{"id":42851,"date":"2023-11-20T02:55:23","date_gmt":"2023-11-20T08:55:23","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/collection\/ispy1\/"},"modified":"2024-02-28T20:35:56","modified_gmt":"2024-02-29T02:35:56","slug":"ispy1","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/ispy1\/","title":{"rendered":"ISPY1"},"featured_media":0,"template":"","class_list":["post-42851","tcia_collection","type-tcia_collection","status-publish"],"cancer_types":["Breast Cancer"],"citations":[42837,42839,9225],"collection_doi":"10.7937\/K9\/TCIA.2016.HdHpgJLK","collection_download_info":"The ISPY team has provided additional options for download. The significance and download links for these subsets are explained under <u>Detailed Description<\/u>.","collection_downloads":[42841,42843,42845],"versions":[42849],"additional_resources":"The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=ispy1\">Imaging Data Commons (IDC)<\/a> (Imaging Data)<\/li>\r\n<\/ul>","cancer_locations":["Breast"],"collection_page_accessibility":"Public","publications_related":"<ul>\r\n \t<li><\/li>\r\n<\/ul>","version_change_log_archived":"<h3>Version 2 (Current): Updated 2016\/09\/28<\/h3><table><colgroup><col \/><col \/><col \/><\/colgroup><tbody><tr><th><p>Data Type<\/p><\/th><th><p>Download all or Query\/Filter<\/p><\/th><th><p>License<\/p><\/th><\/tr><tr><td><p>Images and Segmentations\u00a0(DICOM, 76.2GB)<\/p><\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1-full-09-26-2016.tcia\" download=\"doiJNLP-ISPY1-full-09-26-2016.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=ISPY1\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><p>(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a> )<\/p><\/div><\/td><td><div><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><tr><td>DICOM Metadata Digest (CSV)<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/ISPY1_MetaData.csv\" download=\"ISPY1_MetaData.csv\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><td><div><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><tr><td>Clinical and Outcome Data<\/td><td><div><p><br \/><a href=\"\/wp-content\/uploads\/I-SPY-1-All-Patient-Clinical-and-Outcome-Data.xlsx\" download=\"I-SPY-1-All-Patient-Clinical-and-Outcome-Data.xlsx\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><td><div><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table><p>Data publicly released and new \"level-specific\" download options provided.<\/p><h3>Version 1: Updated 2015\/06\/18<\/h3><table><colgroup> <col \/> <col \/> <\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Images (DICOM, 76.2GB)<\/td><td colspan=\"1\"><div><p><br \/><a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=ISPY1\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"The Collection authors suggest the below will give context to this dataset:\r\n<ul>\r\n \t<li>Hylton, N. M., Blume, J. D., Bernreuter, W. K., Pisano, E. D., Rosen, M. A., Morris, E. A., Weatherall, P. T., Lehman, C. D., Newstead, G. M., Polin, S., Marques, H. S., Esserman, L. J., &amp; Schnall, M. D. (2012). <strong>Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy\u2014Results from ACRIN 6657\/I-SPY TRIAL<\/strong> Radiology.\u00a0 <a href=\"https:\/\/doi.org\/10.1148\/radiol.12110748\">https:\/\/doi.org\/10.1148\/radiol.12110748<\/a> \u00a0\u00a0 \u00a0<a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/pmc3359517\/\">PMC3359517<\/a><\/li>\r\n \t<li>Hylton, N. M., Gatsonis, C. A., Rosen, M. A., Lehman, C. D., Newitt, D. C., Partridge, S. C., Bernreuter, W. K., Pisano, E. D., Morris, E. A., Weatherall, P. T., Polin, S. M., Newstead, G. M., Marques, H. S., Esserman, L. J., &amp; Schnall, M. D. (2016). Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival\u2014Results from the ACRIN 6657\/CALGB 150007 I-SPY 1 TRIAL.\u00a0 \u00a0<a href=\"https:\/\/doi.org\/10.1148\/radiol.2015150013\">https:\/\/doi.org\/10.1148\/radiol.2015150013<\/a> \u00a0<a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/pmc4819899\/\">PMC4819899<\/a><\/li>\r\n<\/ul>\r\n&nbsp;\r\n\r\nTCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage our data.