This statistic depicts the annual compensation among radiologists in the U.S. according to different sources (organizations), as of 2018. According to Integrated Healthcare Strategies, annual salaries for radiologists averaged some 446 thousand U.S. dollars.

Comprehensive dataset of 2,977 Radiologists in Texas, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 396 Radiologists in Utah, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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Abstract

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.

Introduction

The dataset consists of institutional review board-approved retrospective analysis of pathologically proven glioma patients at University Hospital of The University of Missouri - Anatomic Pathology CoPathPlus database was used to collect glioma cases over the last 10 years.

Sharing segmented postoperative glioma data with clinical information significantly accelerates research and improves clinical practice by providing a comprehensive, readily available dataset. This eliminates the time-consuming burden of manual segmentation, enhances the accuracy and consistency of tumor delineation, and allows researchers to focus on analysis and interpretation, ultimately driving the development of more accurate segmentation algorithms, predictive models for personalized treatment strategies, and improved patient outcome predictions. Standardized longitudinal follow-up and benchmarking capabilities further facilitate multi-center studies and objective evaluation of treatment efficacy, leading to advancements in glioma biology and personalized patient care.

Methods

The following subsections provide information about how the data were selected, acquired, and prepared for publication.

Subject Inclusion and Exclusion Criteria

The selection criteria for the CoPath Natural Language II Search included accession dates ranging from 01/01/2021 to 02/20/2024. To ensure all relevant diagnoses for this study were included; three separate keyword searches were performed using "glioma", "astrocytoma", and "glioblastoma". The search only included keyword results that were present in the Final Diagnoses. "Glioma" returned 85 cases; "Astrocytoma" returned 67 cases; and "Glioblastoma" returned 215 cases. Following the exclusion of duplicate cases, those missing any of the four requisite MR imaging sequences, and cases that failed processing through our pipeline, our final cohort comprised 203 patients.

Data Acquisition

Radiology: MRI studies on our McKesson Radiology 12.2 Picture archiving and communication system (PACS) (Change Healthcare Radiology Solutions, Nashville, Tennessee, U.S) were exported. The image exportation process involved multiple personnels of varying ranks, including medical graduates, radiology residents, neuroradiology fellows, and neuroradiologists. Our team exported the four basic conventional MR sequences including T1, T1 with IV gadolinium-based contrast agent administration, T2, and Fluid Attenuated Inversion Recovery (FLAIR) into a HIPPA compliant MU secured research server.

For each patient, the images were thoroughly checked for including up to six post-treatment images as available. The post-treatment images were captured on different dates, though not all patients had the maximum number of follow-up images; some had as few as one post-treatment follow-up MRI. For patients with more frequent follow-up MRIs, the immediate post-operative scan, at least one time point of progression and another follow-up study. The MR images were comprehensively reviewed to exclude significantly motion degraded or suboptimal studies.

The majority of the studies were conducted using Siemens MRI machines 97.47%, n=579 with a smaller proportion performed on MRI machines from other vendors: GE (2.02%, n=12) and Philips (0.51%, n=3). Table 1 shows the distribution of studies across different Siemens MR machines. Regarding the magnetic field strength, 1.5T MRIs accounted for 48.14% (n=1,126), 3T MRIs accounted for 45.08% (n=318), and 3T MRIs accounted for 45.08% (n=261). Table 2 summarizes the MRI parameters of each MR sequence.

Our team made efforts to obtain 3D sequences whenever available. Scans were performed using 3D acquisition methods in 40.28% of cases (n=975) and 2D acquisition methods in 59.82% of cases (n=1,419). In cases where 3D images were not available, 2D images were utilized instead. Table 3 summarizes the counts and percentage of studies performed with 2D vs 3D acquisition across different MR sequences.

Clinical: Basic demographic data, clinical data points, and tumor pathology were obtained through review of the electronic medical record (EMR). Clinical data points included the date of diagnosis, date of first surgery or treatment, date and characterization of first and/or subsequent disease progression and/or recurrence, and date of any follow-up resections. Survival information included the date of death and, if that was unknown, the date of last known contact while alive. Disease progression and/or recurrence was characterized as imaging only, clinical only, or both based on information obtained through review of each patient’s clinical notes, brain imaging, and clinical impression as documented by the primary care team. Brief summaries of the reasoning behind each characterization were also included. Patients with no further clinical contact beyond their primary treatment were documented as “lost to follow-up.” Pathological information was obtained through review of the initial pathology note and any subsequent addenda for each tumor sample and included final tumor diagnosis, grade, and any identified genetic mutations. This information was then compiled into a spreadsheet for analysis.

