Sample-Specific Learning of Lymphovascular Invasion with Heterogeneous Spatial Patterns

Lymphovascular invasion ((Formula presented.)) represents one of the earliest stages of metastasis. This article is motivated by using the multiplex immunofluorescence imaging data for identification of (Formula presented.) that can improve diagnosis of early breast cancer prior to metastasis. One unique aspect for this type of data is that individual-level imaging are taken at various locations depending on where the biopsy was located on the breast. Thus, there exists substantial spatial heterogeneity between the images. We present a novel sample-specific learning framework for (Formula presented.) -penalized logistic regression to aid accurate (Formula presented.) classification in real time. We derive finite sample guarantees for our proposed estimator in this article and present a computationally efficient algorithm for translation. The finite sample performance for the proposed method is assessed via intensive simulation studies. Using images generated from the Stanford cohort, we rigorously assess the (Formula presented.) classification performance with both internal and external validation and demonstrate high concordance with pathologist labeling.