MR Imaging–guided Focused US Ablation of Breast Cancer: Histopathologic Assessment of Effectiveness—Initial Experience
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of noninvasive magnetic resonance (MR) imaging-guided focused ultrasonographic (US) ablation of breast carcinomas. MATERIALS AND METHODS: Before undergoing tumor resection, 12 patients with invasive breast carcinomas were treated with MR imaging-guided focused US ablation consisting of multiple sonications of targeted points that were monitored with temperature-sensitive MR imaging. The patients were treated with either one of two focused US systems. The effectiveness of the treatment was determined at histopathologic analysis of the resected mass that was performed to determine the volumes of necrosed and residual tumor. Complications resulting from the procedure were assessed by means of questionnaires, medical examinations, and MR image analysis. RESULTS: US ablation was well tolerated by the patients, and with the exception of minor skin burns in two patients, no complications occurred. Histopathologic analysis of resected tumor sections enabled quantification of the amount of necrosed and residual tumor and visualization of the surrounding hemorrhage. In three patients treated with one of the US systems, a mean of 46.7% of the tumor was within the targeted zone and a mean of 43.3% of the cancer tissue was necrosed. In nine patients treated with the other US system, a mean of 95.6% of the tumor was within the targeted zone and a mean of 88.3% of the cancer tissue was necrosed. Residual tumor was identified predominantly at the periphery of the tumor mass; this indicated the need to increase the total targeted area (ie, with an increased number of sonications). CONCLUSION: Thermal coagulation of small breast tumors by means of MR imaging-guided focused US appears to be a promising noninvasive ablation procedure.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".