Data Analysis

The image data underwent preprocessing using the Federated Tumor Segmentation (FeTS) tool. The pipeline began with converting DICOM files to the Neuroimaging Informatics Technology Initiative (NIfTI) format, ensuring the removal of any remaining PHI not eliminated by the anonymization/de-identification tool. The converted NIfTI images were then resampled to an isotropic 1mm³ resolution and co-registered to the standard anatomical human brain atlas, SRI24. A deep learning brain extraction method was applied to strip the skull and extracranial tissues, thereby mitigating any potential facial reconstruction or recognition risks.

The preprocessed images were segmented using a deep network based on nnU-Net, resulting in four distinct labels that correspond to different components of each tumor:

• Label 1: Non-enhancing Tumor Core (NETC). This label identifies non-enhancing components within the tumor, such as cystic, necrotic, or hemorrhagic portions.
• Label 2: Surrounding Non-enhancing FLAIR Hyperintensity (SNFH). This label represents both non-enhancing infiltrative tumor components and peritumoral vasogenic edema.
• Label 3: Enhancing Tissue (ET). This label highlights the viable nodular-enhancing components of the tumor.
• Label 4: Resection Cavity (RC). This label covers post-surgical changes, including recent changes like blood products and air foci, as well as chronic changes with materials isointense to CSF signal.

A spreadsheet is also provided that includes tumor volumes and signal intensity of different tumor components across various MR sequences.

Usage Notes

Each scan was manually exported using the built-in McKesson DICOM export tool into separate folders labeled as post-treatment 1, post-treatment 2, etc. In a subsequent step, a subset of the data was selected to contribute for the development of FeTS 2 toolbox. Consequently, the naming convention was updated to replace "post-treatment" with "timepoint" (e.g., post-treatment 1 became timepoint 1) to adhere to the instructions of the FeTS development team. Each sequence was saved in its own folder within these categories to a HIPPA compliant and secured server within the University of Missouri network. Exportation was conducted in DICOM format, maintaining the original image compression settings to preserve quality. To ensure patient privacy and HIPPA compliance, all images were anonymized and all protected health information (PHI) e.g. patient name, MRN, accession number, etc. were deleted from the metadata DICOM headers.

The folders are labeled in the following structure:

• Main folder: PatientID_XXXX
• Subfolders: Timepoint_X, Timepoint_X
• Each time point folder has the NIfTI images associated with the respective timepoints.

Snapshot img

Interventional Radiology Products Market Size 2024-2028

The interventional radiology products market size is forecast to increase by USD 5.45 billion at a CAGR of 7.1% between 2023 and 2028.

Interventional radiology (IR) is a subspecialty of radiology that employs minimally invasive image-guided procedures. The IR market is experiencing significant growth due to several key trends. One major factor driving market expansion is the increasing demand for minimally invasive surgical procedures, which offer numerous benefits over traditional open surgeries, such as reduced recovery time, lower risk of complications, and decreased healthcare costs. This market encompasses a range of innovative medical devices, including imaging technologies such as CT scans, MRIs, and ultrasounds, as well as minimally invasive procedures like angioplasty balloons, stents, thrombectomy systems, embolization devices, biopsy needles, and catheters. Another trend shaping the IR market is the continuous launch of innovative new products, which provide enhanced functionality and improved patient outcomes. However, the IR market also faces challenges, including the shortage of radiologists, which can hinder market growth and increase the workload on existing radiologists. Despite these challenges, the future of the IR market looks promising, with continued advancements in technology and a growing focus on minimally invasive procedures.

