cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/hrzh-2n82
title: Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Annotated Multi-Center Routine Clinical Dataset (Vestibular-Schwannoma-MC-RC)
title: Vestibular-Schwannoma-MC-RC

description: This multi-center routine clinical (MC-RC) dataset consists of 160 patients with a single sporadic Vestibular Schwannoma (VS) who were referred from 10 medical sites and consecutively seen at a single center. These routine clinical datasets are more diverse in terms of the tumor manifestation as well as the acquisition parameters. Using this dataset, researchers can develop and validate methods for automatic surveillance of Vestibular Schwannoma, which work robustly on images acquired at different hospitals. Patients had multiple time points resulting in a total of 428 timepoints and 487 3D-images. Manual ground truth segmentations were obtained in an iterative process in which segmentations were: 1) produced or amended by a specialized company; and 2) reviewed by one of three trained radiologists; and 3) validated by an expert team. Compared to the existing Vestibular-Schwannoma-SEG dataset on TCIA that was obtained from a single scanner, this dataset was acquired from multiple scanners manufactured by different vendors. This dataset also provides a refined segmentation of intrameatal and extrameatal components of the VS.
DOI: 10.7937/k9/tcia.2014..ua0jgpdg
title: Data from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (Radiomics-Tumor-Phenotypes)
title: Radiomics-Tumor-Phenotypes

description: In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
DOI: 10.7937/k9/tcia.2014.k6m61gdw
title: Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group

description: The Cancer Imaging Program (CIP) is working directly with primary investigators from institutes participating in TCGA to obtain and load images relating to the genomic, clinical, and pathological data being stored within the TCGA Data Portal. Currently this large CT and MR multi-sequence image collection of kidney renal clear cell carcinoma (KIRC) patients can be matched by each unique case identifier with the extensive gene and expression data of the same case from The Cancer Genome Atlas Data Portal to research the link between clinical phenome and tissue genome.
DOI: 10.7937/k9/tcia.2014.v7cvh1jo
title: Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset (Pulmonary-Nodules-Segmentation)
title: Pulmonary-Nodules-Segmentation

description: We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully--automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed.

The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system.
DOI: 10.7937/k9/tcia.2015.pf0m9rei
title: Data From NSCLC-Radiomics

description: This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications. In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. The dataset described here (Lung1) was used to build a prognostic radiomic signature. The Lung3 dataset used to investigate the association of radiomic imaging features with gene-expression profiles consisting of 89 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics-Genomics. For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School (hugo_aerts@dfci.harvard.edu).   More Description
DOI: 10.7937/k9/tcia.2020.jhz2-t694
title: MRQy quality measures for TCIA MRI datasets

description: Magnetic Resonance Imaging (MRI) quality assessment measures were generated for multiple TCIA datasets including:
(1) T1-weighted post-contrast axial MRI sequences from 133 TCGA-GBM subjects, 
(2) T1-weighted post-contrast axial MRI sequences from 46 CPTAC-GBM subjects,  
(3) Both T1- and T2-weighted axial MRI sequences from 54 TCGA-CESC subjects.
All MRI scans for each cohort were downloaded as DICOM files from TCIA and then processed via MRQy to compute quality measures for (a) interrogating the presence of site- or equipment-specific variations within a cohort, and (b) quantifying the impact of MRI artifacts to determine what pre-analytical corrections are needed. The MRQy output can be easily interrogated via the associated HTML5 based front-end, allowing for real-time filtering and visualization. MRQy is available for download at: http://github.com/ccipd/MRQy.
DOI: 10.7937/p5k5-tg43
title: Abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer
title: Stage II-Colorectal-CT

description: This dataset includes abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer (CRC). The inclusion criteria were as follows: (i) patients with radical surgery for CRC (complete removal of the original tumor and regional lymphadenectomy); (ii) patients with stage II CRC confirmed by histology and pathology; (iii) abdominal or pelvic enhanced CT examination was performed within 10 days before surgery, with complete CT enhanced scan images. The exclusion criteria were as follows: (i) preoperative treatment (neoadjuvant chemotherapy or radiotherapy); (ii) patients with another tumor diseases at the same time; (iii) patients who died within 1 month after surgery due to acute surgical complications. 
All CT images were enhanced abdominal or pelvic CT scan, which were scanned by using a Sensation 64 (Siemens Healthcare, Erlangen, Germany) CT scanner or Brilliance (Philips Healthcare, Best, The Netherlands) CT scanner. The specific scanning parameters were as follows: 120kV tube voltage, 200mA tube current, 5 mm slice thickness, 0.5s/week rack speed, 1.4 or 0.9 pitch, 4.11 cm field of view and a 512×512 matrix. 80-100ml iodine contrast agent ioprolamine was injected through the cubital vein at a speed of 2-3mL/s. CT enhanced images were collected after 65-75s contrast agent injection.

