Finding Patterns In Mass Spectrometry Images
Bibliographic record
Abstract
Analysis of mass spectrometry (MS) images could have many applications including aiding a pathologist in diagnosis of tissue samples, guiding a surgeon during tumor removal procedures, and discovering biomarkers of disease. MS is an analytical chemistry technique that can provide insight into metabolite patterns of tissue. MS imaging (MSI) produces mappings of MS information across a tissue sample. The processing and visualization of MS images is challenging due to their high dimensionality. In this work we used MS images obtained from breast conserving surgery (BCS), a common treatment for breast cancer. Margin analysis of removed tissue from BCS has been associated with factors such as decrease in local recurrence and is commonly performed post-operatively by a pathologist. Pathologic diagnosis has reported inter-observer and intra-observer variability. The lack of high quality intraoperative analysis has led to high rates of reexcision. Analysis of tissue samples could enhance efficacy of surgical procedures such as BCS. We hypothesize that application of proposed computational methods will enable multivariate visualization of MS images with strong correspondence to gold standard annotated histology images. For this dissertation we had access to 9 tissue samples obtained from BCS with a diagnosis of invasive ductal carcinoma. We proposed a series of computations including dimensionality reduction and graph clustering for automatic foreground segmentation and foreground clustering; affine spaces, or flats, to represent metabolite patterns of tissue types from across samples; and distance maps for novel multivariate visualization of MS images. We compared our results to conventional forms of visualization and to conventional processing methods. Our computations achieved untargeted multivariate processing and visualization of MS images. Distance maps showed strong correspondence to annotated histology images and were superior to conventional visualization techniques. The affine spaces identified biologically relevant ions that could be associated with fatty acids, which are precursors to energy cycles that are enhanced in malignant cells. Our proposed computations were statistically significantly different than conventional methods. The proposed computations could be applied to BCS to provide surgical guidance and be used in conjunction with gold standard pathology analysis to increase speed of diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".