Journal article
Lymph Node Metastasis Status in Breast Carcinoma Can Be Predicted via Image Analysis of Tumor Histology
Analytical and quantitative cytopathology and histopathology, v 37(5), pp 273-285
Oct 2015
PMID: 26856112
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
To develop a method whereby axillary lymph node (ALN) metastasis can be predicted without ALN dissection, via computational image analysis of routinely acquired tumor histology.
We employed digital image processing to stratify patients based on the histological attributes of the primary tumor. We extracted image features that capture the nuclear and architectural properties of the specimen. We then used a novel machine learning algorithm to transform image features into a scalar score that provided not only a metastasis prediction but also the certainty of classification.
We applied this procedure to 101 patients with a ground truth established by histological examination of the lymph nodes and found that 68.3% of the cohort could be classified, exhibiting a correct prediction rate of 88.4%.
These results demonstrate a technique that potentially can be used to supplant existing surgical methods to determine ALN metastasis status, thereby reducing patient morbidity associated with over-treatment.
Metrics
9 Record Views
Details
- Title
- Lymph Node Metastasis Status in Breast Carcinoma Can Be Predicted via Image Analysis of Tumor Histology
- Creators
- Mark D ZarellaDavid E BreenAlimoor RezaAladin MilutinovicFernando U Garcia
- Publication Details
- Analytical and quantitative cytopathology and histopathology, v 37(5), pp 273-285
- Publisher
- United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing); Pathology (and Laboratory Medicine)
- Web of Science ID
- WOS:000367870000001
- Scopus ID
- 2-s2.0-84947932962
- Other Identifier
- 991014878224304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Cell Biology