Rare Mesenchymal Tumors of the Pelvis: Imaging and Pathologic Correlation
Bibliographic record
Abstract
Most pelvic tumors originate from the organs. Less commonly, tumors can arise from the various anatomic pelvic compartments and are comprised of mesenchymal tissue: muscles, connective tissue, vessels, lymphatics, and fat. Among some of the rarer entities are benign tumors (eg, angiomyxoma, cellular angiofibroma, and desmoid fibromatosis), malignant tumors (eg, sarcoma), and tumors that can manifest as benign or malignant (eg, solitary fibrous tumor or nerve sheath tumor). Because these tumors are uncommon and often manifest with nonspecific clinical features, imaging (usually MRI) is an initial step in the evaluation. Radiologists interpreting these images are asked to help narrow the differential diagnosis and assess the likelihood of malignancy for treatment planning. Thus, the MRI report should include the imaging features that would indicate the underlying tissue histology for pathologic diagnosis as well as a description of the anatomic extent and pattern of growth. The authors describe multiple locally aggressive benign and malignant mesenchymal tumors and highlight characteristic clinical and imaging features that enable the radiologist to narrow the differential diagnosis. The anatomic spaces of the pelvis are reviewed with illustrations to aid the radiologist in describing these tumors, which often span multiple pelvic compartments. Tumor appearance at T2-weighted, diffusion-weighted, and postcontrast MRI is summarized and illustrated with correlation at CT or fluorodeoxyglucose PET/CT, when available. MRI features that correspond to specific types of tissue (eg, myxoid, fibrous, or vascular) are highlighted and correlated with images from pathologic evaluation. Online supplemental material is available for this article. ©RSNA, 2021
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".