{"id":"CU26309","slug":"automated-breast-density--CU26309","source":{"id":"CU26309","dataset":"techtransfer","title":"Automated breast density measurement to support personalized breast cancer screening","description_":"<p>This technology is an automated software platform that segments and analyzes digital breast tomosynthesis (DBT) images to provide objective and reproducible breast density assessments for cancer screening, risk stratification, and monitoring. </p>\r\r<h2>Unmet Need: Standardized and reliable breast density measurement for risk assessment</h2>\r\r<p>Breast density is an important predictor of breast cancer risk and is used to guide patient monitoring and screening decisions. Current breast density assessment relies heavily on the Breast Imagining Reporting and Data System (BI-RADS), which is based on radiologist interpretation and can vary with experience, training, and subjective judgment. In addition, breast density measurements from digital breast tomosynthesis (DBT) can be influenced by variations in imaging hardware, reconstruction algorithms, and post-processing techniques. These variabilities can limit consistency of longitudinal monitoring and breast cancer risk assessment. There is a need for standardized, reproducible methods to accurately quantify breast density across imagining platforms.</p>\r\r<h2>The Technology: Automated breast density measurement from digital breast tomosynthesis</h2>\r\r<p>This technology is an automated platform that analyzes digital breast tomosynthesis (DBT) images to estimate volumetric breast density. Using image segmentation and tissue classification methods, it distinguishes dense breast tissue from fatty tissue and generates an objective measurement of breast density. The software incorporates image standardization and refinement steps to reduce variability caused by differences in imaging systems and processing workflows, enabling more consistent density assessment over time and across sites.</p>\r\r<p>Preliminary validation cohorts demonstrated strong within-patient correlation of volumetric breast density measurements over time, with slightly reduced consistency across vendors, and strong agreement with BI-RADS categories. </p>\r\r<h2>Applications:</h2>\r\r<ul>\r<li>Breast cancer screening and breast density assessment </li>\r<li>Longitudinal monitoring of breast density changes </li>\r<li>Multi-vendor imaging studies </li>\r<li>Decision-support tool for radiologists </li>\r<li>Developing other imaging-based cancer risk assessment tools </li>\r<li>Automated tissue characterization and segmentation methods </li>\r<li>Pregnancy and pre-term birth monitoring </li>\r</ul>\r\r<h2>Advantages:</h2>\r\r<ul>\r<li>Provides objective and reproducible breast density measurements </li>\r<li>Reduces variability associated with differences in imaging hardware and image processing</li>\r<li>Reduces inter-operator variability </li>\r<li>Demonstrates agreement with clinically used BI-RADS density classification </li>\r<li>Potentially less time consuming that traditional screening processes</li>\r<li>Enables reliable monitoring of breast density changes over time </li>\r<li>Easily implementable at multiple different sites </li>\r<li>Easily integrates with current clinical workflow </li>\r</ul>\r\r<h2>Lead Inventor:</h2>\r\r<p><a href=\"https://www.columbiaradiology.org/profile/despina-kontos-phd\">Despina Kontos, Ph.D.</a> </p>\r\r<h2>Tech Ventures Reference:</h2>\r\r<ul>\r<li><p>IR CU26309</p></li>\r<li><p>Licensing Contact: <a href=\"mailto:techtransfer@columbia.edu\">Joan Martinez</a></p></li>\r</ul>\r","tags":["Adipose tissue","Breast cancer","Breast cancer screening","Cancer","Digital image processing","Image segmentation","Radiology","Risk assessment","Stratified sampling","Workflow"],"file_number":"CU26309","collections":[],"meta_description":"Automated DBT breast density analytics standardizes, quantifies density across platforms to improve screening, risk assessment, and monitoring.","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":3.0,\"scalability\":4.0,\"timeliness\":3.0},\"weighted_score\":3.95,\"risks\":[\"Readiness may be influenced by regulatory/clinical adoption hurdles.\",\"Potential competition from existing density quantification tools.\",\"Need for external prospective validation across diverse populations.\"],\"one_sentence_take\":\"Strong cross-platform automated density quantification with practical clinical utility, but readiness and timeliness depend on regulatory clearance and broader prospective validation.\"}","inventors":["Anne Marie McCarthy","Despina Kontos","Eric A. Cohen","Nehal Doiphode","Sarah Ehsan","Walter C. Mankowski"],"manager":"Joan Martinez","depts":["Biomedical Engineering","Biostatistics, Epidemiology & Informatics","Radiology"],"divs":["Columbia University Medical Center (CUMC)","Fu Foundation School of Engineering and Applied Science (SEAS)","Perelman School of Medicine"],"date_released":"2026-07-22"},"highlight":{},"matched_queries":null,"score":0.0}