Pre-clinical Evaluation of a Cyanine-Based SPECT Probe for Multimodal Tumor Necrosis Imaging
Bibliographic record
Abstract
Recently we showed that a number of carboxylated near-infrared fluorescent (NIRF) cyanine dyes possess strong necrosis avid properties in vitro as well as in different mouse models of spontaneous and therapy-induced tumor necrosis, indicating their potential use for cancer diagnostic- and prognostic purposes. In the previous study, the detection of the cyanines was achieved by whole body optical imaging, a technique that, due to the limited penetration of near-infrared light, is not suitable for investigations deeper than 1 cm within the human body. Therefore, in order to facilitate clinical translation, the purpose of the present study was to generate a necrosis avid cyanine-based NIRF probe that could also be used for single photon emission computed tomography (SPECT). For this, the necrosis avid NIRF cyanine HQ4 was radiolabeled with 111 indium, via the chelate diethylene triamine pentaacetic acid (DTPA). The necrosis avid properties of the radiotracer [ 111 In]DTPA-HQ4 were examined in vitro and in vivo in different breast tumor models in mice using SPECT and optical imaging. Moreover, biodistribution studies were performed to examine the pharmacokinetics of the probe in vivo . Using optical imaging and radioactivity measurements, in vitro , we showed selective accumulation of [ 111 In]DTPA-HQ4 in dead cells. Using SPECT and in biodistribution studies, the necrosis avidity of the radiotracer was confirmed in a 4T1 mouse breast cancer model of spontaneous tumor necrosis and in a MCF-7 human breast cancer model of chemotherapy-induced tumor necrosis. The radiotracer [ 111 In]DTPA-HQ4 possessed strong and selective necrosis avidity in vitro and in various mouse models of tumor necrosis in vivo , indicating its potential to be clinically applied for diagnostic purposes and to monitor anti-cancer treatment efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".