ArticleEBioMedicine2018
Survival Prediction in High-grade Osteosarcoma Using Radiomics of Diagnostic Computed Tomography.
Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 3 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.European radiology · 2021Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Radiomics model for preoperative prediction of 3-year survival-based CT image biomarkers in esophageal cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022Trial
- Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Trial
- Learning discrete structures for cancer radiomics.APL bioengineering · 2025Article
- Article
- Predictive modeling of obstructive sleep apnea using pharyngeal magnetic resonance imaging radiomics and clinical data.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Eye movement parameters as biomarkers for diagnosis and levodopa responsiveness in patients with Parkinson's disease.Aging clinical and experimental research · 2025Article
- MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas From Benign Soft Tissue Musculoskeletal Myxomas.Journal of magnetic resonance imaging : JMRI · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance.Bioengineering (Basel, Switzerland) · 2025Article
- Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept.Scientific reports · 2025Article
- Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.Cardiovascular and interventional radiology · 2025Article
- Radiographic signature in apical periodontitis improves prediction of apical lesion healing through survival prediction model.PloS one · 2025Article
- Osteopontin, WNT3A, and ABCB5 Biomarkers Expression in Osteosarcoma Patients.Revista brasileira de ortopedia · 2024Article
- Novel methods to diagnose rotator cuff tear and predict post-operative Re-tear: Radiomics models.Asia-Pacific journal of sports medicine, arthroscopy, rehabilitation and technology · 2024Article
- Detecting High-Dose Methotrexate-Induced Brain Changes in Pediatric and Young Adult Cancer Survivors Using [Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma.Journal of translational medicine · 2024Article
5 more citing papers are in PubMed but not listed here.
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16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The poor 5-year survival rate in high-grade osteosarcoma (HOS) has not been increased significantly over the past 30 years. This work aimed to develop a radiomics nomogram for survival prediction at the time of diagnosis in HOS. In this retrospective study, an initial cohort of 102 HOS patients, diagnosed from January 2008 to March 2011, was used as the training cohort. Radiomics features were extracted from the pretreatment diagnostic computed tomography images. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability by using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed by using clinical factors only. The models were validated in an independent cohort comprising 48 patients diagnosed from April 2011 to April 2012. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Kaplan-Meier survival analysis was performed. The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.86 vs. 0.79 for the training cohort, and 0.84 vs. 0.73 for the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p-value <.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups. The radiomics nomogram may assist clinicians in tailoring appropriate therapy.
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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.