cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25737/h65s-8f58
title: Annotations for Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer Collection (ARAR0331-Tumor-Annotations)

description: This dataset contains image annotations derived from the NCI Clinical Trial "Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer (ARAR0331)”.  This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.
For each patient, every DICOM Study and DICOM Series was reviewed to identify and annotate the clinically relevant time points and sequences. In a typical patient all available time points were annotated.
DOI: 10.7937/1fpg-vm46
title: Voxel-level segmentation of pathologically-proven Adrenocortical carcinoma with Ki-67 expression (Adrenal-ACC-Ki67-Seg)
title: Adrenal-ACC-Ki67-Seg

description: Adrenocortical carcinoma (ACC) is a rare tumor of the adrenal cortex with a reported annual incidence of one case per million population. ACC is highly aggressive, highly fatal tumor with 5-year overall survival rates ranging from 14% to 44%. Diagnosis of ACC is primarily based on histopathological parameters from resected tumors, which include Ki-67. The Ki-67 index is one of the most important established prognostic markers for local recurrence of ACC. Radiomic feature extraction showed a significant association between radiomic signature and Ki-67 expression status in ACC. 
This retrospectively acquired data includes contrast enhanced CT imaging studies of 53 confirmed ACC patients between 2006 to 2018 with available clinical, pathological data includes Ki-67 index. Semi-automatic segmentation of the adrenal tumor created using AMIRA, then manually refined by experienced radiologist. The segmentations of each contrast-enhanced CT were done for purpose of radiomic features extraction.
DOI: 10.7937/3yat-e768
title: Yale longitudinal dataset of brain metastases on MRI with associated clinical data (Yale-Brain-Mets-Longitudinal)

description: We present a dataset of 11,892 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases. T1-weighted pre-contrast, T1-weighted post-contrast, T2-weighted, and fluid-attenuated inversion recovery MRI sequence images are provided in NIfTI format. Additionally, an Excel spreadsheet with patient demographic information, scanner details, and image acquisition parameters are provided. This dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis.
DOI: 10.7937/6be1-r748
title: A Large Open Access Dataset of Brain Metastasis 3D Segmentations on MRI with Clinical and Imaging Feature Information
title: Pretreat-MetsToBrain-Masks

description: We present a dataset of 200 patients with pathological or clinical diagnosis of metastases to the brain. The dataset contains pretreatment T1, T1 post-contrast, T2, and FLAIR MR images in NifTI format along with a NifTI file of manual segmentation masks for each patient. The segmentation masks include peritumoral edema, contrast-enhancing tumor, and necrotic tumor. There are a total of 975 contrast-enhancing lesions, many of which are sub centimeter. An excel sheet of the following clinical and imaging information is also included: age at diagnosis, sex, ethnicity, smoking history, primary tumor origin, presence of extranodal metastasis, survival time, presence of infratentorial involvement, and number/volume of lesions with contrast-enhancement, necrosis, and peritumoral edema.
DOI: 10.7937/6pj1-m859
title: Vincristine, Dactinomycin, and Doxorubicin With or Without Radiation Therapy or Observation Only in Treating Younger Patients Who Are Undergoing Surgery for Newly Diagnosed Stage I, Stage II, or Stage III Wilms' Tumor (AREN0532)

description: This collection contains data from the Children’s Oncology Group (COG) Clinical Trial NCT00352534 NCT00352534, “Vincristine, Dactinomycin, and Doxorubicin With or Without Radiation Therapy or Observation Only in Treating Younger Patients Who Are Undergoing Surgery for Newly Diagnosed Stage I, Stage II, or Stage III Wilms' Tumor". Principle Investigator: Conrad Fernandez, MD in Halifax, NS.  It was sponsored by NCI and performed by the Children's Oncology Group under study number AREN0532. This phase III trial is studying vincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only to see how well they work in treating patients undergoing surgery for newly diagnosed stage I, stage II, or stage III Wilms' tumor.
DOI: 10.7937/6ppe-4020
title: A morphological dataset of white blood cells from patients with four different genetic AML entities and non-malignant controls (AML-Cytomorphology_MLL_Helmholtz)