\r\n<ol>\r\n \t<li>Al-Tashi, Q., Saad, M. B., Sheshadri, A., Wu, C. C., Chang, J. Y., Al-Lazikani, B., . . . Wu, J. (2023). SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. Patterns. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.patter.2023.100777\">10.1016\/j.patter.2023.100777<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nCattell, R. F., Kang, J. J., Ren, T., Huang, P. B., Muttreja, A., Dacosta, S., . . . Duong, T. Q. (2019). MRI volume changes of axillary lymph nodes as predictor of pathological complete responses to neoadjuvant chemotherapy in breast cancer. Clinical Breast Cancer. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.clbc.2019.06.006\">10.1016\/j.clbc.2019.06.006<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nChitalia, R., Pati, S., Bhalerao, M., Thakur, S. P., Jahani, N., Belenky, V., . . . Bakas, S. (2022). Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657\/I-SPY1. Sci Data, 9(1), 440. doi: <a href=\"https:\/\/doi.org\/10.1038\/s41597-022-01555-4\">10.1038\/s41597-022-01555-4<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nComes, M. C., Fanizzi, A., Bove, S., Didonna, V., Diotaiuti, S., La Forgia, D., . . . Massafra, R. (2021). Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs. Sci Rep, 11(1), 14123. doi: <a href=\"https:\/\/doi.org\/10.1038\/s41598-021-93592-z\">10.1038\/s41598-021-93592-z<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nDrukker, K., Edwards, A., Papaioannou, J., Giger, M., Hahn, H. K., &amp; Mazurowski, M. A. (2020). Long short-term memory networks predict breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy. Paper presented at the SPIE Medical Imaging, Houston TX USA. doi: <a href=\"https:\/\/doi.org\/10.1117\/12.2549044\">10.1117\/12.2549044<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nDu, R., &amp; Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montr\u00e9al, QC, Canada. Retrieved from <a href=\"https:\/\/proceedings.mlr.press\/v121\/du20a.html\">https:\/\/proceedings.mlr.press\/v121\/du20a.html<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nDuanmu, H., Huang, P. B., Brahmavar, S., Lin, S., Ren, T., Kong, J., . . . Duong, T. Q. (2020). Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data. In Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020 (Vol. 12262, pp. 242-252). Lima, Peru: Springer. doi: <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-59713-9_24\">10.1007\/978-3-030-59713-9_24<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nFan, M., Xia, P., Liu, B., Zhang, L., Wang, Y., Gao, X., &amp; Li, L. (2019). Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast Cancer Res, 21(1), 112. doi: <a href=\"https:\/\/doi.org\/10.1186\/s13058-019-1199-8\">10.1186\/s13058-019-1199-8<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nGierlinger, M., Brandner, D., &amp; Zagar, B. G. (2021, March 17th \u2013 18th, 2021). Vessel extraction from breast MR. Paper presented at the OCM 2021-Optical Characterization of Materials, KARLSRUHE | GERMANY. doi: <a href=\"https:\/\/doi.org\/10.5445\/KSP\/1000128686\">10.5445\/KSP\/1000128686<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nJahani, N., Cohen, E., Hsieh, M.-K., Weinstein, S. P., Pantalone, L., Hylton, N., . . . Kontos, D. (2019). Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration. Sci Rep, 9(1), 12114. doi: <a href=\"https:\/\/doi.org\/10.1038\/s41598-019-48465-x\">10.1038\/s41598-019-48465-x<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nKang, J., Li, H., Cattell, R., Talanki, V., Cohen, J. A., Bernstein, C. S., &amp; Duong, T. (2020). The contribution of axillary lymph node volume to recurrence-free survival status in breast cancer patients with sub-stratification by molecular subtypes and pathological complete response. Breast Cancer Research. doi: <a href=\"https:\/\/doi.org\/10.21203\/rs.3.rs-57680\/v1\">10.21203\/rs.3.rs-57680\/v1<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nLi, R., &amp; Chen, X. (2022). An efficient interactive multi-label segmentation tool for 2D and 3D medical images using fully connected conditional random field. Comput Methods Programs Biomed, 213, 106534. