ReviewCancers2023
Systematic Review of Tumor Segmentation Strategies for Bone Metastases.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention and multi-scale learning.Quantitative imaging in medicine and surgery · 2025Article
- Microstructure of the human metastatic vertebral body.Frontiers in endocrinology · 2024Article
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposeTo investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions.
methodThe 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.
resultsThe 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.
conclusionDevelopment 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.