analyze pathologic vertebral fracture risk drivers in patients with metastatic spine disease.
DOI: 10.7937/sjez-cj78
title: Combination Chemotherapy With or Without Radiation Therapy in Treating Young Patients With Newly Diagnosed Stage III or Stage IV Wilms Tumor (AREN0533)

description: This collection contains data from the Children’s Oncology Group (COG) Clinical Trial NCT00379340, “Combination Chemotherapy With or Without Radiation Therapy in Treating Young Patients With Newly Diagnosed Stage III or Stage IV Wilms' Tumor, " Study Chair: David Dix, MD. It was sponsored by NCI and performed by the Children's Oncology Group under study number AREN0533. This phase III trial is studying how well combination chemotherapy with or without radiation therapy works in treating young patients with newly diagnosed stage III or stage IV Favorable Histology Wilms' tumor. Select patient-level clinical data from this trial is available via the following link: https://nctn-data-archive.nci.nih.gov/node/737.
DOI: 10.7937/skq4-qx48
title: Annotations for The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC-Tumor-Annotations)

description: This dataset contains image annotations derived from "The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC)”.  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/spgk-0p94
title: Prediction of Sunitinib Efficacy using Computed Tomography in Patients with Pancreatic Neuroendocrine Tumors (CTpred-Sunitinib-panNET)

description: Clinically effective methods to predict the efficacy of sunitinib, for patients with metastatic or locally advanced pancreatic neuroendocrine tumors (panNET) are scarce, making precision treatment difficult. This study aimed to develop and validate a computed tomography (CT)-based method to predict the efficacy of sunitinib in patients with panNET.

Pretreatment CT images of 171 lesions from 38 patients with panNET were included. Clinical information including sex, age at diagnosis, progression-free survival of sunitnib treatment, ki-67 index, tumor grade and previous treatment before sunitinib were also collected.  CT value ratio (CT value of tumor/CT value of abdominal aorta from the same patient) and radiomics features were extracted for model development. Receiver operating curve (ROC) with area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the proposed model.

Tumor shrinkage of >10% at first follow-up after sunitinib treatment was significantly associated with longer progression-free survival (PFS; P<0.001) and was used as the major treatment outcome. The CT value ratio could predict tumor shrinkage with AUC of 0.759 (95% confidence interval [CI], 0.685–0.833). We then developed a radiomics signature, which showed significantly higher AUC in training (0.915; 95% CI, 0.866–0.964) and validation (0.770; 95% CI, 0.584–0.956) sets than CT value ratio. DCA also confirmed the clinical utility of the model. Subgroup analysis showed that this radiomics signature had a high accuracy in predicting tumor shrinkage both for primary and metastatic tumors, and for treatment-naive and pretreated tumors. Survival analysis showed that radiomics signature correlated with PFS (P=0.020). The proposed radiomics-based model accurately predicted tumor shrinkage and PFS in patients with panNET receiving sunitinib and may help select patients suitable for sunitinib treatment.

Pancreatic neuroendocrine tumors are a rare group of tumors. The dataset can be used to validate the findings of our study. More importantly, researchers can use this dataset to study the imaging characteristics of pancreatic neuroendocrine tumors.
DOI: 10.7937/tcia.709x-dn49
title: Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM)

description: This collection comprises multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the Hospital of the University of Pennsylvania, coupled with patient demographics, clinical outcome (e.g., overall survival, genomic information, tumor progression), as well as computer-aided and manually-corrected segmentation labels of multiple histologically distinct tumor sub-regions, computer-aided and manually-corrected segmentations of the whole brain, a rich panel of radiomic features along with their corresponding co-registered mpMRI volumes in NIfTI format. Scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated computational method. These segmentation labels were revised and any label misclassifications were manually corrected/approved by expert board-certified neuroradiologists. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters. The segmentation labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered, and hence allow associations with molecular markers (radiogenomic biomarker research), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.
DOI: 10.7937/tcia.bbag-2923
title: Stony Brook University COVID-19 Positive Cases

description: We have curated clinical and imaging data for covid19-positive patients admitted to the SBU hospital. The dataset consists of de-identified Radiology imaging data along with linked clinical data for each patient. The clinical data consists of diagnoses, procedures, lab tests, covid19 specific data values (e.g., intubation status, symptoms at admission) and a set of derived data elements, which were used in analyses of this data. The clinical data is stored as a set of csv files which comply with OMOP Common Data Model data elements.
DOI: 10.7937/v8h6-bg25
title: MR imaging of pediatric subjects with high-grade gliomas
title: DFCI-BCH-BWH-PEDs-HGG