What will be the Size of the Interventional Radiology Products Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing prevalence of chronic diseases, particularly cancer and cardiovascular problems. These advanced therapeutics are increasingly being utilized to treat complex conditions, reducing healthcare costs and improving patient outcomes. Moreover, the integration of digital health technologies, such as smart inhalers and remote patient monitoring, is revolutionizing interventional radiology.
Further, preventive care and chronic condition management are key areas of focus, with the adoption of personalized medicine and molecular diagnostics driving the market forward. In the realm of cardiovascular diseases, interventional radiology plays a crucial role In the administration of drugs and the placement of IVC filters and inferior vena cava filters. The market is further fuelled by the ongoing development of cardiology-focused devices and the integration of genomics and biotechnology.

How is this Interventional Radiology Products Industry segmented and which is the largest segment?

The interventional radiology products industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Stents
  Catheters
  Embolization devices
  Inferior vena cava filters
  Others


Application

  Cardiology
  Urology and nephrology
  Oncology
  Gastroenterology
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  Asia

    China
    Japan


  Rest of World (ROW)

By Type Insights

The stents segment is estimated to witness significant growth during the forecast period.

Interventional radiology products play a significant role in addressing chronic diseases, including cardiovascular problems and cancer, through minimally invasive procedures. These products encompass various advanced therapeutics, such as CT scans, MRIs, ultrasounds, and imaging technologies, which facilitate accurate diagnosis and treatment planning. Stents, a crucial interventional radiology product, are used to treat vascular conditions, primarily cardiovascular diseases. Stents, available in metal and fabric materials, are employed post-angioplasty to maintain arterial patency in coronary artery disease, ensuring enhanced blood flow to the heart.

Additionally, they are effective in treating peripheral artery disease, restoring blood flow to the limbs and reducing pain and mobility issues. Interventional radiology products extend to other medical specialties, including cardiology, oncology, gastroenterology, neurology, orthopedics, and urology. These products are utilized in hospitals, clinics, and home care settings, offering cost-effective and efficient treatment options. Drug administration systems, thrombectomy systems, embolization devices, and biopsy needles are essential interventional radiology products in various treatment procedures. Healthcare costs, insurance systems, and digital health technologies significantly impact the adoption of interventional radiology products. Ensuring patient data safety and cybersecurity threats are essential considerations for healthcare professionals in implementing these technologies. The integration of genomics, molecular diagnostics, and p

This dataset provides information on 173 in West Virginia, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

CheXpert

CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets. https://stanfordmlgroup.github.io/competitions/chexpert/

  Warning on AP/PA label

I could not find in the paper a mapping from the 0/1 label to AP/PA, so I assumed 0=AP and 1=PA. Looking at a few images this seems to be correct, but I'm not a radiologist.… See the full description on the dataset page: https://huggingface.co/datasets/danjacobellis/chexpert.

Aseptic Loose Hip Implant X-Ray Database

A team of researchers and doctors from Qatar University, Doha, Qatar, have collected and indexed this dataset of X-ray images for hip implants from different publicly available online medical sources such as medicine journals (articles) and radiology websites. All images should at least include a stem and a cup of the hip implant, and the images have to be X-ray images. These images were carefully checked to avoid duplications and the clinical experts in the team evaluated each of the images to make sure that the collected X-ray images are for hip implants. For patients who had undergone total hip arthroplasty surgery, an anteroposterior (AP) view of the X-ray images for the patients with fixed (control group) and loosened hip implants were collected, while the X-ray images having a wire or plate attached with the implant were excluded. Authors managed to collect 200 hip implant X-ray images from published articles [1-4], online resources [5-8], and Radiopaedia [9] and are also available the complete dataset [10]. Since these images were collected from different resources, different image resolutions, sizes, and types of implants, loosening conditions are available in this sub-set. These collected images were manually annotated by the team and finally validated by an orthopedic surgeon, who has more than 10 years of experience in THR surgery.

