Kinetic model optimization for characterizing tumour physiology by dynamic contrast-enhanced near-infrared spectroscopy
Bibliographic record
Abstract
Dynamic contrast-enhanced (DCE) methods are widely used with magnetic resonance imaging and computed tomography to assess the vascular characteristics of tumours since these properties can affect the response to radiotherapy and chemotherapy. In contrast, there have been far fewer studies using optical-based applications despite the advantages of low cost and safety. This study investigated an appropriate kinetic model for optical applications to characterize tumour haemodynamics (blood flow, F, blood volume, V(b), and vascular heterogeneity) and vascular leakage (permeability surface-area product, PS). DCE data were acquired with two dyes, indocyanine green (ICG) and 800 CW carboxylate (IRD(cbx)), from a human colon tumour xenograph model in rats. Due to the smaller molecular weight of IRD(cbx) (1166 Da) compared to albumin-bound ICG (67 kDa), PS of IRD(cbx) was significantly larger; however, no significant differences in F and V(b) were found between the dyes as expected. Error analysis demonstrated that all parameters could be estimated with an uncertainty less than 5% due to the high temporal resolution and signal-to-noise ratio of the optical measurements. The next step is to adapt this approach to optical imaging to generate haemodynamics and permeability maps, which should enhance the clinical interest in optics for treatment monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".