Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning - Archive ouverte HAL Access content directly
Journal Articles Scientific Reports Year : 2022

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning

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Javiera Pérez-Anker
  • Function : Author
Linda Tognetti
  • Function : Author
Angelo Di Naro
  • Function : Author
Mariano Suppa
Elisa Cinotti
Théo Viel
  • Function : Author
Jilliana Monnier
Pietro Rubegni
  • Function : Author
Véronique del Marmol
  • Function : Author
Josep Malvehy
Susana Puig
Arnaud Dubois
Jean-Luc Perrot
  • Function : Author

Abstract

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.

Dates and versions

hal-03583319 , version 1 (21-02-2022)

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Cite

Sébastien Fischman, Javiera Pérez-Anker, Linda Tognetti, Angelo Di Naro, Mariano Suppa, et al.. Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning. Scientific Reports, 2022, 12 (1), pp.481. ⟨10.1038/s41598-021-04395-1⟩. ⟨hal-03583319⟩
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