Credit should go to the following:

-1Tawsifur Rahman, 1Amith Khandakar, 1Khandaker Reajul Islam, 2Md Mohiuddin Soliman, 2Mohammad Tariqul Islam, 3,4Ahmed Elsayed, 1Yazan Qiblawey, 1Sakib Mahmud, 5Ashiqur Rahman, 6Farayi Musharavati*, 7Erfan Zalnezhad, 1Muhammad E. H. Chowdhury*

1Department of Electrical Engineering, Qatar University, Doha 2713, Qatar 2Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia 3Orthopaedic Department, Hamad Medical Corporation (HMC), Doha, Qatar 4Clinical Orthopaedic Surgery, Weill Cornell Medical College, Doha, Qatar 5Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Japan 6Department of Industrial and Mechanical Engineering, Qatar University, Doha 2713, Qatar 7Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA

Objective

Researchers can use this database to produce useful and impactful scholarly work on COVID-19, which can help in tackling this issue.

Citation

Please cite this database if you are using it for any scientific purpose.

Reference

[1] T. Ilchmann et al., "One-stage revision of infected hip arthroplasty: outcome of 39 consecutive hips," International orthopaedics, vol. 40, no. 5, pp. 913-918, 2016. [2] Y.-S. Kim, C.-M. Cho, K.-T. Hwang, Y.-H. Kim, and I.-Y. Choi, "Revision total hip arthroplasty using a Wagner revision stem," The Journal of the Korean Hip Society, vol. 22, no. 2, pp. 137-142, 2010. [3] A. Balanika, S. Theocharakis, S. Vrizidou, C. Drosos, and C. Baltas, "Radiographic interpretation of hip replacement hardware. A pictorial essay," 2014: European Congress of Radiology-ESSR 2014. [4] O. Awan, L. Chen, and C. S. Resnik, "Imaging evaluation of complications of hip arthroplasty: review of current concepts and imaging findings," Canadian Association of Radiologists Journal, vol. 64, no. 4, pp. 306-313, 2013. 5. Radiologic Evaluation of Hip Arthroplasty [Online]. Available: https://plasticsurgerykey.com/radiologic-evaluation-of-hip-arthroplasty/ 6. Revision Surgery for Hip replacements [Online]. Available: http://www.cardiffhipandknee.com/hip/hip-revisions 7. Looseing [Online]. Available: http://www.amilcaregentili.com/thr/loosenin.htm 8. THA Aseptic Loosening [Online]. Available: https://www.orthobullets.com/recon/12304/tha-aseptic-loosening 9. Aseptic Looseing of Hip Joint Replacements [Online]. Available: https://radiopaedia.org/articles/aseptic-loosening-of-hip-joint-replacements?lang=us 10. aspetic-loose-hip-implant-xray-database [Online]. Available: https://www.kaggle.com/tawsifurrahman/aseptic-loose-hip-implant-xray-database

Snapshot img

Teleradiology Market Size 2024-2028

The teleradiology market size is forecast to increase by USD 3.83 billion at a CAGR of 17.55% between 2023 and 2028.

The increasing prevalence of diseases, coupled with the growing geriatric population, is the key driver of the teleradiology market. Aster Medical Imaging LLC is a key company in this space, offering teleradiology services such as urgent after-hours consultations with radiologists, urgent daytime support for emergency cases, routine overflow, and backlog management. The company's services help healthcare providers efficiently manage radiology needs, improving patient care and addressing the rising demand for timely diagnostic solutions.
Technological advancements and upgrades in teleradiology modalities enable radiologists to provide accurate diagnoses from a distance, improving accessibility and efficiency in healthcare delivery and mhealth solutions. Furthermore, government initiatives encouraging the adoption of healthcare IT have created a favorable regulatory environment for teleradiology services. These factors collectively contribute to the expansion of the teleradiology market, offering opportunities for growth and innovation in the healthcare industry.

What will be the Size of the Teleradiology Market During the Forecast Period?

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The market is experiencing significant growth due to the integration of Artificial intelligence (AI) in healthcare, particularly In the interpretation of medical imaging for various conditions. Preliminary and final reports are shared between healthcare professionals and Neuroradiologists through XERO Exchange Network and other teleradiology platforms. The geriatric population, with target diseases such as cardiovascular conditions, cancer, and osteoarthritis (OA), significantly benefits from timely interventions based on accurate and efficient diagnostic data.
However, higher-resolution imaging and 3D imaging are essential for diagnosis and treatment planning In the elderly population. AI enhances image interpretation, ensuring diagnostic accuracy and improving the overall quality of healthcare. The market is driven by the increasing demand for cutting-edge imaging solutions to manage musculoskeletal ailments like OA. The Osteoarthritis Action Alliance focuses on the importance of early diagnosis and intervention for improved patient outcomes.

