{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:59:21Z","timestamp":1791910761441,"version":"4.3.4"},"reference-count":16,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"4","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clinical Cancer Informatics"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p> Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health\/National Cancer Institute (NIH\/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research. <\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p> Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH\/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses. <\/jats:p><\/jats:sec><jats:sec><jats:title>RESULT<\/jats:title><jats:p> The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use. <\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p> Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package. <\/jats:p><\/jats:sec>","DOI":"10.1200\/cci.19.00129","type":"journal-article","created":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T19:59:18Z","timestamp":1585339158000},"page":"290-298","update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":8,"title":["MLCD: A Unified Software Package for Cancer Diagnosis"],"prefix":"10.1200","author":[{"given":"Wenjun","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibin","family":"Li","sequence":"additional","affiliation":[{"name":"Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ezgi","family":"Mercan","sequence":"additional","affiliation":[{"name":"Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA"},{"name":"Craniofacial Center, Seattle Children\u2019s Hospital, Seattle WA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sachin","family":"Mehta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Washington, Seattle, WA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamen","family":"Bartlett","sequence":"additional","affiliation":[{"name":"University of Vermont Medical Center, Burlington, VT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald L.","family":"Weaver","sequence":"additional","affiliation":[{"name":"Department of Pathology and University of Vermont Cancer Center, Larner College of Medicine, University of Vermont, Burlington, VT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joann G.","family":"Elmore","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda G.","family":"Shapiro","sequence":"additional","affiliation":[{"name":"Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"B1","volume-title":"FDA OKs marketing of whole slide imaging for digital pathology","author":"Swift D","year":"2017"},{"key":"B3","doi-asserted-by":"crossref","unstructured":"Mehta S, Mercan E, Bartlett J, et al: Y-net: Joint segmentation and classification for diagnosis of breast biopsy images, International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2018, pp 893-901","DOI":"10.1007\/978-3-030-00934-2_99"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.8777"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0323"},{"key":"B7","unstructured":"Marcus D, Olsen T, Ramaratnam M, et al: XNAT: A software framework for managing neuroimaging laboratory data. 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