Correlation between imaging and histology in benign solitary retroperitoneal nerve sheath tumors: a pictorial review
Bibliographic record
Abstract
BACKGROUND: Benign nerve sheath tumors presenting as solitary retroperitoneal masses (RBNSTs) pose a complex diagnostic challenge for multidisciplinary teams regarding differential diagnosis, staging, and treatment planning. This article reviews the role played by different imaging techniques in assessing RBNSTs and elucidates their typical pathological features with a particular emphasis on the correlation between imaging and histological findings. Furthermore, some examples of retroperitoneal tumors that merit consideration in the process of differential diagnosis based on cross-sectional investigations (CSIs) are reported. The correlation between tissue architecture and appearance on imaging can help increase the accuracy of differential diagnosis with other retroperitoneal neoplasms at CSIs. CRITICAL RELEVANCE STATEMENT: This educational review critically examines the correlation between imaging and histological features in solitary retroperitoneal benign nerve sheath tumors, offering valuable insights for improving the accuracy of differential diagnosis in clinical radiology. KEY POINTS: RBNSTs are challenging to diagnose because they lack specific radiological features. Differential diagnosis of RBNSTs from other retroperitoneal neoplasms on imaging is complex. Surgical removal of RBNSTs is recommended for an accurate diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".