The Necrosis-Avid Small Molecule HQ4-DTPA as a Multimodal Imaging Agent for Monitoring Radiation Therapy-Induced Tumor Cell Death
Bibliographic record
Abstract
Purpose: Most effective antitumor therapies induce tumor cell death. Non-invasive, rapid and accurate quantitative imaging of cell death is essential for monitoring early response to antitumor therapies. To facilitate this, we previously developed a biocompatible necrosis-avid near-infrared fluorescence (NIRF) imaging probe, HQ4, which was radiolabelled with 111Indium-chloride (111In-Cl3) via the chelate diethylene triamine pentaacetic acid (DTPA), to enable clinical translation. The aim of the present study was to evaluate the application of HQ4-DTPA for monitoring tumor cell death induced by radiation therapy. Apart from its NIRF and radioactive properties, HQ4-DTPA was also tested as a photoacoustic imaging probe to evaluate its performance as a multimodal contrast agent for superficial and deep tissue imaging. Material/Methods: Radiation-induced tumor cell death was examined in a xenograft mouse model of human breast cancer (MCF7). Tumors were irradiated with three fractions of 9 Gy each. HQ4-DTPA was injected intravenously after the last irradiation, NIRF and photoacoustic imaging of the tumors were performed at 12, 20 and 40 h after injection. Changes in probe accumulation in the tumors were measured in vivo, and ex vivo histological analysis of excised tumors was performed at experimental endpoints. In addition, biodistribution of radiolabeled [111In]DTPA-HQ4 was assessed using hybrid single-photon emission computed tomography-computed tomography (SPECT-CT) at the same time points. Results: In vivo NIRF imaging demonstrated a significant difference in probe accumulation between control and irradiated tumors at all time points after injection. A similar trend was observed using in vivo photoacoustic imaging, which was validated by ex vivo tissue fluorescence and photoacoustic imaging. Serial quantitative radioactivity measurements of probe biodistribution further demonstrated increased probe accumulation in irradiated tumors. Conclusion: HQ4-DTPA has high specificity for dead cells in vivo, potentiating its use as a contrast agent for determining the relative level of tumor cell death following radiation therapy using NIRF, photoacoustic imaging and SPECT in vivo. Initial preclinical results are promising and indicate the need for further evaluation in larger cohorts. If successful, such studies may help develop a new multimodal method for non-invasive and dynamic deep-tissue imaging of treatment-induced cell death to quantitatively assess therapeutic response in patients.
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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 | 0.000 |
| 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) | 0.000 | 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".