Highly Accurate Detection of Cancer <i>In Situ</i> with Intraoperative, Label-Free, Multimodal Optical Spectroscopy
Bibliographic record
Abstract
Abstract Effectiveness of surgery as a cancer treatment is reduced when all cancer cells are not detected during surgery, leading to recurrences that negatively impact survival. To maximize cancer cell detection during cancer surgery, we designed an in situ intraoperative, label-free, optical cancer detection system that combines intrinsic fluorescence spectroscopy, diffuse reflectance spectroscopy, and Raman spectroscopy. Using this multimodal optical cancer detection system, we found that brain, lung, colon, and skin cancers could be detected in situ during surgery with an accuracy, sensitivity, and specificity of 97%, 100%, and 93%, respectively. This highly sensitive optical molecular imaging approach can profoundly impact a wide range of surgical and noninvasive interventional oncology procedures by improving cancer detection capabilities, thereby reducing cancer burden and improving survival and quality of life. Cancer Res; 77(14); 3942–50. ©2017 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".