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.cmpb.2021.106534\">10.1016\/j.cmpb.2021.106534<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nMassafra, R., Comes, M. C., Bove, S., Didonna, V., Gatta, G., Giotta, F., . . . Paradiso, A. V. (2022). Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. Journal of Personalized Medicine, 12(6). doi: <a href=\"https:\/\/doi.org\/10.3390\/jpm12060953\">10.3390\/jpm12060953<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nMoyya, P. D., Asaithambi, M., &amp; Ramaniharan, A. K. (2022). Progesterone Receptor Status Analysis in Breast Cancer Patients using DCE- MR Images and Gabor Derived Anisotropy Index. Paper presented at the 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Messina, Italy. doi: <a href=\"https:\/\/doi.org\/10.1109\/MeMeA54994.2022.9856476\">10.1109\/MeMeA54994.2022.9856476<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nNave, O. (2020). Adding features from the mathematical model of breast cancer to predict the tumour size. International Journal of Computer Mathematics: Computer Systems Theory, 5(3), 159-174. doi: <a href=\"https:\/\/doi.org\/10.1080\/23799927.2020.1792552\">10.1080\/23799927.2020.1792552<\/a>.<\/li>\r\n \t<li>&nbsp;\r\n\r\nPati, S., Thakur, S. P., Hamamc\u0131, \u0130. E., Baid, U., Baheti, B., Bhalerao, M., . . . Bakas, S. (2023). GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows. Communications Engineering, 2(1). doi: <a href=\"https:\/\/doi.org\/10.1038\/s44172-023-00066-3\">10.1038\/s44172-023-00066-3<\/a>.<\/li>\r\n<\/ol>\r\n<h3>Altmetrics<\/h3>\r\n<script src=\"https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js\" type=\"text\/javascript\"><\/script>\r\n\r\nIf you have a manuscript you'd like to add please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA's Helpdesk<\/a>.","related_analysis_results":[45915,45937,46167],"species":["Human"],"version_number":"2","collection_title":"Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1\/ACRIN 6657 trials","date_updated":"2016-09-28","related_collection":false,"subjects":"222","analysis_results":"","collection_short_title":"ISPY1","data_types":["MR","SEG"],"version_change_log":"<p class=\"auto-cursor-target\">Data publicly released and new \"level-specific\" download options provided.<\/p>","collection_browse_title":"ISPY1 (ACRIN 6657)","detailed_description":"<h4>Requirements for MR imaging (As specified in the ACRIN 6657 protocol)<\/h4>\r\n<span style=\"text-decoration: underline;\">Imaging time points:<\/span> MRI exams were performed within four weeks prior to starting anthracycline-cyclophosphamide chemotherapy (T1, MRI<sub>1<\/sub>), at least 2 weeks after the first cycle of AC and prior to the second cycle of AC (T2,\u00a0MRI<sub>2<\/sub>), between anthracycline-cyclophosphamide treatment and taxane therapy if taxane was administered (T3,\u00a0MRI<sub>3<\/sub>), and after the final chemotherapy treatment and prior to surgery (T4,\u00a0MRI<sub>4<\/sub>). The study schema is shown in Figure 1\r\n\r\n<a href=\"\/wp-content\/uploads\/Fig1.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignright wp-image-732 size-medium\" src=\"\/wp-content\/uploads\/Fig1.png\" \/><\/a>\r\n\r\n<strong>Figure 1.<\/strong>\u00a0CALGB 150007 and ACRIN 6657 study schema.\r\n\r\nImaging protocol: MR imaging was performed on a 1.5 Tesla field strength scanner using a dedicated breast radiofrequency coil. The image acquisition protocol included a localization scan and T2-weighted sequence followed by a contrast-enhanced T1-weighted series. All imaging was performed unilaterally over the symptomatic breast and in the sagittal orientation. The contrast-enhanced series consisted of a high resolution (\u22641mm in-plane spatial resolution) three-dimensional, fat-suppressed, T1-weighted gradient echo sequence with TR\u226420 ms, TE = 4.5 ms, flip angle \u2264 45\u00ba, 16-18 cm field-of-view, minimum matrix 256x192, 64 slices, slice thickness \u2264 2.5 mm. Scan time length for the T1-weighted sequence was required to be between 4.5 and 5 minutes. The sequence was acquired once before contrast injection and repeated at least twice following injection.