Abstract:
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 overtreatment.
