Laura M G van Huizen, Max Blokker, Johannes M A Daniels, Teodora Radonic, Jan H von der Thüsen, Mitko Veta, Jouke T Annema, Marie Louise Groot
{"title":"Rapid on-site histology of lung and pleural biopsies using higher harmonic generation microscopy and artificial intelligence analysis.","authors":"Laura M G van Huizen, Max Blokker, Johannes M A Daniels, Teodora Radonic, Jan H von der Thüsen, Mitko Veta, Jouke T Annema, Marie Louise Groot","doi":"10.1016/j.modpat.2024.100633","DOIUrl":null,"url":null,"abstract":"<p><p>Lung cancer is both one of the most prevalent and lethal cancers. To improve health outcomes while reducing the healthcare burden, it becomes crucial to move towards early detection and cost-effective workflows. Currently there is no method for on-site rapid histological feedback on biopsies taken in diagnostic endoscopic or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. Here, we report the feasibility of a HHG portable microscope in the clinical workflow in terms of acquisition time, image quality and diagnostic accuracy in suspected pulmonary and pleural malignancy. 109 biopsies of 47 patients were imaged and a biopsy overview image was provided within a median of 6 minutes after excision. The assessment by pathologists and an artificial intelligence (AI) algorithm showed that image quality was sufficient for a malignancy or non-malignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide rapid pathology outcome on biopsy samples enabling immediate diagnosis and (local) treatment.</p>","PeriodicalId":18706,"journal":{"name":"Modern Pathology","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern Pathology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.modpat.2024.100633","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PATHOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Lung cancer is both one of the most prevalent and lethal cancers. To improve health outcomes while reducing the healthcare burden, it becomes crucial to move towards early detection and cost-effective workflows. Currently there is no method for on-site rapid histological feedback on biopsies taken in diagnostic endoscopic or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. Here, we report the feasibility of a HHG portable microscope in the clinical workflow in terms of acquisition time, image quality and diagnostic accuracy in suspected pulmonary and pleural malignancy. 109 biopsies of 47 patients were imaged and a biopsy overview image was provided within a median of 6 minutes after excision. The assessment by pathologists and an artificial intelligence (AI) algorithm showed that image quality was sufficient for a malignancy or non-malignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide rapid pathology outcome on biopsy samples enabling immediate diagnosis and (local) treatment.
期刊介绍:
Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology.
Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.