HistoIndex Explores the Clinical Utility of Stain-free AI Digital Pathology Platform in 388 Patients with Triple-Negative Breast Cancer (TNBC)

Assessing the morphological and architectural changes in collagen fibers with the platform could potentially aid TNBC clinical trials in categorizing patients and monitoring therapeutic responses.SINGAPORE, Jan. 24,A 2021 /PRNewswire/ -- HistoIndex's stain-free AI digital pathology platform, incorporating Second Harmonic Generation (SHG), holds promise as a quantitative tool in the assessment of morphological and architectural changes in collagen fibers within the tumor-stromal microenvironment in patients with TNBC. This will allow clinicians to better interpret the role of collagen remodeling in tumor progression and its prognostic value. On a larger scale, the tool may greatly aid existing and future TNBC studies that are currently exploring new therapies for targeted treatments.TNBC is an incredibly challenging and aggressive form of breast cancer compared to other subtypes and holds a relatively poor prognosis primarily due to a lack of targeted treatments. In cancer, collagen fibers play a significant role in the tumor microenvironment, with remodeling of the extracellular matrix (ECM) that is often more collagen-rich with increased 'stiffness' [1]. As a component of the ECM, collagen may also influence cancer cell behaviorA [2]. Particularly in TNBC, collagen remodeling is seen in the stromal compartment [3]. Assessing Collagen Features at a Finer Level of DetailIn a collaborative study involving scientists from the Institute of Molecular and Cell Biology (IMCB) in Singapore and TNBC pathologists from the Singapore General Hospital (SGH), unstained biopsies from 388 TNBC patients were scanned using HistoIndex's AI-based SHG platform and analyzed to extract different collagen features from the SHG images at a finer level of detail. Findings published in the leading peer-reviewed oncology journal, Breast Cancer Research [3], showed a strong correlation between several imaging features and clinicopathological characteristics. Aggregation of collagen fibers, collagen fiber density and the length of dispersed thin collagen fibers were key collagen-associated parameters revealed to be of prognostic value based on the patient cohort and clinical outcomes. Furthermore, analyzing the aggregated thick collagen (ATC) fibers and dispersed thin collagen (DTC) fibers (as shown in Figure 1) provided a novel understanding of collagen remodeling during cancer progression.
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