无需染色，光吸收成像可生成与传统病理切片等效的皮肤癌诊断图像。
Photon Absorption Remote Sensing Virtual Histopathology: A Preliminary Exploration of Diagnostic Equivalence to Gold-Standard H&E Staining in Skin Cancer Excisional Biopsies
- 利用光吸收成像结合深度学习，直接从未染色组织生成虚拟H&E图像。
- 95.5%诊断一致率，对基底细胞癌诊断完全吻合，边缘判定准确率达92%。
- 医生无法区分虚拟染色与真实染色，适合临床诊断与AI辅助分析。
光子吸收远程传感（PARS）通过检测生物分子特异性吸收，实现无需染色的亚细胞形态成像。结合深度学习，PARS可在未处理组织上生成无标记虚拟苏木精-伊红（H&E）图像。本研究评估了16例皮肤切除活检样本中PARS虚拟H&E图像的诊断性能，涵盖鳞状细胞癌（SCC）、基底细胞癌（BCC）及正常皮肤。所有样本经PARS成像后生成虚拟H&E图像，再进行化学H&E染色并成像（40倍）。七位经过专科培训的皮肤病理学家对所有图像进行评估。结果显示，PARS与化学H&E图像的主诊断一致性达95.5%（Cohen's k=0.93），组间可靠性接近完美（Fleiss' k=0.89 for PARS, k=0.80 for H&E）。SCC亚型分类一致性为91%（k=0.73），BCC分类完全一致。恶性病变边界判断一致性为92%（k=0.718）。评估过程中，病理医生无法可靠区分图像来源，且诊断信心相当。该结果表明，PARS虚拟组织学在皮肤病理诊断中可能与化学H&E染色等效，同时可直接对未标记切片进行分析，保留组织用于后续检测，并便于与人工智能集成，有望加速和提升皮肤癌诊断效率。
原文摘要 · Abstract (English)
Photon Absorption Remote Sensing (PARS) enables label-free imaging of subcellular morphology by observing biomolecule specific absorption interactions. Coupled with deep-learning, PARS produces label-free virtual Hematoxylin and Eosin (H&E) stained images in unprocessed tissues. This study evaluates the diagnostic performance of PARS virtual H&E images in excisional skin biopsies, including Squamous (SCC), Basal (BCC) Cell Carcinoma, and normal skin. Sixteen unstained formalin-fixed paraffin-embedded skin excisions were PARS imaged, virtually H&E stained, then chemically stained and imaged at 40x. Seven fellowship trained dermatopathologists assessed all images. Example PARS and chemical H&E whole-slide images from this study are available at the BioImage Archive (https://doi.org/10.6019/S-BIAD2324). Concordance analysis indicates 95.5% agreement between primary diagnoses from PARS versus H&E images (Cohen's k=0.93). Inter-rater reliability was near-perfect for both image types (Fleiss' k=0.89 for PARS, k=0.80 for H&E). For subtype classification, agreement was near-perfect 91% (k=0.73) for SCC and was perfect for BCC. For malignancy confinement (e.g., cancer margins), agreement was 92% between PARS and H&E (k=0.718). During assessment dermatopathologists could not reliably distinguish image origin (PARS vs. H&E), and diagnostic confidence was equivalent. Inter-rater reliability for PARS virtual H&E was consistent with reported histologic evaluation benchmarks. These results indicate that PARS virtual histology may be diagnostically equivalent to chemical H&E staining in dermatopathology diagnostics, while enabling assessment directly from unlabeled slides. In turn, the label-free PARS virtual H&E imaging workflow may preserve tissue for downstream analysis while producing data well-suited for AI integration potentially accelerating and enhancing skin cancer diagnostics.
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