Collagen organisation 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 visualisation and quantitative assessment of fibrillar collagen architecture; however, its high cost, specialised 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 that computational analysis of routine histopathological images can capture key spatial features of collagen organisation comparable to SHG microscopy. Digital pathology-based collagen quantification therefore, represents a 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 organisation, its limited accessibility has prevented integration into routine clinical workflows. This creates a translational gap between advanced imaging capabilities and 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 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.

The authors have declared no competing interest.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was given for the project titled “Molecular profiling of Indian Triple-Negative Breast Cancer”, on September 30th, 2022, by a registered Independent Ethics Committee (Registration EC/NEW/INST/2021/2443 Dated 12/May/2022). The PCCM-CETCR IEC has approved the proposal to conduct the study in accordance with ICMR (2017) guidelines.

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Quantifications are available in the S1 Dataset as part of Supporting Information.