\r\n\r\nTumor diameter measurement and volumetric analysis: Tumor longest diameter (LD) was measured by the site radiologist as the greatest extent of disease on baseline MR images, including intervening areas of non-enhancing tissue. The same measurement direction was used on all subsequent MRI exams. The primary predictor variable, functional tumor volume (FTV) was measured from contrast-enhanced images using the signal enhancement ratio (SER) method. Volumetric analysis, including Quality Control assessment, was performed centrally at the Breast Imaging Research Program (BIRP) laboratory at University of California at San Francisco (UCSF).\r\n\r\nDetailed information about the DICOM data is available in the <u> <a href=\"\/display\/Public\/https:\/\/www.cancerimagingarchive.net\/wp-content\/uploads\/Public-ISPY-1DCEMRIDataSharingDICOMDictionary.pdf\">DICOM Dictionary<\/a><\/u>.\r\n<h3>Further information on these studies can be found at:<\/h3>\r\n<ul>\r\n \t<li>ACRIN 6657 Protocol <a href=\"https:\/\/www.acr.org\/Research\/Clinical-Research\/ACRIN-Legacy-Trials\">https:\/\/www.acr.org\/Research\/Clinical-Research\/ACRIN-Legacy-Trials<\/a><\/li>\r\n \t<li>CALGB 150007 <a href=\"https:\/\/clinicaltrials.gov\/study\/NCT01042379\">https:\/\/clinicaltrials.gov\/study\/NCT01042379<\/a><\/li>\r\n<\/ul>\r\n<h3>Imaging Data Transfer History<\/h3>\r\nThe processing of the MR image data for ACRIN 6657 consisted of the following steps between image acquisition and the creation of this shared data set on TCIA:\r\n<ul>\r\n \t<li>MRI studies were sent from the study centers to the ACRIN Core Lab either via media (DVD) or the TRIAD program<\/li>\r\n \t<li>Image data were de-identified and centrally archived at the ACRIN Core Lab<\/li>\r\n \t<li>Archived data was sent to the Breast Imaging Research Program (BIRP) at the University of California, San Francisco (UCSF) for volumetric analysis.<\/li>\r\n \t<li>De-identified image data, derived analysis maps and segmentations, and ancillary data files were transferred from UCSF to TCIA for data sharing.<\/li>\r\n<\/ul>\r\n<em>While every effort was made to preserve the integrity of both the original image data and image meta-data (DICOM attributes, public and private), multiple file transfers and strict adherence to HIPPA guidelines for patient confidentiality may have resulted in loss of some data. If any questions arise, or patient PHI is found in any data on this collection, please contact the\u00a0<a href=\"mailto:birp@ucsf.edu\">UCSF Breast Imaging Research Program (BIRP)<\/a>. For scientific or other inquiries about this dataset, please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA's Helpdesk<\/a><\/em><em>.<\/em>\r\n<h3>Curated Data Sets<\/h3>\r\nIn addition to the complete set of ACRIN 6657 imaging studies (\"Level 0\" data), the following curated data sets based on UCSF QC assessment, protocol compliance and data completeness are provided:\r\n<ul>\r\n \t<li>Level 1: \u00a0\u00a0\u00a0MRI Longest Diameter (LD)<\/li>\r\n \t<li>Level 2a: \u00a0SER Volume Dataset for ongoing volumetric analyses (updated 9\/17\/16)<\/li>\r\n \t<li>Level 2b: \u00a0SER Volume Dataset for pCR Analysis (Hylton, et al; Radiology 2012)<\/li>\r\n \t<li>Level 3: \u00a0 \u00a0SER Volume Dataset for RFS Analysis (Hylton, et al; Radiology, 2016)<\/li>\r\n<\/ul>\r\nThe image data sets are accompanied by Excel files with selected patient clinical and outcome data.\r\n<h4>Data subset Descriptions<\/h4>\r\n<table><colgroup> <col \/> <col \/> <col \/> <col \/> <col \/> <col \/> <\/colgroup>\r\n<tbody>\r\n<tr>\r\n<th>\u00a0Data set<\/th>\r\n<th colspan=\"1\">subjects<\/th>\r\n<th>All Series<\/th>\r\n<th>DCE + Derived Only<\/th>\r\n<th>DCE Only<\/th>\r\n<th colspan=\"1\">Clinical and outcome data<\/th>\r\n<\/tr>\r\n<tr>\r\n<td colspan=\"1\">Level 0: Complete image data set<\/td>\r\n<td colspan=\"1\">222<\/td>\r\n<td colspan=\"1\">\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1-full-09-26-2016.tcia\" download=\"doiJNLP-ISPY1-full-09-26-2016.