Implantable marker to facilitate use of hypofractionated radiation in early breast cancer.
Bibliographic record
Abstract
38 Background: Shorter courses of radiation therapy have advantages for patients and could result in significant cost savings for the healthcare system. The British and Canadian experience with hypo-fractionated regimens have shown that survival rates in appropriately selected patients is equivalent to other methods. However, adoption of these techniques has been hindered by fear of potential complications such as poor cosmetic outcomes. By improving targeting of the surgical tumor bed, an increased use of "field in field" planning helps to decrease "hot spots" commonly related to toxicities. This could potentially assist with increased use of hypo-fractionated regimens in a reliable, safe and cost-effective manner. To determine the ability of a new surgical implant marker to facilitate use of hypofractionated radiation delivery, we prospectively studied the radiation regimens delivered to 100 patients implanted with the device in our community-based practice. Methods: Over a 36 month period, 110 volumetric tissue markers were surgically implanted at the tumor bed excision site in 109 patients during partial mastectomy (PM). Routine CT imaging of the breast was performed for treatment planning and the marker was rated for visibility and its utility in delineating the target region. Radiation treatment regimens were recorded and reported. A cost analysis was also performed. Results: In all patients, the marker was easily visible and in 95.7% of cases, it was found to be useful during radiation treatment planning for RT target delineation. 36.8% of patients received conventional full-course whole breast irradiation plus boost, 56.6% received hypo-fractionation plus boost, and 4.3% received accelerated partial breast irradiation. With routine use of the implant, there was a marked increase in the use of hypo-fractionated regimens, resulting in a 25% cost savings per patient. Conclusions: This 3-dimensional implant was easily visible and particularly useful in targeting the tumor bed when implanted during PM. With routine use of the device we observed an unanticipated increased use of accelerated radiation protocols, a net cost savings in patients' treatment and a significant improvement in cosmetic outcomes.
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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 | 0.000 | 0.002 |
| 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".