Diagnosis of fibrotic interstitial lung diseases based on the combination of label-free quantitative multiphoton fiber histology and machine learning.

Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional H&E staining primarily reveals cellular inflammation with limited detail on fibrosis. To address these issues, we introduce a pioneering label-free quantitative multiphoton fiber histology (MPFH) technique that delineates the intricate characteristics of collagen and elastin fibers for ILDs diagnosis. We acquired co-located multiphoton and H&E-stained images from a single tissue slice. Multiphoton imaging was performed on the deparaffinized section to obtain fibrotic tissue information, followed by H&E staining to capture cellular information. This approach was tested in a blinded diagnostic trial among 7 pathologists involving 14 relatively normal lung patients and 31 ILD patients (11 idiopathic pulmonary fibrosis (IPF) / usual interstitial pneumonia (UIP), 14 nonspecific interstitial pneumonia (NSIP), and 6 pleuroparenchymal fibroelastosis (PPFE)). A customized algorithm extracted quantitative fiber indicators from multiphoton images. These indicators, combined with clinical and radiological features, were used to develop an automatic multi-class ILDs classifier. Using MPFH, we can acquire high-quality, co-localized images of collagen fibers, elastin fibers, and cells. We found that the type, distribution, and degree of fibrotic proliferation can effectively distinguish between different subtypes. The blind study showed MPFH enhanced diagnostic consistency (kappa values from 0.56 to 0.72) and accuracy (from 73.0% to 82.5%, p=0.0090). The combination of quantitative fiber indicators effectively distinguished between different tissues, with areas under the receiver operating characteristic curves exceeding 0.92. The automatic classifier achieved 93.8% accuracy, closely paralleling the 92.2% accuracy of expert pathologists. The outcomes of our research underscore the transformative potential of MPFH in the field of f-ILD diagnostics. By integrating quantitative analysis of fiber characteristics with advanced machine learning algorithms, MPFH facilitates the automatic and accurate identification of various fibrotic disease subtypes, showcasing a significant leap forward in precision diagnostics.