{ "abstract": "Collagen organization within the tumour microenvironment plays a critical role in tumour progression and has emerged as an important structural biomarker in cancer. Second Harmonic Generation (SHG) microscopy enables label-free visualization and quantitative assessment of fibrillar collagen architecture; however, its high cost, specialized instrumentation, and limited field-of-view restrict routine clinical application. In this study, we evaluated whether collagen features quantified from digitally scanned Masson–Goldner’s Trichrome-stained histopathological sections can approximate measurements obtained from SHG microscopy. Formalin-fixed paraffin-embedded breast tumour tissues, including benign and invasive ductal carcinoma (IDC) samples with varying collagen content, were analysed using SHG microscopy and whole-slide brightfield imaging. Matched regions of interest were analysed using two independent digital image analysis approaches: a conventional ImageJ-based workflow (TWOMBLI) and a machine learning–based computational pipeline. Collagen structural parameters including collagen deposition area, fibre number, and alignment metrics were quantified and compared across imaging modalities using correlation analysis. SHG signals were consistently detected from Trichrome-stained sections, confirming compatibility of SHG imaging. Quantitative comparison demonstrated significant concordance between SHG-derived collagen metrics and those obtained from digital image analysis pipelines, particularly for collagen area and fibre alignment. These findings demonstrate a proof-of-concept, that computational analysis of routine histopathological images can capture key spatial features of collagen organization comparable to SHG microscopy. Digital pathology–based collagen quantification therefore represents a potentially scalable and clinically accessible approach for assessing extracellular matrix architecture in tumour tissues. Author summary: Quantitative assessment of extracellular matrix architecture remains a critical yet underutilized dimension of cancer pathology. While Second Harmonic Generation (SHG) microscopy provides a gold-standard, label-free approach to interrogate fibrillar collagen organization, its limited accessibility has prevented integration into routine clinical workflows. This creates a translational gap between advanced imaging capabilities and potential scalable clinical implementation. Here, we address this gap by demonstrating that collagen structural features derived from routine histopathological whole-slide images can approximate SHG-derived measurements. Using matched regions from breast tumour tissues, we perform a systematic cross-modality comparison between SHG microscopy and two independent computational pipelines, a conventional ImageJ-based workflow and a machine learning–based framework. We observe strong concordance across key spatial metrics, including collagen deposition, fibre density, and alignment. Importantly, we establish that SHG imaging is fully compatible with Trichrome-stained sections, enabling direct validation on identical tissue regions. Our findings show that widely available digital pathology data, when coupled with computational analysis, can recover biologically and clinically relevant features of collagen architecture previously accessible primarily through specialized optical systems. This work introduces a potentially scalable, cost-effective, and clinically translatable framework for extracellular matrix quantification, with implications for biomarker discovery, computational pathology, and integration of stromal features into digital health platforms.", "abstract_shown": true, "affiliations": [ "Centre for Translational Cancer Research, Indian Institute of Science Education and Research, Pune, andPrashanti Cancer Care Mission, Pune, India.", "Prashanti Cancer Care Mission, Pune, India.", "Ashoka University, Sonipat, India.", "Department of Electrical Communication Engineering (ECE), Indian Institute of Science, Bangalore, India.", "Amaranth Medical Analytics, Bangalore, India.", "National Center for Biological Sciences, Bangalore, India." ], "article_type": "Research Article", "arxiv": null, "author_total": 9, "authors": [ { "affs": [ 0, 1, 2 ], "name": "Vedant Ingawale", "orcid": "0009-0007-5727-4618" }, { "affs": [ 3 ], "name": "Krishnendu Dandapat", "orcid": "0000-0002-1663-3908" }, { "affs": [ 3 ], "name": "Jyothsna Konkada Manattayil" }, { "affs": [ 4 ], "name": "Shashank Gupta", "orcid": "0009-0004-3809-8530" }, { "affs": [ 0, 1, 2, 5 ], "name": "L S Shashidhara" }, { "affs": [ 0, 1 ], "name": "Chaitanyanand Koppiker" }, { "affs": [ 4 ], "name": "Nameeta Shah", "orcid": "0000-0002-4429-8414" }, { "affs": [ 3 ], "name": "Varun Raghunathan", "orcid": "0000-0002-0141-4032" }, { "affs": [ 0, 1 ], "name": "Madhura Kulkarni", "orcid": "0000-0002-0406-5739" } ], "bibtex": "https://research.draconitic.com/paper/9ghmx21w9ryx8.bib", "cited_by": 0, "doi": "10.1371/journal.pdig.0001364", "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001364", "id": "9ghmx21w9ryx8", "issns": [ "2767-3170" ], "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001364", "language": "eng", "licence": "CC-BY", "licence_label": "CC BY", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1702|AI in cancer detection" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Collagens" ], [ "plos", "/Computer and information sciences/Digital imaging" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms/Benign tumors" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms/Breast tumors/Breast cancer" ], [ "plos", "/Research and analysis methods/Computational techniques/Computational pipelines" ], [ "plos", "/Research and analysis methods/Imaging techniques/Image analysis" ], [ "plos", "/Research and analysis methods/Microscopy/Light microscopy/Bright field microscopy" ], [ "plos", "/Research and analysis methods/Specimen preparation and treatment/Staining" ] ], "openalex": "W7223649230", "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001364&type=printable", "pmcid": "PMC13704883", "pmid": "42932315", "published": "2026-10-26", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/9ghmx21w9ryx8.ris", "sources": [ { "first_seen": "2026-10-30T12:17:17Z", "native_id": "10.1371/journal.pdig.0001364", "source": "plos", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001364" }, { "first_seen": "2026-10-30T12:22:01Z", "native_id": "MED:42932315", "source": "europepmc", "url": "https://europepmc.org/article/MED/42932315" } ], "subjects": [ { "field": "medicine", "origin": "plos:cancers and neoplasms", "subfield": "oncology" } ], "title": "Quantitative assessment of collagen architecture from routine histopathological images shows concordance with Second Harmonic Generation microscopy", "title_text": "Quantitative assessment of collagen architecture from routine histopathological images shows concordance with Second Harmonic Generation microscopy", "type": "peer-reviewed", "updated_at": "2026-10-30T12:22:01Z", "url": "https://research.draconitic.com/paper/9ghmx21w9ryx8", "venue": "PLOS Digital Health", "versions": [] }