{"id":5557,"date":"2023-09-04T03:08:07","date_gmt":"2023-09-04T03:08:07","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/breast-cancer-screening-dbt\/"},"modified":"2023-09-13T11:57:08","modified_gmt":"2023-09-13T11:57:08","slug":"breast-cancer-screening-dbt","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/breast-cancer-screening-dbt\/","title":{"rendered":"BREAST-CANCER-SCREENING-DBT"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Breast Cancer"],"citations":[4397,4398,2925],"collection_doi":"10.7937\/E4WT-CD02","collection_download_info":"Click the Versions tab for more info about data releases.","collection_downloads":[4936,4937,4938,4939,4940,4941,4942,4943],"full_export":"

Summary<\/h2>

Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.<\/span><\/p>

Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.\u00a0 The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) I<\/span>mage paths for patients\/studies\/views<\/span>.\u00a0 A detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:<\/span><\/p>

M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. \u015awi\u0119cicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model.<\/span>\u00a0(https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100<\/a><\/span>).<\/em><\/p><\/div>

Additional information and resources related to this dataset can be found here:\u00a0<\/span>https:\/\/sites.duke.edu\/mazurowski\/resources\/digital-breast-tomosynthesis-database\/<\/a><\/p>

A Version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.<\/p>

Please visit this discussion forum for any questions related to the data: https:\/\/www.reddit.com\/r\/DukeDBTData\/<\/a><\/p>

Required Preprocessing of DBT Images<\/h3>

For some of the images, the laterality stored in the DICOM header and\/or image orientation are incorrect. The reference standard "truth" boxes are defined with respect to the\u00a0corrected<\/strong>\u00a0image orientation in these instances. Therefore, it is\u00a0crucial<\/strong>\u00a0to provide your results for images in the correct image orientation. Python functions for loading image data from a DICOM file into 3D array of pixel values in the correct orientation and for displaying "truth" boxes (if any) are on\u00a0GitHub<\/a>. Please see the readme file there for instructions.<\/p>

DBTex Lesion Detection Challenge Predictions<\/h3>

The\u00a0DBTex lesion detection challenge tasked participating teams with detecting lesions in the BCS-DBT test set. The challenge had two phases:\u00a0DBTex1<\/a>\u00a0and\u00a0DBTex2<\/a>.\u00a0Here<\/a><\/strong>\u00a0we provide the BCS-DBT lesion predictions made by all participating teams for both phases, for both the BCS-DBT test and validation sets, as \u201cteam_predictions_bothphases.zip\u201d. Please see\u00a0here<\/a>\u00a0under \u201cOutput format for the DBTex2 Challenge test set results<\/strong>\u201d for a description of how these results are formatted. Finally, when comparing lesion bounding box predictions to the image data, be sure to load the images correctly according to the above \u201cRequired Preprocessing of DBT Images<\/strong>\u201d.\u00a0<\/span><\/p>

If you use these predictions, please reference the DBTex challenge paper:<\/span><\/p>

Konz N, Buda M, Gu H, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.\u00a0JAMA Netw Open.<\/em>\u00a02023;6(2):e230524. doi:10.1001\/jamanetworkopen.2023.0524<\/span><\/p>

Acknowledgements<\/span><\/h3>

We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p>