Interventional Optical Imaging Permits Instant Visualization of Pathological Zones of Ablated Tumor Periphery and Residual Tumor Detection
Bibliographic record
Abstract
Abstract Optical imaging (OI) provides real-time clinical imaging capability and simultaneous molecular, morphological, and functional information of disease processes. In this study, we present a new interventional OI technique, which enables in vivo visualization of three distinct pathologic zones of ablated tumor periphery for immediate detection of residual tumors during a radiofrequency ablation (RFA) session. Rabbits with orthotopic hepatic tumors were divided into two groups (n = 8/group): incomplete RFA and complete RFA. Indocyanine green-based interventional OI was used to differentiate three pathological zones: ablated tumor, transition margin, and residual tumor or surrounding normal liver—with quantitative comparison of signal-to-background ratios among the three zones and between incompletely and completely ablated tumors. Subsequent ex vivo OI and pathologic correlation were performed to confirm the findings of interventional OI. Interventional OI could differentiate incompletely or completely ablated tumor peripheries, thus permitting identification of residual tumor. This technique may open new avenues for immediate assessment of tumor eradication during a single interventional ablation session. Significance: Interventional optical imaging can instantly visualize pathologic zones of ablated tumor peripheries to detect residual tumors, which could revolutionize current image-guided interventional oncologic ablation techniques.
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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) | 0.000 | 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".