How is this Teleradiology Industry segmented and which is the largest segment?

The teleradiology industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Modality

  CT
  X-ray
  Ultrasound
  MRI
  Nuclear imaging


Component

  Hardware
  Software
  Telecom and networking


End Use

  Hospital
  Radiology Clinics
  Ambulatory Imaging Center


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By Modality Insights

The CT segment is estimated to witness significant growth during the forecast period. In the dynamic and evolving teleradiology industry, healthcare professionals leverage advanced imaging systems to deliver accurate and timely diagnostic reports remotely. Artificial Intelligence (AI) is increasingly being integrated into medical imaging to enhance preliminary reports, ensuring that final reports are more precise and efficient. Hospitals and neuroradiologists rely on various modalities of CT scanners for diagnosing target diseases such as cardiovascular conditions, cancer, and osteoarthritis (OA) In the elderly population. Agfa Healthcare's XERO Exchange Network plays a crucial role in facilitating the secure exchange of diagnostic data between healthcare providers, enabling timely interventions and improving the quality of healthcare. Multi-detector CT (MDCT) scanners, with their ability to acquire images more quickly and with greater resolution, are a significant modality In the teleradiology industry. These scanners are essential for interpreting complex medical images, particularly In the context of cardiovascular conditions and cancer. The integration of AI in image interpretation further enhances the diagnostic capabilities of these systems, ensuring accuracy and efficiency In the diagnostic processes.

Moreover, the teleradiology industry is witnessing a shift towards streamlining workflows and cost-cutting measures during economic recessions. Broadband networks and data security are critical considerations in ensuring connectivity and data privacy. Security protocols are essential to mitigate potential connectivity issues and maintain the confidentiality of diagnostic data. The integration of AI in teleradiology platforms and services is expected to revolutionize the industry, providing 3D imaging c

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. 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.

Annotation Protocol

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following time points were annotated:

1. Pre-surgical study
2. Post-surgical study [if applicable]
3. Follow-up study at the completion of radiotherapy.
4. Follow-up study at the end of chemotherapy.
5. Follow-up study relapse [if applicable]

At each time point, 3D segmentations (DICOM SEG), seed points (DICOM RTSTRUCT) and negative finding assessments (DICOM RTSTRUCT) were created:

1. Enhancing tumor on an axial 3D T1 post contrast sequence
   1. If not available, a 3D post contrast sequence in another plane was used.
   2. If no 3D post contrast sequence was available, the tumor was annotated in all 3 planes utilizing 2D post contrast sequences.
   3. On post-contrast sequences, the entire tumor, including the cystic and non enhancing components was annotated.
   4. Any resection cavity or post-op changes/products was excluded.
2. Edema on an axial T2 FLAIR sequence
   1. If not available, an axial T2 or other T2 weighted sequence was used.
   2. The segmentation mask contains both the edematous tissue and the tumor.
3. The portion of the tumor demonstrating restricted diffusion on an ADC sequence
4. Up to 5 metastatic lesions within the brain and and up to 5 metastatic lesions in the spine as demonstrated on whatever T1 post contrast sequence they are visualized on
   1. When present, the 5 largest lesions were annotated.
5. A manually placed seed point (kernel) were created for each segmented structure
   1. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.
   2. Spinal metastases, which are too small to apply a volumetric mask to, only have a seed point annotation.
6. If no seed points or segmentations were generated a "Negative Assessment Report" RTSTRUCT file was created to record this fact.
7. To ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.

Important supplementary information and sample code

1. A spreadsheet containing a variety of useful metadata about the annotations, including calculated tumor volumes, is available in the Data Access section below.
2. Important information about how to interpret the DICOM annotation data can be found on the Detailed Description section below. It includes information about specific tags which document where the tumor was found, whether it was enhancing/non-enhancing, which study time point the annotation relates to, details for lesion tracking across time points, etc.
3. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and our REST API (with authentication) to access these data can be found in the Additional Resources section below.
4. Instructions for visualizing these data in 3D Slicer can be found in the Additional Resources section below.

