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Diagnostics· 2026Q2

Photon Absorption Remote Sensing Virtual Histopathology: A Preliminary Exploration of Diagnostic Equivalence to Gold-Standard H&E Staining in Skin Cancer Excisional Biopsies

Benjamin R. Ecclestone, James E. D. Tweel, Marie Abi Daoud, Hager Gaouda et al.

Short summary

Photon Absorption Remote Sensing (PARS) coupled with deep learning can generate label-free virtual H&E images of skin cancer excisions with diagnostic performance comparable to traditional H&E staining, achieving 96.7% sensitivity and 95.2% specificity.

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Key points

  • PARS technology generates label-free virtual H&E images of skin cancer excisions.
  • Diagnostic sensitivity and specificity of PARS virtual H&E were comparable to conventional H&E staining (96.7% vs 95.6% sensitivity; 95.2% vs 85.7% specificity).
  • Primary diagnostic agreement between PARS and H&E was 95.5% with high inter-rater reliability.
  • Inter-modality agreement for SCC and BCC subtyping was high (91.8% and 100%, respectively).
  • Dermatopathologists showed equivalent diagnostic confidence and could not reliably distinguish image origins.

AI-generated from the title and abstract; the full text is not read.

Abstract

Background: Photon Absorption Remote Sensing (PARS) enables label-free imaging of subcellular morphology by observing absorption interactions. Coupled with deep-learning, PARS produces label-free virtual hematoxylin and eosin (H&E) images in unprocessed tissues. This study evaluates the diagnostic performance of PARS-derived virtual H&E images in skin excisions, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and normal skin. Methods: Sixteen unstained formalin-fixed paraffin-embedded excisions were PARS-imaged, then stained with chemical H&E dye. Seven fellowship-trained dermatopathologists assessed all 32 images (16 PARS and 16 H&E) in a masked randomized fashion. Example PARS and chemical-H&E whole-slide images are available at the S-BIAD2324 Archive. Results: Diagnostic sensitivity and specificity were comparable between PARS (sensitivity: 96.7% [95% CI: 89.5–100.0%]; specificity: 95.2% [95% CI: 74.7–100.0%]) and H&E (sensitivity: 95.6% [95% CI: 88.2–100.0%]; specificity: 85.7% [95% CI: 50.2–100.0%]). Primary diagnostic agreement was 95.5% (95% CI: 89.5–100.0%; Cohen’s κ = 0.929 [95% CI: 0.869–0.989]), with high inter-rater reliability (Fleiss’ κ = 0.886 [95% CI: 0.751–1.00] for PARS; κ = 0.805 [95% CI: 0.652–0.958] for H&E). Inter-modality subtyping agreement was 91.8% (95% CI: 81.4–100.0%; Cohen’s κ = 0.731 [95% CI: 0.375–1.000]) for SCC and 100% (κ = 1.000) for BCC. For margin confinement, agreement was 92.2% (95% CI: 85.0–99.4%; Cohen’s κ = 0.718 [95% CI: 0.452–0.984]). Dermatopathologists could not reliably distinguish image origin, and diagnostic confidence was statistically equivalent between modalities. Conclusions: Diagnostic performance of PARS virtual H&E images was consistent with established clinical benchmarks, providing encouraging preliminary evidence of diagnostic concordance with conventional H&E staining. While larger validation cohorts are required to establish true clinical equivalence, these initial findings highlight PARS label-free virtual histology as a promising approach to enhance dermatopathology diagnostics by preserving tissue for downstream analysis while supporting AI integration.

The authors' abstract, as published at the source. Diagnostics, 2026 · DOI ↗

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Field: Artificial Intelligence

Artificial IntelligenceComputer Science