Towards virtual biopsies of gastrointestinal tissues using photoacoustic remote sensing microscopy
Bibliographic record
Abstract
Abstract: Gastrointestinal (GI) tissue biopsies provide critical diagnostic information for a wide variety of conditions such as neoplastic diseases (colorectal, small bowel and stomach cancers) and non-neoplastic diseases (inflammatory disorders, infection, celiac disease). Endoscopic biopsies collect small tissue samples that require resource intensive processing to permit histopathological analysis. Unfortunately, the sparsely collected biopsy samples may fail to capture the pathologic condition because selection of biopsy sites relies on macroscopic superficial tissue features and clinician judgement. Here, we present the first all-optical non-contact label-free non-interferometric photoacoustic microscopy system capable of performing “virtual biopsies”. A modular photoacoustic remote sensing (PARS™) architecture is used facilitating imaging of unprocessed tissues providing information similar to conventional histopathological staining techniques. Prospectively this would allow gastroenterologists to assess subcellular tissue morphology in situ when selecting biopsy location. Tested on preserved unstained human and freshly resected murine tissues, the presented PARS microscope rapidly retrieves images of similar area to current biopsies, while maintaining comparable quality to the current standard for histopathological analysis. Additionally, results show the first label free assessment of subsurface cellular morphology in FFPE GI tissue blocks. Clinically relevant features are recovered including cellular details such as lamina propria within colon tissue and cell nuclear structure in resected smooth muscle. Constructed with a modular architecture, this system facilitates the future development of compact imaging heads. The modular PARS system overcomes many of the challenges with imaging unstained thick tissue in situ, representing a significant milestone in the development of a clinical microscope providing virtual biopsy capabilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".