Regulatory Aspects of Optical Methods and Exogenous Targets for Cancer Detection
Bibliographic record
Abstract
Abstract Considerable advances in cancer-specific optical imaging have improved the precision of tumor resection. In comparison to traditional imaging modalities, this technology is unique in its ability to provide real-time feedback to the operating surgeon. Given the significant clinical implications of optical imaging, there is an urgent need to standardize surgical navigation tools and contrast agents to facilitate swift regulatory approval. Because fluorescence-enhanced surgery requires a combination of both device and drug, each may be developed in conjunction, or separately, which are important considerations in the approval process. This report is the result of a one-day meeting held on May 4, 2016 with officials from the National Cancer Institute, the FDA, members of the American Society of Image-Guided Surgery, and members of the World Molecular Imaging Society, which discussed consensus methods for FDA-directed human testing and approval of investigational optical imaging devices as well as contrast agents for surgical applications. The goal of this workshop was to discuss FDA approval requirements and the expectations for approval of these novel drugs and devices, packaged separately or in combination, within the context of optical surgical navigation. In addition, the workshop acted to provide clarity to the research community on data collection and trial design. Reported here are the specific discussion items and recommendations from this critical and timely meeting. Cancer Res; 77(9); 2197–206. ©2017 AACR.
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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 | 0.043 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".