OBJECTIVE: 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. STUDY DESIGN: 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. RESULTS: 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%. CONCLUSION: 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.
OBJECTIVE: 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. STUDY DESIGN: 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. RESULTS: 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%. CONCLUSION: 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.
Authors: Famke Aeffner; Mark D Zarella; Nathan Buchbinder; Marilyn M Bui; Matthew R Goodman; Douglas J Hartman; Giovanni M Lujan; Mariam A Molani; Anil V Parwani; Kate Lillard; Oliver C Turner; Venkata N P Vemuri; Ana G Yuil-Valdes; Douglas Bowman Journal: J Pathol Inform Date: 2019-03-08
Authors: Kelly Yi Ping Liu; Sarah Yuqi Zhu; Alan Harrison; Zhao Yang Chen; Martial Guillaud; Catherine F Poh Journal: PLoS One Date: 2021-11-04 Impact factor: 3.240