ArticleBiomedical optics express2018
Label-free classification of hepatocellular-carcinoma grading using second harmonic generation microscopy.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Margin diagnosis for endoscopic submucosal dissection of early gastric cancer using multiphoton microscopy.Surgical endoscopy · 2020Trial
- Prediction of mRECIST tumor response at firstfollow-up after DEB-TACE using a combined radiomics-clinical model and explainability methods.Clinical and experimental medicine · 2026Article
- Whole-slide mapping of tumor tissue fiber architecture via computational scattered light imaging.Biomedical optics express · 2026Article
- Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.Biomedical optics express · 2024Article
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma.BMC medical imaging · 2023Article
- Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma.Liver cancer · 2021Article
- Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer.Bioengineering (Basel, Switzerland) · 2021Review
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Authors and funding
9 authors.
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Abstract
The clear and accurate understanding of the degree of hepatocellular-carcinoma (HCC) differentiation plays a key role in the determination of the patient prognosis and development of a treatment plan by the clinician. However, label-free and automated classification of the HCC grading is challenging. Here, we demonstrate second-harmonic generation (SHG) microscopy for label-free classification of HCC grading in paraffin-embedded specimens. A total of 217 images from 113 patients were obtained using SHG microscopy, and the SHG signals from the collagen within the tumor were analyzed using feature extraction and selection, the Mann-Whitney test, and the receiver operating characteristic curves. The results exhibit good correlation between the software analysis and the diagnosis by experienced pathologists. Combining the image features and clinical information, an adaptive quantification algorithm is generated for automatically determining the HCC grade. The results suggest that SHG microscopy might be a promising automated diagnostic method for clinical use, without requiring time for tissue processing and staining.
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