Preoperative Ultrasound‐Guided Incisional Biopsy Enhances the Pathological Accuracy of Incisional Biopsy of Cutaneous Melanoma
Bibliographic record
Abstract
OBJECTIVES: To assess the feasibility of preoperative ultrasound (US)-guided incisional biopsy through a prospective controlled clinical trial. METHODS: This was a prospective, double-arm, single-center study of Chinese patients. Thirty patients were enrolled in the study. Fourteen patients received incisional biopsies for which the choice of biopsy area relied on a clinical evaluation, and 16 patients received incisional biopsies for which the choice of biopsy area relied on a US-guided evaluation. The following procedure was used in the US-guided incisional biopsy group: 1) clinical and dermoscopic evaluation of skin lesions; 2) US examination; 3) incisional biopsy; 4) surgical excision; and 5) histopathological examination. The same procedure was used in the non-US-guided group except without US examination. RESULTS: In the non-US-guided group, the mean tumor thicknesses obtained from incisional biopsy and postoperative histopathological examination were 2.1 and 4.1 mm, respectively. Seven melanomas were underestimated by incisional biopsy, resulting in margins narrower than currently recommended. In the US-guided group, the mean tumor thicknesses obtained from US, incisional biopsy, and postoperative histopathological examination were 3.4, 2.9, and 2.7 mm, respectively. In only 3 melanomas was the tumor thickness of the incisional biopsy less than that of the postoperative histopathological examination, demonstrating that US-guided biopsy obtains the maximum thickness area. CONCLUSIONS: Preoperative US-guided incisional biopsy can enhance the pathological accuracy of incisional biopsy, which may allow us to better perform surgical excision with safe peripheral surgical margins.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".