Soft tissue vascular tumor-like lesions in adults: imaging and pathological analysis pitfalls per ISSVA classification
Bibliographic record
Abstract
OBJECTIVES: To compare the magnetic resonance imaging (MRI) and Doppler ultrasound (DUS) findings with the pathological findings of soft tissue vascular tumors (STVTs) according to the 2018 ISSVA (International Society for the Study of Vascular Anomalies) classification to differentiate vascular tumors from vascular malformations. METHODS: This retrospective study included patients with STVTs who underwent contrast-enhanced MRI and pathological analysis at our hospital between 2010 and 2020. The presumptive diagnosis based on the on-site imaging and histological analysis was compared with imaging and histological analysis conducted off-site utilizing the ISSVA criteria. RESULTS: This study included 31 patients with 31 vascular tumors located in the head and neck (n = 3), trunk (n = 2), and extremities (n = 26). The off-site pathological analysis confirmed benign vascular tumors in 54.8% of cases (non-involuting congenital hemangioma: 35.5%; epithelioid hemangioma: 13%; pyogenic granuloma: 3%; and spindle cell hemangioma: 3%). Based on the off-site histological analysis, 25.8% were reclassified as having a vascular malformation whereas three had other benign lesions. Only phleboliths were associated with a vascular malformation (p = 0.03). The concordance between off-site MRI and pathological findings was fair (k = 0.3902 (0.0531-0.7274)), whereas that between on-site and off-site pathological analyses was poor (k = -0.0949 (-0.4661 to 0.2763)). CONCLUSION: Benign vascular tumors have non-specific imaging features on imaging with some overlap with atypical vascular malformations. Therefore, histological analysis is recommended. Imaging and pathological analyses should be performed in accordance with the ISSVA classification to minimize inter-observer discrepancies. CRITICAL RELEVANCE STATEMENT: Imaging features of benign vascular tumors on MRI are non-specific, leading to discrepancies with pathological findings and potential overlap with atypical vascular malformations. Imaging and histological analyses should be performed in accordance with ISSVA guidelines to improve patient management. KEY POINTS: The imaging features of benign vascular tumors are non-specific. Histological analysis is recommended for soft tissue vascular tumors in adults. Analyses of soft tissue vascular tumors should be performed in accordance with ISSVA guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".