{"collection_id":"advanced_mri_breast_lesions","collection_name":"Advanced-MRI-Breast-Lesions","cancer_types":"Breast Cancer","tumor_locations":"Breast","species":"Human","subjects":632,"supporting_data":"Clinical","description":"The application of deep-learning methods to breast MRI has a potential to improve diagnosis as well\nas predict pathological features of the tumors and their response to therapy, and is currently\nwidely investigated. Standard contrast-enhanced MRI of the breast provides high sensitivity but\nvariable specificity in detecting breast cancer, and may lead to excessive benign biopsies. Large,\nwell annotated datasets are needed to improve the current results in this field. This dataset is a\nsingle-institutional, retrospective collection of 632 breast-MRI imaging sessions acquired on a 1.5T\nMR system between 2018-2021. Supporting data, collected for 200 patients from clinical notes,\nradiology report and pathology reports, is included: patient age, lesion location, malignant/benign\noutcome, pathology and grade (if applicable), receptor status and KI67 (if applicable). The main\nuniqueness of this data is that it contains the standard protocol series (T1-weighted DCE sequence\nwith 5 time points- before and 4 time points after contrast injection, and T2-weighted MRI\nwith/without fat suppression) as well as the delayed contrast T1-images acquired 20-28 minutes after\ncontrast administration and the calculated treatment response assessment maps (TRAMs).\nPlease see the\nAdvanced-MRI-Breast-Lesions (https://doi.org/10.7937/C7X1-YN57) collection page to learn more\nabout the images and to obtain any supporting metadata for this collection.","patients":632,"studies":632,"series":6811,"instances":1259688,"size_TB":0.646,"modalities":["MR","SEG"],"licenses":[{"license_short_name":"CC BY 4.0","series":6811,"size_TB":0.646}]}