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(76 GB)\r\n\r\n<\/div><\/td>\r\n<td colspan=\"1\">NA<\/td>\r\n<td colspan=\"1\">NA<\/td>\r\n<td colspan=\"1\">\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/I-SPY-1-All-Patient-Clinical-and-Outcome-Data.xlsx\" download=\"I-SPY-1-All-Patient-Clinical-and-Outcome-Data.xlsx\"><button><i><\/i> Download<\/button><\/a>\r\n(48 KB)\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Level 1: Studies with MRI LD measurements<\/td>\r\n<td colspan=\"1\">219<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_1_Good_MRI_LD_Measurement-All_Series.tcia\" download=\"doiJNLP-ISPY1_Level_1_Good_MRI_LD_Measurement-All_Series.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(75 GB)\r\n\r\n<\/div><\/td>\r\n<td>NA<\/td>\r\n<td>NA<\/td>\r\n<td colspan=\"1\">\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/I-SPY-1-level1-Patient-Clinical-and-Outcome-Data.xlsx\" download=\"I-SPY-1-level1-Patient-Clinical-and-Outcome-Data.xlsx\"><button><i><\/i> Download<\/button><\/a>\r\n(46 KB)\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Level 2a: Studies with SER Volume measurements<\/td>\r\n<td colspan=\"1\">207<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_2a_Good_VOLSER-All_Series.tcia\" download=\"doiJNLP-ISPY1_Level_2a_Good_VOLSER-All_Series.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(63 GB)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_2a_Good_VOLSER-DCEandDerived_Series.tcia\" download=\"doiJNLP-ISPY1_Level_2a_Good_VOLSER-DCEandDerived_Series.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(43 GB)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_2a_Good_VOLSER-DCE_Series_Only.tcia\" download=\"doiJNLP-ISPY1_Level_2a_Good_VOLSER-DCE_Series_Only.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(24 GB)\r\n\r\n<\/div><\/td>\r\n<td colspan=\"1\">\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/I-SPY-1-level2a-Patient-Clinical-and-Outcome-Data.xlsx\" download=\"I-SPY-1-level2a-Patient-Clinical-and-Outcome-Data.xlsx\"><button><i><\/i> Download<\/button><\/a>\r\n(72 KB)\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Level 3: Studies used in primary aim analysis<\/td>\r\n<td colspan=\"1\">162<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_3_PrimaryAim-All_Series.tcia\" download=\"doiJNLP-ISPY1_Level_3_PrimaryAim-All_Series.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(49 GB)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_3_PrimaryAim-DCEandDerived_Series.tcia\" download=\"doiJNLP-ISPY1_Level_3_PrimaryAim-DCEandDerived_Series.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(34 GB)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-ISPY1_Level_3_PrimaryAim-DCE_Series_Only.tcia\" download=\"doiJNLP-ISPY1_Level_3_PrimaryAim-DCE_Series_Only.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(18 GB)\r\n\r\n<\/div><\/td>\r\n<td colspan=\"1\">\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/I-SPY-1-level3-Patient-Clinical-and-Outcome-Data.xlsx\" download=\"I-SPY-1-level3-Patient-Clinical-and-Outcome-Data.xlsx\"><button><i><\/i> Download<\/button><\/a>\r\n(76 KB)\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<h5>Level 0: Complete I-SPY 1 \/ ACRIN 6657 MRI Dataset<\/h5>\r\nThis data set is comprised of all HIPAA compliant, DICOM compliant MRI series.\r\n\r\nLevel 0 image data set consists of 847 on-study MRI studies on 222 subjects in the UCSF image database.\r\nOne patient in the image data collection (I-SPY ID 1079) does not appear in the Feb. 2, 2011 I-SPY FINAL LOCKED clinical data dump. So no clinical or outcome data is available for this subject.\r\n<h5>Level 1:\u00a0MRI exams for which longest diameter was measured<\/h5>\r\nThis data set is comprised of all studies with MRI measured longest diameter (LD) values reported.\r\n839 MRI studies have LD reported in the I-SPY 1 clinical database, of which 5 studies are not present in either the UCSF or ACRIN image data collections (see Table 1).