ArticleSkeletal radiology2026
A proposed radiological model for preoperative chemotherapy response prediction in patients with skeletal Ewing sarcoma.
Article in Skeletal radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a predictive model for estimating the histological response to preoperative chemotherapy based on imaging data in patients with Ewing sarcoma. MATERIALS AND
methodsWe included 133 patients with Enneking stage IIB or IIIB Ewing sarcoma who underwent chemotherapy and definitive surgery between 2003 and 2020. We analyzed various radiological parameters before and after preoperative chemotherapy. The necrotic area was evaluated using gadolinium-contrasted magnetic resonance imaging (radiological necrotic grade). Patients were classified as good histological responders if > 95% of their resected specimens showed necrosis; otherwise, they were classified as poor responders. Radiological parameters were assessed using the least absolute shrinkage and selection operator (LASSO) with cross-validation. Optimal regularization parameters were identified as those minimizing cross-validation error. The area under the curve (AUC) was calculated based on the predictive model with the selected parameters for training and test data using receiver operating characteristic (ROC) curve.
resultsLASSO models identified key parameters including volume change, radiological necrotic grade, complete regression of the extraskeletal component, and the disappearance of peritumoral gadolinium-enhancement after preoperative chemotherapy. ROC curve analysis showed that the predictive model achieved measurable discrimination ability on both training and test datasets (AUC = 0.89 [95% confidence interval (95%CI); 0.83-0.95] on training data, 0.77 [95%CI; 0.58-0.95] on test data).
conclusionThe developed model may facilitate accurate monitoring of the efficacy of preoperative chemotherapy in patients with Ewing sarcoma. Identifying patients with a poor histological response to preoperative chemotherapy can aid in the planning of secure surgical margins and effective treatment strategies.
Indexed as
Identifiers
41076496What 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.