Smooth Muscle Tumors of the Uterus at MRI: Focus on Leiomyomas and FIGO Classification
Bibliographic record
Abstract
Leiomyomas are smooth muscle tumors of the uterus and are the most common uterine neoplasm. Although leiomyomas are usually asymptomatic, they can manifest with symptoms such as pain or uterine bleeding. Leiomyomas are classified on the basis of their anatomic location and morphology. Localization of leiomyomas relative to the endometrium, myometrium, and uterine serosa with use of the International Federation of Gynecology and Obstetrics (FIGO) classification system is helpful for guiding management in symptomatic patients. The FIGO system is a practical and universally accepted approach for classifying leiomyomas to guide radiologists and clinicians in deciding management. The MRI appearance of conventional leiomyomas is related to their tissue contents of smooth muscle and fibrous tissue and is well established. The MRI features of some leiomyoma subtypes and forms of degeneration also have been described. Other smooth muscle tumors of the uterus recognized in the 2020 World Health Organization classification system include intravenous leiomyomatosis, smooth muscle tumors of uncertain malignant potential, and metastasizing leiomyoma. At the far end of the spectrum are leiomyosarcomas, which are frankly malignant and therefore must be managed accordingly. Although MRI features that suggest a diagnosis of leiomyosarcoma have been proposed, these features overlap with those of some leiomyoma subtypes and degeneration. © RSNA, 2023 See the invited commentary by Fennessy and Gargiulo in this issue. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.
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 | 0.000 | 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".