The digital CT images were retrieved from the picture archiving and communication system (PACS), and exported with digital imaging and communication in medicine (DICOM) format. Patient identifying information has been removed. The publishing of this dataset follows the ethical and privacy rules of China. Other researchers can further analyze these CT images of stage II colorectal cancer or use the data as validation sets for their studies.
DOI: 10.7937/tcia.2019.4a4dkp9u
title: Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types

description: We developed an image analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types. Our pipeline also implements a whole slide image nuclear segmentation quality control process. We have generated nucleus segmentation results in more than 5,000 Whole Slide Tissue images (WSIs) from 10 cancer types in The Cancer Genome Atlas (TCGA). The datasets we publish in this work consist of roughly 5 billion quality controlled nuclei from more than 5,000 TCGA WSIs from 10 different TCGA cancer types and 1356 manually segmented TCGA image patches from the same 10 cancer types plus additional 4 cancer types.
DOI: 10.7937/a1v1-rc66
title: Task-Based Anthropomorphic CT Phantom for Radiomics Stability and Discriminatory Power Analyses (CT-Phantom4Radiomics)
title: CT-Phantom4Radiomics

description: The aims of this dataset are to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data.
A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom [2]. 240 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing are available.
The conclusions of our preliminary study [1] showed that the differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.
[1] Jimenez del-Toro, O. et al. (2021) DOI: 10.1097/rli.0000000000000795
[2] Bach, M. et al (2023) DOI: 10.1002/mp.16373
DOI: 10.7937/k9/tcia.2014.a2n1ixox
title: Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge (QIN Breast DCE-MRI)
title: QIN Breast DCE-MRI

description: This collection of breast dynamic contrast-enhanced (DCE) MRI data contains images from a longitudinal study to assess breast cancer response to neoadjuvant chemotherapy. Images were acquired at four time points: prior to the start of treatment (Visit 1, V1), after the first cycle of treatment (Visit 2, V2), at midpoint of treatment course (Visit 3, V3), and after completion of treatment (prior to surgery) (Visit 4, V4). The value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of breast cancer response to treatment. Data is provided by Oregon Health & Science University, PI Dr. Wei Huang. The MRI data consist of DCE-MRI images, which were acquired using a Siemens 3T TIM Trio system with the body coil and a four-channel bilateral phased-array breast coil as the transmitter and receiver, respectively.  Following pilot scans and pre-contrast T2-weighted MRI with fat-saturation and T1-weighted MRI without fat-saturation, axial bilateral DCE-MRI images with fat-saturation and full breast coverage were acquired with a 3D gradient echo-based TWIST (Time-resolved angiography WIth Stochastic Trajectories) sequence, which employs the strategy of k-space undersampling during acquisition and data sharing during reconstruction.  DCE-MRI acquisition parameters included 10o flip angle, 2.9/6.2 ms TE/TR, a parallel imaging acceleration factor of two, 30-34 cm FOV, 320x320 in-plane matrix size, and 1.4 mm slice thickness.  The total acquisition time was ~10 minutes for 32-34 image volume sets of 112-120 slices each with 18-20 s temporal resolution.  The contrast agent Gd(HP-DO3A) [ProHance] IV injection (0.1 mmol/kg at 2 mL/s) by a programmable power injector was timed to commence after acquisition of two baseline image volumes, followed by a 20-mL saline flush.           A total of 20 data sets from this collection have been used for a multi-QIN center challenge, in which each participating site performed pharmacokinetic analysis of the breast DCE-MRI data using software tools/algorithms available to them. The shared data sets are from the V1 and V2 studies of 10 patients (BreastChemo 1, 5, 6, 8, 10, 12, 13, 14, 15, and 16) – 3 pathologic complete responders (pCRs) and 7 non-pCRs.  The goal of the challenge was to evaluate variations in DCE-MRI assessment of breast cancer response to neoadjuvant chemotherapy caused by differences in software tools/algorithms only.
DOI: 10.7937/k9/tcia.2014.iirmbunx
title: Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging (LuminalB-Breast-MR-Enhancement)
title: LuminalB-Breast-MR-Enhancement

description: To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. Materials and Methods Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial). Results There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype. Conclusion The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma. (c) RSNA, 2014 Online supplemental material is available for this article.
DOI: 10.7937/k9/tcia.2015.7go2gsks
title: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