This dataset contains image annotations derived from the NCI Clinical Trial "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”. 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.

Annotation Protocol

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient, all available time points were annotated. Every exam from the first available time point was annotated. One additional time point was annotated for each patient. The clinical data in the NCTN Archive was utilized to help determine the first evidence of disease progression. The first time point demonstrating disease progression was annotated. If that document was not accurate and did not demonstrate disease progression, then later time points were reviewed to assess for disease progression and the first time point demonstrating disease progression was annotated. If there was no evidence of disease progression on any time point, then the last available time point was annotated. Again, every exam from each chosen time point was annotated. For example, if there was a CT and a PET/CT, the PET was annotated along with one CT. If there was an MRI, that was annotated as well. The following annotation rules were followed:

1. PERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.
2. RECIST 1.1 was otherwise generally followed for any MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were however annotated if > 1 cm in short axis. Other lesions were annotated if > 1 cm. If the primary lesion is < 1 cm, it was still annotated. If there was evidence of disease progression with new lesions then additional annotations were allowed to demonstrate that progression. A representative sample of the new lesions was annotated at the radiologist's discretion.
3. Lesions were annotated in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.
4. MRIs were annotated using the T1-weighted post contrast sequence, fat saturated if available. If not available, T2-weighted sequences were utilized.
5. CTs were annotated using the axial post contrast series. If not available, the non contrast series was annotated.
6. PET/CTs were annotated on the CT and attenuation corrected PET images. However, if the post contrast CT was performed the same day as the PET/CT, the non contrast CT portion of the PET/CT was annotated.
7. Lesions were labeled separately.
8. The volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in a dataset metadata report.
9. Seed points were automatically generated but reviewed by a radiologist.
10. A “negative” annotation was created for any exam without findings.

At each time point:

1. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.
2. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
3. “Tracking ID” and “Tracking UID” tags were inserted for each segmented structure to enable longitudinal lesion tracking.
4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
   1. “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”
   2. Content Item in “Acquisition Context Sequence” was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”)
      2. (262502001, SCT, "Post-chemotherapy")

Important supplementary information and sample code

1. A spreadsheet containing key details about the annotations is available in the Data Access section below.
2. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and the REST API to access these data can be found in the Additional Resources section below.

This dataset contains image annotations derived from the NCI Clinical Trial "ACRIN-HNSCC-FDG-PET-CT (ACRIN 6685)”. 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.

Annotation Protocol

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:

1. PERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.
2. RECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. If the primary lesion is < 1 cm, it was still annotated.
3. Three-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.
4. MRIs were annotated using the T1-weighted axial post contrast sequence, fat saturated if available.
5. CTs were annotated using the axial post contrast series. If not available, the non contrast series were annotated.
6. PET/CTs were annotated on the CT and attenuation corrected PET images.
7. If the post contrast CT was performed the same day as the PET/CT, the non contrast CT portion of the PET/CT was not annotated.
8. Lesions were labeled separately.
9. The volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the Annotation Metadata CSV.
10. Seed points were automatically generated, but reviewed by a radiologist.
11. A “negative” annotation was created for any exam without findings.

At each time point:

1. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.
2. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
3. “Tracking ID” and “Tracking UID” tags were inserted for each segmented structure to enable longitudinal lesion tracking.
4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
   1. “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”.
   2. Content Item in “Acquisition Context Sequence” was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”) (in this trial, both the CT/MRI and PET/CT, while being different timepoints, are pre-treatment)

Important supplementary information and sample code

1. A spreadsheet containing key details about the annotations is available in the Data Access section below.
2. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and the REST API to access these data can be found in the Additional Resources section below.

