Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images

• 成果类型: Article
• 署名作者: Zhang, Cong; Bedi, Tejasv; Moon, Chul; Xie, Yang; Chen, Min; Li, Qiwei
• 署名单位: University of Texas System; University of Texas Dallas; Southern Methodist University; University of Texas System; University of Texas Southwestern Medical Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2298031
• 发表日期: 2024
• 页码: 798-810
• 关键词: cancer; Heterogeneity

摘要

Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this article, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value <0.001). Supplementary materials for this article are available online.