\r\n<strong>Level 1<\/strong>\u00a0image data set\u00a0consists of\u00a0<strong>834 MRI studies on 219 subjects<\/strong>\u00a0in the UCSF image database\r\n<table>\r\n<tbody>\r\n<tr>\r\n<th colspan=\"3\">Table 1. Studies that have LD measurement but are missing from\r\n\r\nthe UCSF and ACRIN TRIAD image data collections:<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>ID 1071, T1\r\nID 1138, T1<\/td>\r\n<td>ID 1101, T3\r\nID 1040, T4<\/td>\r\n<td>ID 1187, T4<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<h5>Level 2a: Good SER Volume Dataset \u2013 updated 9\/3\/14, 9\/17\/16<\/h5>\r\nThis data set is comprised of the patient studies\u00a0which, following quality reviews in 2014 and 2016, were judged to have sufficiently good image quality and protocol compliance for volumetric DCE SER analysis. Rejection criteria included: incomplete volumetric DCE acquisitions, lack of a 2nd post-contrast acquisition, variability in fat suppression across the image, observed patient motion during the DCE acquisition, significant DCE protocol deviations such as changing scan parameters or image position during DCE acquisition.\r\n\r\n<strong>Level 2a<\/strong>\u00a0image data set\u00a0consists of\u00a0<strong>706 MR studies on 207 subjects<\/strong>\u00a0in the UCSF image database. These include 7 studies not included in Level 1 (no MRI LD recorded) as listed in Table 2.\r\n<table><colgroup> <col \/> <col \/> <col \/> <\/colgroup>\r\n<tbody>\r\n<tr>\r\n<th colspan=\"3\">Table 2. Studies in Level 2a (good volumetric analysis) that do NOT have LD measures:<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>ID 1059, T4\r\nID 1079, T2 *\r\nID 1104, T4<\/td>\r\n<td>ID 1192, T2\r\nID 1212, T4<\/td>\r\n<td>ID 1215, T1\r\nID 1238, T2<\/td>\r\n<\/tr>\r\n<tr>\r\n<td colspan=\"3\">\u00a0 * Patient 1079 does not appear in the Feb. 2, 2011 I-SPY FINAL LOCKED clinical data set. So no clinical or outcome data is available.<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<h5>Level 2b: SER Volume Dataset Reported in <a href=\"https:\/\/doi.org\/10.1148\/radiol.12110748\">Hylton et al. (Radiology, 2012)<\/a><\/h5>\r\nThis data set is comprised of the patient studies analyzed for pCR outcome and reported in the 2012 Radiology paper on ACRIN 6657 pCR results *. This data set is not provided as a shared list, as it is not recommended for use in further analysis. It is described here because it is the data set from which the Level 3 (primary aim analysis) set was derived.\u00a0Inclusion and exclusion was determined by quality and protocol reviews available at that time. In addition to the exclusion criteria listed for Level 2a, studies done with imaging in the axial plane, in violation of the sagittal orientation specified in the trial imaging protocol, were excluded due to processing limitations of the analysis software. Similarly, bi-lateral sagittal acquisitions (alternating left and right volumetric acquisitions) were excluded.\r\n\r\n<strong>Level 2b<\/strong>\u00a0image data set\u00a0consists of\u00a0<strong>707 MRI studies on 207 subjects<\/strong>\u00a0in the UCSF image database.\r\n<h5>Tables 3 and 4 show the specific inclusion\/exclusion differences between Levels 2a and 2b:<\/h5>\r\n<table>\r\n<tbody>\r\n<tr>\r\n<th colspan=\"3\">Table 3. 16 studies\u00a0<strong>accepted<\/strong>\u00a0for SER analysis since 2008 (in Level 2a but not in 2b)<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>ID 1005, T3\r\nID 1043, T2\r\nID 1046, T4\r\nID 1057, T3\r\nID 1074, T3\r\nID 1084, T1<\/td>\r\n<td>ID 1110, T4\r\nID 1139, T4\r\nID 1159, T4\r\nID 1176, T2\r\nID 1201, T2<\/td>\r\n<td>ID 1203, T4\r\nID 1206, T4\r\nID 1225, T3\r\nID 1219, T3\r\nID 1228, T4<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n&nbsp;\r\n<table><colgroup> <col \/> <col \/> <\/colgroup>\r\n<tbody>\r\n<tr>\r\n<td colspan=\"2\"><strong>Table 4. 