Systematic Review of Tumor Segmentation Strategies for Bone Metastases

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.

Keywords: bone metastases; computational approaches; deep learning; machine learning; malignant lesions; radiation therapy.

Figure 1

Inclusion and exclusion of articles for the review.

Figure 2

Analysis of characteristics of included articles. (a) Distribution of articles according to the image modality; (b) Distribution of articles according to the method; (c) Distribution of articles over years; (d) Distribution of evaluation metrics; (e) Countries of first authors; (f) Distribution of cancer type.