Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
This work addresses a domain-specific problem for clinical oncology by providing a novel method to mine prognostic biomarkers from tumor boundaries, though it is incremental in applying sequential learning techniques to this task.
The paper tackled the problem of analyzing liver tumor boundary semantics to predict microvascular invasion (MVI) by proposing a computational framework that localizes boundary vertices and classifies semantics, demonstrating effectiveness in experiments and feasibility as a prognostic biomarker.
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tumor boundary has proven to be clinically correlated with the prognostic indicator, microvascular invasion (MVI). Investigating tumor boundary semantics has tremendous clinical values. In this paper, we propose the first and novel computational framework that disentangles the task into two components: spatial vertex localization and sequential semantic classification. (1) A HCC tumor segmentor is built for tumor mask boundary extraction, followed by polar transform representing the boundary with radius and angle. Vertex generator is used to produce fixed-length boundary vertices where vertex features are sampled on the corresponding spatial locations. (2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification. Extensive experiments on tumor capsule semantics demonstrate the effectiveness of our framework. Mining the correlation between the boundary semantics and MVI status proves the feasibility to integrate this boundary semantics as a valid HCC prognostic biomarker.