SynthesisPloS one2026
Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.
Synthesis in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary malignant bone tumours of the skeleton have a great diversity in their biological behaviour, and the most common in adolescence is osteosarcoma for which the diagnosis and therapeutic management are both a challenge. The use of machine and deep learning in image analysis for MRI, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) to diagnose osteosarcoma has shown major improvements recently. This systematic review is performed according to PRISMA 2020 guidelines, and it comprises a critical analysis of 38 peer-reviewed articles from 2020 to 2025, which address the various aspects of tumour segmentation, classification, and prognosis. The methodological mapping revealed seven main categories that included numerous examples of radiomics (n = 8) and convolutional neural networks (CNNs, n = 6), as well as vision transformers and multimodal architectures for emerging technologies (n = 5 each). A quantitative evaluation of the segmentation performance showed that Multimodal achieved the best spatial overlap with an average Dice Similarity Coefficient (DSC) of 94.7% (range: 94.5-94.9%), while CNN of standard size was comparable, Lightweight networks had a DSC between 91.4 and 99.4% with an average of 91.9% and Vision Transformers a range of 90.5-94.9% with an average of 92.1%. By contrast, the performance gap among the Attention Mechanisms was greatest with a mean of 87.67% (64.6-98.5%), while the lowest was the mean 85.7% (81.6-89.8%) for Unsupervised Clustering. In addition to segmentation, the clinical nomograms based on radiomics also showed excellent prognostic potential:The AUC values for predicting pathological necrosis after chemotherapy varied from 0.807 to 0.848. More importantly it was found that there were significant translational gaps because all 38 models were limited to retrospective, single centre cohorts, with evaluation of risk of bias. Advanced algorithms perform well with localized precision but face a number of challenges with robust multicenter testing and a public repository.
Indexed as
Identifiers
What Socratic 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 Socratic graph.