description: The DFCI-BCH-BWH-PEDs-HGG 2023 dataset is a subset of the BraTS-PEDs 2023/2024 challenge. The DFCI-BCH-BWH-PEDs-HGG 2023 dataset includes a retrospective multi-institutional cohort of conventional/structural magnetic resonance imaging (MRI) sequences, including pre-and post-gadolinium T1-weighted (labeled as T1 and T1 CE), T2-weighted (T2), and T2-weighted fluid-attenuated inversion recovery (T2- FLAIR) images, from Boston's Children Hospital and Dana-Farber Cancer Institute (n=61). Inclusion criteria comprised pediatric subjects with histologically approved high-grade glioma, i.e., high-grade astrocytoma and diffuse midline glioma (DMG), including radiologically or histologically proven diffuse intrinsic pontine glioma (DIPG). Exclusion criteria consisted of (1) Images assessed as low quality or with artifacts that would not allow for reliable tumor segmentation. (2) Infants younger than one month of age.
DOI: 10.7937/3rag-d070
title: The Brain Resection Multimodal Imaging Database (ReMIND)

description: The Brain Resection Multimodal Imaging Database (ReMIND) contains pre- and intra-operative data collected on 114 consecutive patients who were surgically treated with image-guided tumor resection in the AMIGO Suite at the Brigham and Women’s Hospital (Boston, USA), between November 2018 and August 2022. For all patients, preoperative MRI, 3D intraoperative ultrasound series, and intraoperative MRI are available. Additionally, each case contains segmentations, including the preoperative tumor, the pre-resection cerebrum, the previous resection cavity derived from the preoperative MRI (if applicable), and any residual tumor identified on the intraoperative MRI. In total, this collection contains 369 preoperative MRI series, 320 3D intraoperative ultrasound series, 300 intraoperative MRI series, and 356 segmentations. We expect this data to be a resource for computational research in brain shift and image analysis as well as for neurosurgical training in the interpretation of intraoperative ultrasound and intraoperative MRI.
DOI: 10.7937/d8a8-6252
title: Annotations for Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma/PNET (ACNS0332-Tumor-Annotations)

description: This dataset contains image annotations derived from the NCI Clinical Trial "Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma/PNET (ACNS0332)".  This curated dataset provides a comprehensive picture of imaging in pediatric patients with newly diagnosed primitive neuroectodermal tumors throughout their treatment and until any potential relapse. This is the largest known dataset of patients with supratentorial primitive neuroectodermal tumors and pineoblastomas. It is primed for both human and AI software analysis.
DOI: 10.7937/dt39-js04
title: A Randomized Phase III Study Comparing Carboplatin/Paclitaxel or Carboplatin/Paclitaxel/Bevacizumab With or Without Concurrent Cetuximab in Patients With Advanced Non-small Cell Lung Cancer (S0819)

description: This collection contains data from the Southwest Oncology Group (SWOG) Clinical Trial NCT00946712, “Carboplatin and Paclitaxel With or Without Bevacizumab and/or Cetuximab in Treating Patients With Stage IV or Recurrent Non-Small Cell Lung Cancer". Principal Investigator: Roy Herbst.  It was sponsored by NCI and performed by the Southwest Oncology Group under study number S0819. This randomized phase III trial studies carboplatin and paclitaxel to compare how well they work with or without bevacizumab and/or cetuximab in treating patients with stage IV or non-small cell lung cancer that has returned after a period of improvement (recurrent). Select patient-level data from this trial is available.
DOI: 10.7937/m87f-mz83
title: RIDER Pilot

description: This data collection was originally supported under supplemental funding for the Lung Image Database Consortium (LIDC) U01 project and focused on the collection of longitudinal studies using X-ray CT for monitoring the response to therapy. The data came primarily from the M.D. Anderson Cancer Center and several of the LIDC academic sites. Much of this data is not annotated and is not hosted in The Cancer Imaging Archive but was from the NBIA. The portion of these data which were analyzed in QIBA-VolCT-NoChange-1B  are available here.

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. See RIDER Collections for more information.
DOI: 10.7937/q1ee-j082
title: ACRIN-Contralateral-Breast-MR (ACRIN 6667)

description: This dataset relates to NCI Clinical trial, "Magnetic Resonance Imaging in Women Recently Diagnosed With Unilateral Breast Cancer (ACRIN-6667)".
Even after careful clinical and mammographic evaluation, cancer is found in the contralateral breast in up to 10% of women who have received treatment for unilateral breast cancer. ACRIN 6667 was conducted to determine whether magnetic resonance imaging (MRI) could improve on clinical breast examination and mammography in detecting contralateral breast cancer soon after the initial diagnosis of unilateral breast cancer.
A total of 969 women with a recent diagnosis of unilateral breast cancer and no abnormalities on mammographic and clinical examination of the contralateral breast underwent breast MRI.