Systematic Review of Tumor Segmentation Strategies for Bone Metastases

SIMPLE SUMMARY: With recent progress in radiation therapy, patients with bone metastases can be treated curatively, provided precise delineation of metastatic lesions is adequately identified. Tumor segmentation is a highly active area of research, but only limited studies have been on bone metastasis. This review aims to investigate methods for differentiating benign from malignant bone lesions and characterizing malignant bone lesions specifically in the context of bone metastases. While computer vision techniques have opened new opportunities for quantifying cancer growth with minimal expert supervision, fully automatic segmentation algorithms still require improvement. This is partly due to limited contrast between tumors and surrounding tissue and the lack of a widely agreed upon “gold standard” for defining these boundaries. Additionally, many studies do not provide evidence that their proposed methods are suitable for use in clinical practice. ABSTRACT: Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.