ArticleSkeletal radiology2024
Evaluation of response to neoadjuvant chemotherapy in osteosarcoma using dynamic contrast-enhanced MRI: development and external validation of a model.
Article in Skeletal radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled it
- Minimal residual disease and relapse surveillance in osteosarcoma: an action-linked framework integrating liquid biopsy and imaging biomarkers.Journal of bone oncology · 2026Review
- Evaluating the Impact of Habitat Analysis via IVIM and DCE-MRI on Neoadjuvant Chemotherapy Outcomes in Osteosarcoma Patients: A Preliminary Study.Journal of imaging informatics in medicine · 2026Article
- Non-invasive MRI biomarkers for assessment of neoadjuvant chemotherapy response in osteosarcoma: Current techniques and clinical perspectives.Journal of bone oncology · 2026Review
- Exploratory quantitative magnetic resonance imaging characterization of histopathologic subtype heterogeneity in osteosarcoma.Polish journal of radiology · 2026Article
- Review
- Iron oxide nanoparticles coated with bioactive materials: a viable theragnostic strategy to improve osteosarcoma treatment.Discover nano · 2025Review
- Prediction of tumor response to neoadjuvant chemotherapy in high-grade osteosarcoma using clustering-based analysis of magnetic resonance imaging: an exploratory study.La Radiologia medica · 2025Article
- Imaging of Osteosarcoma: Presenting Findings, Metastatic Patterns, and Features Related to Prognosis.Journal of clinical medicine · 2024Review
- Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.European journal of nuclear medicine and molecular imaging · 2024Review
- Article
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Authors and funding
15 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveTo identify which dynamic contrast-enhanced (DCE-)MRI features best predict histological response to neoadjuvant chemotherapy in patients with an osteosarcoma.
methodsPatients with osteosarcoma who underwent DCE-MRI before and after neoadjuvant chemotherapy prior to resection were retrospectively included at two different centers. Data from the center with the larger cohort (training cohort) was used to identify which method for region-of-interest selection (whole slab or focal area method) and which change in DCE-MRI features (time to enhancement, wash-in rate, maximum relative enhancement and area under the curve) gave the most accurate prediction of histological response. Models were created using logistic regression and cross-validated. The most accurate model was then externally validated using data from the other center (test cohort).
resultsFifty-five (27 poor response) and 30 (19 poor response) patients were included in training and test cohorts, respectively. Intraclass correlation coefficient of relative DCE-MRI features ranged 0.81-0.97 with the whole slab and 0.57-0.85 with the focal area segmentation method. Poor histological response was best predicted with the whole slab segmentation method using a single feature threshold, relative wash-in rate <2.3. Mean accuracy was 0.85 (95%CI: 0.75-0.95), and area under the receiver operating characteristic curve (AUC-index) was 0.93 (95%CI: 0.86-1.00). In external validation, accuracy and AUC-index were 0.80 and 0.80.
conclusionIn this study, a relative wash-in rate of <2.3 determined with the whole slab segmentation method predicted histological response to neoadjuvant chemotherapy in osteosarcoma. Consistent performance was observed in an external test cohort.
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