Abstract 4316: A novel wide field-of-view imaging device for real-time, intra-operative tumor bed assessment
Bibliographic record
Abstract
Abstract Limb-sparing surgery for extremity soft tissue sarcoma removes all cancer cells at the primary tumor site in the majority of patients. However, without radiation therapy, microscopic residual sarcoma cells left behind in the tumor bed will cause a tumor recurrence in approximately one-third of patients. Therefore, adjuvant radiation therapy is delivered to most patients, even when the tumor bed lacks residual cancer. Here, we present an imaging system for residual cancer assessment, consisting of a novel wide field-of-view imaging device and a protease-activated fluorescent probe. We demonstrate that this system directly images microscopic residual sarcoma cells in the tumor bed of mice when primary soft tissue sarcomas are resected. Moreover, this system can detect single tumor cells that have activated the fluorescent probe in vivo. This technology has the potential to be used as an intra-operative tool to identify microscopic residual disease for soft tissue sarcoma and other cancers with the goal of reducing rates of local recurrence, re-operation for positive margins, and adjuvant radiation therapy to tumor beds that lack residual cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4316.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".