description: This dataset comprises four prevalent AML subtypes with defining genetic abnormalities and typical morphological features according to the WHO 2022 classification: (i) APL with PML::RARA fusion, (ii) AML with NPM1 mutation, (iii) AML with CBFB::MYH11 fusion (without NPM1 mutation), and (iv) AML with RUNX1::RUNX1T1 fusion, as well as a control group of healthy stem cell donors. A total of 242 peripheral blood smears from the Munich Leukemia Laboratory (MLL) database from the years 2009 to 2020 were digitized and consist of 99 - 500 automatically selected individual white blood cell images per patient.
DOI: 10.7937/89m3-kq43
title: Annotations for The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC-Tumor-Annotations)

description: This dataset contains image annotations derived from "The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC)”.  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/cf2p-aw56
title: Data in Support of the MIDI-B Challenge (MIDI-B-Synthetic-Validation, MIDI-B-Curated-Validation, MIDI-B-Synthetic-Test, MIDI-B-Curated-Test)

description: These resources comprise a large and diverse collection of multi-site, multi-modality, and multi-cancer clinical DICOM images from 538 subjects infused with synthetic PHI/PII in areas encountered by TCIA curation teams. Also provided is a TCIA-curated version of the synthetic dataset, along with mapping files for mapping identifiers between the two.

This new MIDI data resource includes DICOM datasets used in the Medical Image De-Identification Benchmark (MIDI-B) challenge at MICCAI 2024. They are accompanied by ground truth answer keys and a validation script for evaluating the effectiveness of medical image de-identification workflows. The validation script systematically assesses de-identified data against an answer key outlining appropriate actions and values for proper de-identification of medical images, promoting safer and more consistent medical image sharing.
DOI: 10.7937/tcia.0eck-c338
title: Imaging tissue characterization of a patient derived xenograft model of adenocarcinoma pancreas: PDMR-833975-119-R

description: Pre-clinical animal models of patient derived xenografts may be an important resource by providing collections with a more homogenous tumor genome across the collection with companion extensive tumor genomic characterization available, allowing determination of the variability of imaging characteristics for that pattern in different individuals. This dataset of a patient derived xenograft model adenocarcinoma pancreas PDMR: 833975-119-R can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and regrowth after surgical resection. In this study we performed a detailed imaging characterization of this model, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 cm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.
DOI: 10.7937/tcia.5m9s-6y97
title: Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor (AREN0534)

description: This collection contains data from the Children’s Oncology Group (COG) Clinical Trial NCT00945009 , “Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor, " Study Chair: Peter F. Ehrlich, M.D. M.S.C. It was sponsored by NCI and performed by the Children's Oncology Group under study number AREN0534This phase III trial studies how well combination chemotherapy and surgery work in treating young patients with Wilms tumor. Children with bilateral Wilms tumor account for 5-7% of all patients with Wilms tumor. Certain patients, with syndromes associated with Wilms tumor development, have been identified to be at increased risk for bilateral tumors. Due to an increased risk for renal failure, patients with bilateral disease at presentation are treated with preoperative chemotherapy in order to preserve renal parenchyma. Although this recommendation was made nearly 30 years ago, patients with bilateral tumors have not been formally studied in prior cooperative trials.
DOI: 10.7937/tcia.eqde-4b16
title: The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast
title: CMMD

description: Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespans. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. The recent popularization of artificial intelligence in computer-aided diagnosis creates opportunities for advances in areas such as (1) Computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) Computer-aided diagnosis for characterizing the suspicious region of lesion and/or estimate its probability of onset; and (3) Findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including Luminal A, Luminal B, HER2 positive, and Triple-negative.

However, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types.  To help fill the gap, we built a database conducted on 1,775 patients from China with benign or malignant breast disease who underwent mammography examination between July 2012 and January 2016. The database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. For 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. Image data were acquired on a GE Senographe DS mammography system.
DOI: 10.7937/tcia.x0h0-1706
title: Pediatric Chest/Abdomen/Pelvic CT Exams with Expert Organ Contours (Pediatric-CT-SEG)
title: Pediatric-CT-SEG

description: This dataset was collected by a collaboration of researchers from Children’s Wisconsin, Marquette University, Varian Medical Systems, Medical College of Wisconsin, and Stanford University as part of a project funded by the National Institute of Biomedical Imaging and Bioengineering (U01EB023822) to develop tools for rapid, patient-specific CT organ dose estimation. The collection consists of CT images in DICOM format of 359 pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from three CT scanners. The datasets represent random pediatric cases based upon routine clinical indications. Each dataset contains expert contours of up to twenty-nine structures in DICOM RTSS format.
DOI: 10.7937/0wf5-sj50
title: Sorafenib Tosylate in Treating Patients With Desmoid Tumors or Aggressive Fibromatosis (A091105)

description: This collection contains data from the Alliance for Clinical Trials in Oncology Clinical Trial NCT02066181  “Sorafenib Tosylate in Treating Patients With Desmoid Tumors or Aggressive Fibromatosis". Principal Investigator: Mrinal M Gounder. It was sponsored by NCI and performed by the Alliance for Clinical Trials in Oncology under study number A091105. This randomized phase III trial compares the effects, good and/or bad, of sorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis. Sorafenib tosylate may stop the growth of tumor cells by blocking some of the proteins needed for cell growth. Select patient-level data from this trial is available via the following link:  https://nctn-data-archive.nci.nih.gov/node/1266.
DOI: 10.7937/2bas-hr33
title: Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts (EA1141)

description: This collection contains data from the Eastern Cooperative Oncology Group (ECOG) Clinical Trial NCT02933489, “Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts". Principle Investigator: Christopher Comstock.  It was sponsored by NCI and performed by the Eastern Cooperative Oncology Group under study number EA1141. This randomized phase II trial enrolled 1,516 subjects to study how well abbreviated breast magnetic resonance imaging (MRI) and digital tomosynthesis mammography work in detecting cancer in women with dense breasts. Imaging and clinical data from 500 of those subjects are being made available through this collection.
DOI: 10.7937/ce1t-ea12
title: Multi-scale signaling and tumor evolution in high-grade gliomas (CPTAC-Glioblastoma-CODEX)

description: This dataset combines proteomic, metabolomic, genomic and transcriptomic measurements with imaging from the CPTAC-GBM and UPENN-GBM datasets on TCIA to uncover multi-scale regulatory interactions governing development and evolution of glioblastoma tumors. The dataset contains co-detection by indexing (CODEX) based multiplexed imaging for 18 samples from 12 patients. Hematoxylin and Eosin staining on adjacent sections are also included for pathological annotation. The CODEX images were segmented using the Mesmer pre-trained nuclei + membrane segmentation model in the Deepcell cell segmentation library.

Matching clinical data and proteomic data (raw MS files and processed data files of global proteomics and PTMs) and metabolomic data from the same tumors can be accessed via the Proteomic Data Commons (PDC) (https://pdc.cancer.gov/ ). Genomic, transcriptomic, and multiome snRNA-seq data files can be accessed via Genomic Data Commons (GDC) (https://portal.gdc.cancer.gov/projects/CPTAC-3 ). We recommend using a sample ID manifest to ensure exploration of the complete dataset including metastatic samples. Multiome snATAC-seq data files can be accessed via the Cancer Data Service (CDS) (https://dataservice.datacommons.cancer.gov/#/program/CPTAC:%20Clinical%20Proteomic%20Tumor%20Analysis%20Consortium ) under accession phs001287.v17.p6.
DOI: 10.7937/k9/tcia.2016.yxoglm4y
title: The Cancer Genome Atlas Prostate Adenocarcinoma Collection (TCGA-PRAD)

description: The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.
DOI: 10.7937/k9/tcia.2017.zuzrml5b
title: Data From Credence Cartridge Radiomics Phantom CT Scans
title: CC-Radiomics-Phantom

