Contrast Agent Mass Spectrometry Imaging Reveals Tumor Heterogeneity
Bibliographic record
Abstract
Mapping intratumoral heterogeneity such as vasculature and margins is important during intraoperative applications. Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated potential for intraoperative tumor imaging using validated MS profiles. The clinical translation of DESI-MS into a universal label-free imaging technique thus requires access to MS profiles characteristic to tumors and healthy tissues. Here, we developed contrast agent mass spectrometry imaging (CA-MSI) that utilizes a magnetic resonance imaging (MRI) contrast agent targeted to disease sites, as a label, to reveal tumor heterogeneity in the absence of known MS profiles. Human breast cancer tumors grown in mice were subjected to CA-MSI using Gadoteridol revealing tumor margins and vasculature from the localization of [Gadoteridol+K](+) and [Gadoteridol+Na](+) adducts, respectively. The localization of the [Gadoteridol+K](+) adduct as revealed through DESI-MS complements the in vivo MRI results. DESI-MS imaging is therefore possible for tumors for which no characteristic MS profiles are established. Further DESI-MS imaging of the flux of the contrast agent through mouse kidneys was performed indicating secretion of the intact label.
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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) | 0.006 | 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".