ArticleScientific reports2022
Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- [Line-field confocal optical coherence tomography and artificial intelligence].Dermatologie (Heidelberg, Germany) · 2025Pooled it
- Reflectance confocal microscopy detects distinct features of field damage in chronically UV-irradiated SKH1 mouse skin.JID innovations : skin science from molecules to population health · 2026Article
- Digital Dermatology.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Review
- Assessment of dermal fiber changes associated with age ethnicity and cosmetic product use by LC-OCT and automated 3D segmentation.Scientific reports · 2025Article
- An Assessment of Cellular Atypia Utilizing Line-Field Confocal Optical Coherence Tomography (LC-OCT).Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2025Article
- Quantitative assessment ofBiomedical optics express · 2025Article
- A Survey on Optical Coherence Tomography-Technology and Application.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Evaluating facial dermis aging in healthy Caucasian females with LC-OCT and deep learning.Scientific reports · 2024Article
- Interobserver agreement on line-field confocal optical coherence tomography image markers in keratinocyte carcinomas and precursor lesions.Archives of dermatological research · 2024Article
- Epidemiology and Risk Factors of Actinic Keratosis. What is New for The Management for Sun-Damaged Skin.Dermatology practical & conceptual · 2024Review
- Epidermal renewal during the treatment of atopic dermatitis lesions: A study coupling line-field confocal optical coherence tomography with artificial intelligence quantifications: LC-OCT reveals new biological markers of AD.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present.Bioengineering (Basel, Switzerland) · 2024Review
- Line-Field Confocal Optical Coherence Tomography for the Diagnosis of Skin Tumors: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Coupling tensile test with LC-OCT and ultrasound imaging: investigation of the skin sublayers mechanical behaviour.Royal Society open science · 2024Article
- Deep learning automatically assesses 2-µm laser-induced skin damage OCT images.Lasers in medical science · 2024Article
- Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Line-Field Confocal Optical Coherence Tomography (LC-OCT) for Skin Imaging in Dermatology.Life (Basel, Switzerland) · 2023Article
- Where Artificial Intelligence Can Take Us in the Management and Understanding of Cancerization Fields.Cancers · 2023Article
Corrections and comments
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Authors and funding
14 authors at 9 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.