Rapid Detection of Necrosis in Breast Cancer with Desorption Electrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
Abstract Identification of necrosis in tumors is of prognostic value in treatment planning, as necrosis is associated with aggressive forms of cancer and unfavourable outcomes. To facilitate rapid detection of necrosis with Mass Spectrometry (MS), we report the lipid MS profile of necrotic breast cancer with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging validated with statistical analysis and correlating pathology. This MS profile is characterized by (1) the presence of the ion of m/z 572.48 [Cer(d34:1) + Cl]− which is a ceramide absent from the viable cancer subregions; (2) the absence of the ion of m/z 391.25 which is present in small abundance only in viable cancer subregions; and (3) a slight increase in the relative intensity of known breast cancer biomarker ions of m/z 281.25 [FA(18:1)-H]− and 303.23 [FA(20:4)-H]−. Necrosis is accompanied by alterations in the tissue optical depolarization rate, allowing tissue polarimetry to guide DESI-MS analysis for rapid MS profiling or targeted MS imaging. This workflow, in combination with the MS profile of necrosis, may permit rapid characterization of necrotic tumors from tissue slices. Further, necrosis-specific biomarker ions are detected in seconds with single MS scans of necrotic tumor tissue smears, which further accelerates the identification workflow by avoiding tissue sectioning and slide preparation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".