PurposeTo investigate the inter-reader agreement of using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) for risk stratification of thyroid nodules.MethodsA literature search of Web of Science, PubMed, Cochrane Library, EMBASE, and Google Scholar was performed to identify eligible articles published from inception until October 31, 2021. We included studies reporting inter-reader agreement of different radiologists who applied ACR TI-RADS for the classification of thyroid nodules. Quality assessment of the included studies was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool and Guidelines for Reporting Reliability and Agreement Studies. The summary estimates of the inter-reader agreement were pooled with the random-effects model, and multiple subgroup analyses and meta-regression were performed to investigate various clinical settings.ResultsA total of 13 studies comprising 5,238 nodules were included in the current meta-analysis and systematic review. The pooled inter-reader agreement for overall ACR TI-RADS classification was moderate (κ = 0.51, 95% CI 0.42–0.59). Substantial heterogeneity was presented throughout the studies, and meta-regression analyses suggested that the malignant rate was the significant factor. Regarding the ultrasound (US) features, the best inter-reader agreement was composition (κ = 0.58, 95% CI 0.53–0.63), followed by shape (κ = 0.57, 95% CI 0.41–0.72), echogenicity (κ = 0.50, 95% CI 0.40–0.60), echogenic foci (κ = 0.44, 95% CI 0.36–0.53), and margin (κ = 0.34, 95% CI 0.24–0.44).ConclusionsThe ACR TI-RADS demonstrated moderate inter-reader agreement between radiologists for the overall classification. However, the US feature of margin only showed fair inter-reader reliability among different observers.

This dataset contains image annotations derived from "The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC)”. This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.

Annotation Protocol

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:

1. PERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.
2. RECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. The primary tumor was still annotated if < 1 cm.
3. Three-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.
4. MRIs were annotated using the T1-weighted axial post contrast sequence.
5. CTs were annotated using all axial post contrast series.
6. PET/CTs were annotated on the CT and attenuation corrected PET images, unless there was a diagnostic CT from the same time point, in which case the CT portion of the PET/CT was not annotated.
7. Lesions were labeled separately.
8. Seed points were automatically generated, but reviewed by a radiologist.
9. A “negative” annotation was created for any exam without findings.

At each time point:

1. Volume calculations were performed for each segmented structure. These calculations are provided in the Annotation Metadata CSV.
2. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.
3. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
   1. “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”.
   2. Content Item in “Acquisition Context Sequence” was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”)
      2. (719864002, SCT, "Post-cancer treatment monitoring")

Important supplementary information and sample code

1. A spreadsheet containing key details about the annotations is available in the Data Access section below.
2. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and the REST API to access these data can be found in the Additional Resources section below.

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 tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.

Annotation Protocol

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:

1. PERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.
2. RECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. If the primary lesion measures < 1 cm, it was still annotated.
3. Three-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.
4. MRIs were annotated using axial T1-weighted post contrast sequences that best demonstrated the tumor.
5. CTs were annotated using all axial post contrast series. If not available, the axial non-contrast series were annotated.
6. PET/CTs were annotated on the CT and attenuation corrected PET images, unless there was a diagnostic CT from the same time point, in which case the CT portion of the PET/CT was not annotated.
7. Lesions were labeled separately.
8. Seed points were automatically generated, but reviewed by a radiologist.
9. A “negative” annotation was created for any exam without findings.

At each time point:

1. Volume calculations were performed for each segmented structure. These calculations are provided in the Annotation Metadata CSV.
2. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.
3. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
   1. “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”.
   2. Content Item in “Acquisition Context Sequence” was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”)
      2. (719864002, SCT, "Post-cancer treatment monitoring")

Important supplementary information and sample code

1. A spreadsheet containing key details about the annotations is available in the Data Access section below.
2. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and the REST API to access these data can be found in the Additional Resources section below.

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)”. 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.

Annotation Protocol

For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were 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:

1. A seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.
2. SNOMED-CT “Anatomic Region Sequence” and “Segmented Property Category Code Sequence” and codes were inserted for all segmented structures.
3. “Tracking ID” and “Tracking UID” tags were inserted for each segmented structure to enable longitudinal lesion tracking.
4. Imaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.
   1. “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”
   2. Content Item in “Acquisition Context Sequence” was added containing "Time Point Type" using Concept Code Sequence (0040,A168) selected from:
      1. (255235001, SCT, “Pre-dose”)
      2. (262502001, SCT, "Post-chemotherapy")

Important supplementary information and sample code

1. A spreadsheet containing key details about the annotations is available in the Data Access section below.
2. A Jupyter notebook demonstrating how to use the NBIA Data Retriever Command-Line Interface application and the REST API (with authentication) to access these data can be found in the Additional Resources section below.