description: This collection consists of 17 CT scans of the Credence Cartridge Radiomics (CCR) phantom, which was designed for use in studies of texture feature robustness. The scans were acquired at four medical centers using each center’s chest protocol and were taken using GE (7 scans), Philips (5 scans), Siemens (2 scans), and Toshiba (3 scans) scanners. The CCR phantom has 10 cartridges, each with a unique texture. The first four cartridges are 3D printed ABS plastic with 20, 30, 40, and 50 honeycomb fill, and they provide regular, periodic textures. The next three cartridges provide natural textures: sycamore wood, cork, and extra dense cork. A cartridges of shredded rubber particles provides textures similar to those of non-small cell lung cancer. The ninth cartridge is solid, homogenous acrylic and provides a minimal texture control. Finally, the 10th cartridge is 3D printed plaster has the highest electron density (400 – 600 HU) and is intended to more similar to bone.In addition to the DICOM images for the 17 scans, this collection also contains two sets of contours as DICOM RT structure files. The first set provides 8x8x2 cm3 contours for each cartridge in each scan. The second set provides 16 adjacent 2x2x2 cm3 contours for each cartridge in each scan.Researchers studying radiomics will be able to evaluate features for robustness across a variety of scanners. Features can be calculated using the researchers own software or third party software such as IBEX (imaging biomarker explorer).
DOI: 10.7937/k9/tcia.2018.y75f9w1
title: Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images

description: Mappings of tumor-infiltrating lymphocytes (TILs), based on H&E images from 13 TCGA tumor types are available here. These TIL maps are derived through computational staining, using a convolutional neural network trained to classify patches of images. In addition to the TIL Maps, the analysis codes and the software used to extract TILs are also available.
DOI: 10.7937/q37d-vh79
title: Imaging characterization of a metastatic patient derived model of adenocarcinoma pancreas: (PDMR-521955-158-R4)

description: Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 521955-158-R4. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 cm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.
DOI: 10.7937/qstf-st65
title: Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases (Prostate-Anatomical-Edge-Cases)
title: Prostate-Anatomical-Edge-Cases

description: In this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation CT and/or MRI imaging. These variations included hip arthroplasty, prostate median lobe hypertrophy, so-called “droopy” seminal vesicles, presence of a urinary catheter, and others. A separate cohort of 19 “normal” cases were randomly selected for inclusion. Prostate, rectum, bladder, and bilateral femoral heads were manually segmented on all CT simulation images (where present) and were ultimately used clinically for radiation treatment planning.
DOI: 10.7937/tcia.2019.9bt23r95
title: The Clinical Proteomic Tumor Analysis Consortium Sarcomas Collection (CPTAC-SAR)
title: CPTAC-SAR

description: This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Sarcomas (CPTAC-SAR) 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 Phase 3 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.
DOI: 10.7937/tcia.2019.zjjwb9ip
title: Data from Anti-PD-1 Immunotherapy Lung

description: This collection comprises 46 lung cases treated with anti-PD1 immunotherapy in 2016, each with 2 or more imaging timepoints.
DOI: 10.7937/tcia.582b-xz89
title: Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma/PNET (ACNS0332)

description: This collection contains data from the Children’s Oncology Group (COG) Clinical Trial NCT00392327, Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma/PNET, " Study Chair: James M. Olson, M.D., Ph.D. It was sponsored by NCI and performed by the Children's Oncology Group under study number ACSN0332. This randomized phase III trial studies different chemotherapy and radiation therapy regimens to compare how well they work in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma.
DOI: 10.7937/tcia.d8z0-9t85
title: I-SPY 2 Breast Dynamic Contrast Enhanced MRI (I-SPY2 TRIAL)

description: I-SPY 2 (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular analysis 2) is an ongoing, multi-center trial designed to quickly evaluate the efficacy of new agents for breast cancer in neoadjuvant chemotherapy (NAC) setting. Women aged ≥18 years diagnosed with locally advanced breast cancer (tumor size ≥2.5 cm) without distant metastasis are eligible to enroll in the trial. Patients with breast cancer at high-risk for recurrence are adaptively-randomized to either control arm (standard NAC) or one of several concurrent experimental drug arms. I-SPY 2 utilizes change in tumor volume by DCE-MRI at serial time-points during NAC to adjust the randomization schema as the trial proceeds, preferentially assigning patients to receive agents showing an increasing likelihood of efficacy against their breast cancer subtype.