Related Experiment Video
Updated: Jun 18, 2026
Identifying, Diagnosing, and Grading Malignant Peripheral Nerve Sheath Tumors in Genetically Engineered Mouse Models
Published on: May 17, 2024
MRI-based prediction of malignancy in peripheral nerve tumors: model development and internal validation in a
Khaldun Ghali Gataa1,2, Katarzyna Dabrowska1,2, Muhamad Kader1,3
1University of Gothenburg Sahlgrenska Academy, Radiology, Västra Götaland County, Sweden, Gothenburg.
Purpose:
Differentiating malignant from benign peripheral nerve sheath tumors (PNSTs) is challenging due to overlapping imaging features. To develop and internally validate an MRI-based model to differentiate malignant from benign PNSTs and guide biopsy decisions.
Materials And Methods:
We retrospectively analyzed 139 consecutive adults with histologically proven PNSTs (121 benign, 18 malignant) discussed at a tertiary referral tumor board (2015-2024). Pre-treatment MRI examinations were independently assessed by two experienced musculoskeletal radiologists blinded to clinical and histopathological data. Based on prior knowledge and sample size limitations, a parsimonious logistic regression model was prespecified, including tumor volume, presence of a well-defined margin, and presence of cystic degeneration. Model performance was evaluated for discrimination, calibration, and clinical utility, with internal validation using 500 bootstrap resamples and uniform shrinkage.
Results:
Malignant tumors were substantially larger, less often well-circumscribed, and more frequently cystic than benign lesions. The final three-predictor model demonstrated good discrimination (area under the curve [AUC] 0.944; optimism-corrected 0.936). At a 20% biopsy threshold, the model would prevent 68 unnecessary biopsies per 100 patients while missing three malignancies. Findings were robust in sensitivity analyses.
Conclusion:
A simple MRI-based model using three available features - tumor volume, margin definition, and cystic change - distinguished malignant from benign PNSTs with high accuracy. This proof-of-concept provides a practical framework for biopsy decision-making and merits external validation in multicenter cohorts.
Key Points:
· A simple MRI-based model estimates malignancy risk in peripheral nerve tumors.. · The model combines tumor volume, margin irregularity, and cystic degeneration.. · An internally validated model may help prioritize biopsy decisions in specialized centers..
Citation Format:
· Ghali Gataa K, Dabrowska K, Kader M et al. MRI-based prediction of malignancy in peripheral nerve tumors: model development and internal validation in a biopsy-confirmed cohort. Rofo 2026; DOI 10.1055/a-2852-8884.
