82 AI-guided spatial profiling of tertiary lymphoid structures in human lung cancer by imaging mass cytometry
Bibliographic record
Abstract
Background Imaging Mass Cytometry™ (IMC™) technology enables high-dimensional, multiplexed imaging at subcellular resolution without autofluorescence or cyclic imaging. The Hyperion™ XTi Imaging System supports three acquisition modes: Preview Mode (PM), Tissue Mode (TM), and Cell Mode (CM). PM provides rapid whole-slide scans for tissue overview within minutes, while TM captures entire tissue sections at 5-μm resolution, mapping over 40 biomarkers to reveal spatial heterogeneity. CM enables high-resolution, single-cell imaging of regions of interest (ROIs) identified in PM. Together with an automated slide loader, these modes support continuous, high-throughput tissue analysis.Methods Lung cancer tissue sections from an immunotherapy-treated patient were stained with a 34-marker IMC panel, combining the Human Immuno-Oncology IMC Panel and Maxpar IMC Cell Segmentation Kit. Imaging was performed using the Hyperion XTi Imaging System (Standard BioTools). PM was used for initial scanning, followed by automated ROI selection via Phenoplex™ software (Visiopharm®) based on three criteria: (1) tertiary lymphoid structures (TLS) expressing CD20 and CD3, (2) Granzyme B-rich regions, and (3) clusters of CD68 and Vimentin double-positive cells. Adjacent serial sections were imaged in TM for whole-slide morphological comparison.Results Tissue segmentation across all modes was performed using a deep-learning AI algorithm trained to identify key features such as vessels and TLS. CM images were analyzed at the single-cell level using iridium DNA-based segmentation. Spatial mapping of the TLS and cellular phenotyping was conducted with the Phenoplex guided workflow. Immune contexture was assessed using t-SNE plots stratified by spatial region and clinical variables.Conclusions This study demonstrates the utility of the Hyperion XTi Imaging System’s multimodal acquisition capabilities in extracting spatial and phenotypic insights from complex tissue samples. The integration of PM, TM, and CM modes with automated ROI selection and AI-driven segmentation enables efficient, high-resolution analysis of immune architecture. The Phenoplex platform facilitates rapid identification and spatial mapping of key cell populations and phenotypes, supporting advanced tissue profiling and biomarker discovery in immuno-oncology research.Ethics Approval This study was approved by the Institutional Review Board of Fukushima Medical University, approval No. 2538.
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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) | 0.000 | 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) | 0.002 | 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".