Identification of human ovarian cancer relying on collagen fiber coverage features by quantitative second harmonic generation imaging

Abstract

Ovarian cancer has the highest mortality rate among all gynecological cancers, containing complicated heterogeneous histotypes, each with different treatment plans and prognoses. The lack of screening test makes new perspectives for the biomarker of ovarian cancer of great significance. As the main component of extracellular matrix, collagen fibers undergo dynamic remodeling caused by neoplastic activity. Second harmonic generation (SHG) enables label-free, non-destructive imaging of collagen fibers with submicron resolution and deep sectioning. In this study, we developed a new metric named local coverage to quantify morphologically localized distribution of collagen fibers and combined it with overall density to characterize 3D SHG images of collagen fibers from normal, benign and malignant human ovarian biopsies. An overall diagnosis accuracy of 96.3% in distinguishing these tissue types made local and overall density signatures a sensitive biomarker of tumor progression. Quantitative, multi-parametric SHG imaging might serve as a potential screening test tool for ovarian cancer.

Label‐free assessment of pathological changes in pancreatic intraepithelial neoplasia by biomedical multiphoton microscopy

“…It has been shown that there is a link between the progression of cancer and the alteration of its surrounding collagen fibers [34][35][36]. To obtain the quantitative information of collagen fibers surrounding normal pancreatic ducts and different grades of PanIN, we extracted eight morphological features of the collagen fibers surrounding the ducts from MPM images by an automated image processing strategy.…”

Mapping the 3D remodeling of the extracellular matrix in human hypertrophic scar by multi-parametric multiphoton imaging using endogenous contrast

“…For waviness, a higher value represented curvier status. Local coverage was another metric that quantified the voxel-wise localized distribution of fibers [ 45 ]. The value of local coverage also ranged from 0 to 1 with a higher value corresponding to morphologically denser fiber distribution in a local area.…”

“…Based on orientation information, we further proposed optical metrics, named directional variance and waviness, respectively, as quantitative measures of alignment and bending degree of fibers, and applied them to mapping physiological and pathological functions of collagen fibers in articular cartilage, mammary glands, breast tissues, ovarian tissues and pancreas, in the context of diseases including osteoarthritis and cancer [ [41] , [42] , [43] , [44] ]. Besides, we developed an algorithm for the quantification of morphologically localized distribution of fiber-like structures, termed local coverage, which provided new insights into the spatial organization of fibrous tissues [ 45 ]. Importantly, by extracting 3D information, these optical metrics faithfully reflected the real architecture of biological tissues.…”

Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning

“… 19 , 20 In addition, alterations in morphology and organization of collagen and elastin fibers, the major components of the ECM, are closely associated with physiologic and pathologic status of biological tissues. 21 – 25 Compared with traditional pathological methods, multiphoton imaging technology has great benefits in simplifying sample preparation. In the pathological method, it requires fixing the tissue sections with paraffin and staining them.…”