Towards Improving Surgical Margins in Tumour Resection Using Mass Spectrometry Imaging
Bibliographic record
Abstract
Successful cancer resection is limited by the inability to differentiate between cancer and normal tissue intraoperatively. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is an emerging and powerful analytical technique that offers a rapid and low cost approach for assessing surgical margins by generating detailed metabolic profiles. However, exploiting this data for tissue characterization based on molecular signals requires machine learning methods to handle its complexity. In this work, we utilize machine learning models for the characterization of tissue using DESI-MSI data obtained from prostate tissue samples. We use ViPRE, a novel open-source software, to annotate a large DESI-MSI dataset. We explore various machine learning models and train test schemes for cancer classification. Cross-validation of our models result in high balanced accuracy, sensitivity and specificity for cancer classification. Furthermore, we simulate the prospective application of perioperative tissue characterization, generating a qualitative visual prediction for whole slides that match pathology annotations. Finally, the application of linear transformation and classification algorithms on DESI-MSI data effectively distinguished between the molecular profiles associated with different cancer grades. Our findings highlight the promise of combining machine learning with large DESI-MSI datasets for tissue characterization, thereby improving surgical margin precision.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".