17 studies\u00a0rejected\u00a0since 2008 (in Level 2b but not in 2a)<\/strong><\/td>\r\n<\/tr>\r\n<tr>\r\n<th>Study ID and TP<\/th>\r\n<th>Reason for rejection for volumetric SER analysis (level 2a)<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>1007 T4 *<\/td>\r\n<td>No fatSat; Different protocol from T1<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1035 T4 *<\/td>\r\n<td>Only 1 post scan then acq. parameters changed<\/td>\r\n<\/tr>\r\n<tr>\r\n<td colspan=\"1\">1045 T1<\/td>\r\n<td colspan=\"1\">Alternating laterality acquisitions, 2 minute time gap<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1047 T1 *<\/td>\r\n<td>Image position changed during DCE<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1053 T2 *<\/td>\r\n<td>Alternating laterality acquisitions, bad pre- acquisition<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1053 T4 *<\/td>\r\n<td>Alternating laterality acquisitions<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1055 T1 *<\/td>\r\n<td>Alternating laterality acquisitions, 4 minute time gap<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1086 T1 *<\/td>\r\n<td>Alternating laterality acquisitions, time gap, different protocol from T4<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1091 T1 *<\/td>\r\n<td>Changing acq. parameters during DCE<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1095 T2 *<\/td>\r\n<td>Only 1 post scan then acq. parameters changed<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1173 T3 *<\/td>\r\n<td>Off protocol timing<\/td>\r\n<\/tr>\r\n<tr>\r\n<td colspan=\"1\">1206 T1<\/td>\r\n<td colspan=\"1\">Bad DCE timing, 20 minute delay<\/td>\r\n<\/tr>\r\n<tr>\r\n<td colspan=\"1\">1206 T2<\/td>\r\n<td colspan=\"1\">Bad DCE timing, 1'29\" acquisition time<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1224 T3 *<\/td>\r\n<td>Scan position changed during DCE<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>1230 T3 *<\/td>\r\n<td>Scan position changed during DCE<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>#128 T1, T2<\/td>\r\n<td>2 studies for ineligible patient:\u00a0\u00a0ACRIN ID 128 (no I-SPY ID)<\/td>\r\n<\/tr>\r\n<tr>\r\n<td colspan=\"2\">\u00a0* Subjects that <em>were<\/em>\u00a0included in the primary aim analysis (Level 3)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<h5><strong>Level 3: Subset of Level 2b used in primary aim analysis,\u00a0<\/strong>reported in <a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/pmc4819899\/\">Hylton et al. (Radiology, 2016)<\/a> *<\/h5>\r\nThis data set is comprised of the patient\u00a0studies analyzed for RFS outcome and reported in the 2015 Radiology paper on ACRIN 6657 survival results (Hylton et al, Radiology *). Table 5 shows the 45 patients excluded from the level 2a cohort for this analysis. Please see the publication for specific information on exclusions of patients from this group.\r\n\r\n<strong>Level 3<\/strong>\u00a0image data set\u00a0consists of\u00a0<strong>586 MRI studies on 162 subjects<\/strong>\u00a0in the UCSF image database. This is also the study cohort used as the Test Phase data in the QIN BMMR Challenge.\r\n<table>\r\n<tbody>\r\n<tr>\r\n<th colspan=\"6\">Table 5. 45 subjects excluded from Level 2b set<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>ID 1027\r\n\r\nID 1040\r\n\r\nID 1045\r\n\r\nID 1046\r\n\r\nID 1048\r\n\r\nID 1054\r\n\r\nID 1063\r\n\r\nID 1067<\/td>\r\n<td>ID 1079\r\n\r\nID 1084\r\n\r\nID 1103\r\n\r\nID 1110\r\n\r\nID 1120\r\n\r\nID 1137\r\n\r\nID 1139\r\n\r\nID 1152<\/td>\r\n<td>ID 1157\r\n\r\nID 1159\r\n\r\nID 1160\r\n\r\nID 1167\r\n\r\nID 1171\r\n\r\nID 1176\r\n\r\nID 1177\r\n\r\nID 1180<\/td>\r\n<td>ID 1182\r\n\r\nID 1185\r\n\r\nID 1187\r\n\r\nID 1189\r\n\r\nID 1192\r\n\r\nID 1194\r\n\r\nID 1203\r\n\r\nID 1206<\/td>\r\n<td>ID 1210\r\n\r\nID 1212\r\n\r\nID 1214\r\n\r\nID 1215\r\n\r\nID 1219\r\n\r\nID 1221\r\n\r\nID 1222\r\n\r\nID 1228<\/td>\r\n<td>ID 1234\r\n\r\nID 1235\r\n\r\nID 1237\r\n\r\nID 1238\r\n\r\nineligible:\r\n\r\nCase\u00a0 #: 128<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n&nbsp;","supporting_data":["Clinical","Image Analyses"],"collection_featured_image":false,"collection_summary":"<p><u> <strong> <a href=\"https:\/\/www.acrin.org\/6657_protocol.aspx\">ACRIN 6657<\/a> <\/strong> <\/u> was designed as a prospective study to test MRI for ability to predict response to treatment and risk-of-recurrence in patients with stage 2 or 3 breast cancer receiving neoadjuvant chemotherapy (NACT). <u> <strong> <a href=\"https:\/\/www.acrin.org\/6657_protocol.aspx\">ACRIN 6657<\/a> <\/strong> <\/u> was conducted as a companion study to CALGB 150007, a correlative science study evaluating tissue-based biomarkers in the setting of neoadjuvant treatment of breast cancer.