Myxoid Leiomyosarcoma of the Uterus: A Case Report With Magnetic Resonance Imaging Findings
Bibliographic record
Abstract
Myxoid leiomyosarcoma (M-LMS) of the uterus is extremely rare and its diagnosis is challenging. We report a case of the M-LMS in a 69-year-old female who referred to our hospital for abdominal discomfort and increased uterine mass lesion. Magnetic resonance imaging (MRI) demonstrated a well-defined intramural mass (approximately 8 cm in diameter) that exhibited isointensity to myometrium on T1-weighted images, and markedly high heterogeneous intensity and flow-void area suggestive of abundant blood flow on T2-weighted images. On diffusion weighted imaging, the major portion the mass showed high intensity, but it was considered to be T2 shine-through effect, because the mean apparent diffusion coefficient (ADC) value of the lesion was relatively high. Thus, we could not make the diagnosis of malignancy. However, considering the increase tendency of the mass at the postmenopausal status, the possibility of malignancy could not be ruled out, so a total abdominal hysterectomy with bilateral salpingo-oophorectomy was performed. The result of pathologic assessment is M-LMS. A diagnosis of M-LMS is difficult preoperatively. In the case that myxoid tumor is suspected, although malignancy is not definite, we might have to consider the possibility of malignancy from the age of the patient and the tendency of the lesion to increase. J Clin Gynecol Obstet. 2021;10(1):18-21 doi: https://doi.org/10.14740/jcgo675
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 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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| 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".