Development of a liquid biopsy test for the pre-operative diagnosis of uterine leiomyomas and leiomyosarcomas
Bibliographic record
Abstract
Leiomyomas (LM), also called fibroids, are common benign tumours originating from myometrium, often presenting with symptoms such as pelvic pain and abnormal bleeding. Their clinical presentation can closely resemble that of leiomyosarcomas (LMS), a rare but aggressive uterine malignancy. Accurate preoperative distinction between LM and LMS remains a significant clinical challenge, as uterine lesions are rarely biopsied before surgery. This can lead to inappropriate surgical procedures that may inadvertently disseminate malignant cells and delay timely oncological treatment.We hypothesize that tumour type-specific genomic and epigenomic markers detectable in circulating tumour DNA (ctDNA), alongside circulating protein markers can be used to distinguish LM from LMS in a non-invasive manner. Our objectives are to demonstrate the feasibility of concurrent detection of tumour-type specific genomic and epigenomic markers in plasma ctDNA of patients with LM and LMS and to evaluate the diagnostic potential of circulating protein biomarkers for preoperative distinction between LM and LMS. By exploring a liquid biopsy approach, this research aims to improve the accuracy of preoperative diagnosis, reduce the risk of malignancy dissemination due to inappropriate surgical management, and ultimately support more effective and timely clinical decision-making in patients presenting with uterine masses
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".