description: In this study, a cohort of 51 patients with histologically proven soft-tissue sarcomas (STSs) of the extremities was retrospectively evaluated. During the follow-up period, 19 patients developed lung metastases. Patients with metastatic and/or recurrent STSs at presentation were excluded from the study. All patients had pre-treatment FDG-PET/CT and MRI scans.
DOI: 10.7937/k9/tcia.2015.a6v7jiwx
title: Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma
title: LungCT-Diagnosis

description: This is a small set of 61 diagnostic contrast enhanced CT scans with known tumors.  The images were retrospectively acquired, to ensure sufficient patient follow-up. Slice thickness is variable : between 3 and 6 mm. All images were done at diagnosis and prior to surgery.
DOI: 10.7937/k9/tcia.2016.sq4m8yp4
title: The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma Collection (TCGA-CESC)

description: The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) 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/tcia.2018.h7umfurq
title: Standardized representation of the TCIA LIDC-IDRI annotations using DICOM
title: DICOM-LIDC-IDRI-Nodules

description: This dataset contains standardized DICOM representation of the annotations and characterizations collected by the LIDC/IDRI initiative, originally stored in XML and available in the TCIA LIDC-IDRI collection (https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI). Only the nodules that were deemed to be greater or equal to 3 mm in the largest planar dimensions have been annotated and characterized by the expert radiologists performing the annotations. Only those nodules are included in the present dataset.
DOI: 10.7937/tcia.2019.cwvlpd26
title: Data from NSCLC-Radiomics-Interobserver1

description: This collection contains clinical data and computed tomography (CT) from 22 non-small cell lung cancer (NSCLC) radiotherapy patients. For 21 of these patients with pre-treatment CT scans, repeated blinded manual delineations by five different radiation oncologists of the 3D volume of the gross tumor volume on CT and clinical outcome data are available. The above was repeated with the same set of five radiation oncologists, using an in-house autosegmentation tool for initial delineation followed by manual adjustment of the gross tumor volume outline. For one patient, clinical data and CT was available but the tumor delineations were not extracted. This patient was included in this collection for the sake of completeness.
DOI: 10.7937/tcia.2019.dc64i46r
title: ALL Challenge dataset of ISBI 2019 (C-NMC 2019)
title: C-NMC 2019

description: Acute lymphoblastic leukemia (ALL) constitutes approximately 25 of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar. In this paper, we propose a deep learning framework for classifying immature leukemic blasts and normal cells. The proposed model combines the Discrete Cosine Transform (DCT) domain features extracted via CNN with the Optical Density (OD) space features to build a robust classifier. Elaborate experiments have been conducted to validate the proposed LeukoNet classifier.
DOI: 10.7937/tcia.2019.ix49e8nx
title: C4KC KiTS Challenge Kidney Tumor Segmentation Dataset

description: The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the association between kidney tumor morphometry and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Reliable semantic segmentation of kidneys and kidney tumors is a powerful tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge. With the presence of comorbidities and clinical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging in conjunction with semantic segmentation masks.
DOI: 10.7937/tcia.2020.1c3h-vp70
title: QIBA VolCT Group 1B Round 2 No Change Size Measurements
title: QIBA-VolCT-1B

description: PURPOSE: To determine the variability of lesion size measurements in computed tomography data sets of patients imaged under a “no change” (“coffee break”) condition and to determine the impact of two reading paradigms on measurement variability. METHOD AND MATERIALS: Using data sets from 32 RIDER Lung CT patients scanned twice within 15 minutes (“no change”), measurements were performed by five radiologists in two phases: (1) independent reading of each computed tomography dataset (timepoint): (2) a locked, sequential reading of datasets. Readers performed measurements using several sizing methods, including one-dimensional (1D) longest in-slice dimension and 3D semi-automated segmented volume. Change in size was estimated by comparing measurements performed on both timepoints for the same lesion, for each reader and each measurement method. For each reading paradigm, results were pooled across lesions, across readers, and across both readers and lesions, for each measurement method. RESULTS: The mean percent difference (± SD) when pooled across both readers and lesions for 1D and 3D measurements extracted from contours was 2.8 ± 22.2% and 23.4 ± 105.0%, respectively, for the independent reads. For the locked, sequential reads, the mean percent differences (± SD) reduced to 2.52 ± 14.2% and 7.4 ± 44.2% for the 1D and 3D measurements, respectively. CONCLUSION: Even under a “no change” condition between scans, there is variation in lesion size measurements due to repeat scans and variations in reader, lesion, and measurement method. This variation is reduced when using a locked, sequential reading paradigm compared to an independent reading paradigm.