In Vivo Evaluation of Gallium-68-Labeled IRDye800CW as a Necrosis Avid Contrast Agent in Solid Tumors
Bibliographic record
Abstract
Necrosis only occurs in pathological situations and is directly related to disease severity and, therefore, is an important biomarker. Tumor necrosis occurs in most solid tumors due to improperly functioning blood vessels that cannot keep up with the rapid growth, especially in aggressively growing tumors. The amount of necrosis per tumor volume is often correlated to rapid tumor proliferation and can be used as a diagnostic tool. Furthermore, efficient therapy against solid tumors will directly or indirectly lead to necrotic tumor cells, and detection of increased tumor necrosis can be an early marker for therapy efficacy. We propose the application of necrosis avid contrast agents to detect therapy-induced tumor necrosis. Herein, we advance gallium-68-labeled IRDye800CW, a near-infrared fluorescent dye that exhibits excellent necrosis avidity, as a potential PET tracer for in vivo imaging of tumor necrosis. We developed a reliable labeling procedure to prepare [68Ga]Ga-DOTA-PEG4-IRDye800CW ([68Ga]Ga-1) with a radiochemical purity of >96% (radio-HPLC). The prominent dead cell binding of fluorescence and radioactivity from [68Ga]Ga-1 was confirmed with dead and alive cultured 4T1-Luc2 cells. [68Ga]Ga-1 was injected in 4T1-Luc2 tumor-bearing mice, and specific fluorescence and PET signal were observed in the spontaneously developing tumor necrosis. The ip injection of D-luciferin enabled simultaneous bioluminescence imaging of the viable tumor regions. Tumor necrosis binding was confirmed ex vivo by colocalization of fluorescence uptake with TUNEL dead cell staining and radioactivity uptake in dichotomized tumors and frozen tumor sections. Our presented study shows that [68Ga]Ga-1 is a promising PET tracer for the detection of tumor necrosis.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".