\u00a0Collectively, CALGB 150007 and ACRIN 6657 formed the basis of the multicenter\u00a0<strong>I<\/strong>nvestigation of\u00a0<strong>S<\/strong>erial Studies to\u00a0<strong>P<\/strong>redict\u00a0<strong>Y<\/strong>our\u00a0<strong>T<\/strong>herapeutic\u00a0<strong>R<\/strong>esponse with\u00a0<strong>I<\/strong>maging and mo<strong>L<\/strong>ecular\u00a0<strong>A<\/strong>nalysis (I-SPY TRIAL) breast cancer trial, a study of imaging and tissue-based biomarkers for predicting pathologic complete response (pCR) and recurrence-free survival (RFS). Additional information about the trial is available in the <a href=\"\/wp-content\/uploads\/ACRIN-6657_protocol.pdf\" download=\"ACRIN-6657_protocol.pdf\" data-linked-resource-container-id=\"20643859\" data-linked-resource-container-version=\"100\" data-linked-resource-content-type=\"application\/pdf\" data-linked-resource-default-alias=\"ACRIN 6657_protocol.pdf\" data-linked-resource-id=\"145753949\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"PDF Document\">Study Protocol<\/a> and <a href=\"\/wp-content\/uploads\/ACRIN-6657_CRF-Set.pdf\" download=\"ACRIN-6657_CRF-Set.pdf\" data-linked-resource-container-id=\"20643859\" data-linked-resource-container-version=\"100\" data-linked-resource-content-type=\"application\/pdf\" data-linked-resource-default-alias=\"ACRIN 6657_CRF Set.pdf\" data-linked-resource-id=\"145753950\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"PDF Document\">Case Report Forms<\/a>.\u00a0<\/p><p><span style=\"text-decoration: underline;\">Participant Eligibility and Enrollment:<\/span> Criteria for inclusion were patients enrolling on CALGB 150007 with T3 tumors measuring at least 3 cm in diameter by clinical exam or imaging and receiving neoadjuvant chemotherapy with an\u00a0\u00a0anthracycline-cyclophosphamide regimen alone or followed by a taxane. Pregnant patients and those with ferromagnetic prostheses were excluded from the study. The study was open to enrollment from May 2002 to March 2006. 237 patients were enrolled, of which 230 met eligibility criteria.<\/p>","collection_acknowledgements":"<p>This shared data set was provided by David Newitt, PhD and Nola Hylton, PhD from\u00a0the Breast Imaging Research Program at UCSF, in collaboration with\u00a0ACRIN, CALGB, the\u00a0I-SPY TRIAL, and TCIA.\u00a0Many thanks are due to <u><a href=\"\/wp-content\/uploads\/Investigator-List-ACRIN-6657-July-16-2015.docx\" download=\"Investigator-List-ACRIN-6657-July-16-2015.docx\" data-linked-resource-container-id=\"20643859\" data-linked-resource-container-version=\"100\" data-linked-resource-content-type=\"application\/vnd.openxmlformats-officedocument.wordprocessingml.document\" data-linked-resource-default-alias=\"Investigator List ACRIN 6657 July 16 2015.docx\" data-linked-resource-id=\"21692558\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"Word Document\">The ACRIN 6657 trial team<\/a><\/u>, <u><a href=\"\/wp-content\/uploads\/Investigator-List-I-SPY-1-July-16-2015.docx\" download=\"Investigator-List-I-SPY-1-July-16-2015.docx\" data-linked-resource-container-id=\"20643859\" data-linked-resource-container-version=\"100\" data-linked-resource-content-type=\"application\/vnd.openxmlformats-officedocument.wordprocessingml.document\" data-linked-resource-default-alias=\"Investigator List I-SPY 1 - July 16 2015.docx\" data-linked-resource-id=\"21692559\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"Word Document\">The I-SPY 1 TRIAL team<\/a><\/u>, and all the patients participating in these studies.<\/p><p>Funding sources include NIH grants to UCSF (R01 CA132870 and U01 CA151235), ACRIN (UO1 CA079778 and UO1 CA080098), and CALGB (UO1 CA31964 and UO1 CA33601).<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["NCI Trials"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42851","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42851\/revisions"}],"predecessor-version":[{"id":47423,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42851\/revisions\/47423"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}