ArticleBMC medical imaging2023
Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Prevalence and risk factors of distant metastasis among Chinese osteosarcoma patients: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2026Pooled it
- Risk factors for pulmonary metastases in osteosarcoma: a systematic review and meta-analysis.BMC cancer · 2026Pooled it
- Tumor markers and multimodal magnetic resonance imaging in predicting rectal cancer stage and differentiation.World journal of gastrointestinal oncology · 2025Article
- Predicting lung metastasis in high-grade Osteosarcoma: The role of CTA Signs, with a Focus on vascular wrapping and intratumoral vascular network.Journal of bone oncology · 2025Article
- T2-weighted MRI radiomics for the prediction of pediatric and young adult rhabdomyosarcoma alveolar subtype and distant metastasis: a pilot study.Pediatric radiology · 2025Article
- X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head.Scientific reports · 2025Article
- Nanoparticles and bone microenvironment: a comprehensive review for malignant bone tumor diagnosis and treatment.Molecular cancer · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesOsteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years.
methods486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.
resultsThe radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (t
conclusionThe clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.
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.