Quantitative assessment of collagen architecture from routine histopathological images shows concordance with Second Harmonic Generation microscopy

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.

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Publication Details

Journal
PLOS Digital Health
Published
2026-10-26
DOI
https://doi.org/10.1371/journal.pdig.0001364
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00

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Quantitative assessment of collagen architecture from routine histopathological images shows concordance with Second Harmonic Generation microscopy

Chaitanyanand B. Koppiker, Madhura Kulkarni, Nameeta Shah, Krishnendu Dandapat et al.
PLOS Digital Health
AI in cancer detection
article

Quantitative assessment of collagen architecture from routine histopathological images shows concordance with Second Harmonic Generation microscopy

Chaitanyanand B. Koppiker, Madhura Kulkarni, Nameeta Shah, Krishnendu Dandapat, Varun Raghunathan, Jyothsna Konkada Manattayil, Vedant Ingawale, Shashank Gupta, L. S. Shashidhara
article en

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.

PLOS Digital HealthVol. 5(10)
National Centre for Biological Sciences (IN), Ashoka University (IN), Prashanti Cancer Care Mission (IN), Indian Institute of Science Bangalore (IN)
Princeton Center for Complex Materials, Department of Biotechnology, Ministry of Science and Technology, India
Openalex Percentile: Top 14%
AI in cancer detection
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