Noninvasive Visualization of Tumoral Fibrin Deposition Using a Peptidic Fibrin-Binding Single Photon Emission Computed Tomography Tracer
Bibliographic record
Abstract
Fibrin deposition plays an important role in the formation of mature tumor stroma and provides a facilitating scaffold for tumor angiogenesis. This study investigates the potential of the (111)In-labeled fibrin-binding peptide EPep for SPECT imaging of intratumoral fibrin deposition. (111)In-EPep and negative control (111)In-NCEPep were synthesized and characterized in vitro. In vivo SPECT images and ex vivo biodistribution profiles and autoradiographs were obtained in a fibrin-rich BT-20 breast cancer mouse model. Furthermore, biodistribution profiles were obtained in the fibrin-poor MDA-MD-231 model. In vitro, (111)In-EPep displayed significantly more binding than (111)In-NCEPep toward human and mouse derived fibrin. SPECT/CT images displayed a marked SPECT signal in the tumor area for BT-20 tumor bearing mice injected with EPep but not for mice injected with NCEPep. Biodistribution profiles of BT-20 tumor bearing mice 3 h post-tracer injection showed significantly higher tumor uptake for EPep with respect to NCEPep (0.39 ± 0.14 and 0.11 ± 0.03% ID g(-1), respectively), whereas uptake in other organs was similar for EPep and NCEPep. Autoradiography of BT-20 tumor sections displayed a high signal for EPep which colocalized with intratumoral fibrin deposits. Histological evaluation of MDA-MB-231 tumor sections displayed no significant tumor stroma and only minute fibrin deposits. Biodistribution profiles in MDA-MB-231 tumor bearing mice 3 h post-injection showed EPep tumor uptake (0.14 ± 0.04% ID g(-1)) which was significantly lower with respect to EPep BT-20 tumor uptake, indicating fibrin-specificity of EPep tumoral uptake. In conclusion, this work demonstrates the potential of EPep SPECT imaging for visualization of tumoral fibrin deposition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".