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La pathologie cancéreuse pulmonaire à l’heure de l’intelligence artificielle : entre espoir, désespoir et perspectives

Abstract : Histopathology is the fundamental tool of pathology used for more than a century to establish the final diagnosis of lung cancer. In addition, the phenotypic data contained in the histological images reflects the overall effect of molecular alterations on the behavior of cancer cells and provides a practical visual reading of the aggressiveness of the disease. However, the human evaluation of the histological images is sometimes subjective and may lack reproducibility. Therefore, computational analysis of histological imaging using so-called “artificial intelligence” (AI) approaches has recently received considerable attention to improve this diagnostic accuracy. Thus, computational analysis of lung cancer images has recently been evaluated for the optimization of histological or cytological classification, prognostic prediction or genomic profile of patients with lung cancer. This rapidly growing field constantly demonstrates great power in the field of computing medical imaging by producing highly accurate detection, segmentation or recognition tasks. However, there are still several challenges or issues to be addressed in order to successfully succeed the actual transfer into clinical routine. The objective of this review is to emphasize recent applications of AI in pulmonary cancer pathology, but also to clarify the advantages and limitations of this approach, as well as the perspectives to be implemented for a potential transfer into clinical routine.
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Contributor : Project-Team Asclepios <>
Submitted on : Tuesday, January 21, 2020 - 10:00:25 AM
Last modification on : Wednesday, October 14, 2020 - 4:22:59 AM

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Simon Heeke, Hervé Delingette, Youta Fanjat, Elodie Long-Mira, Sandra Lassalle, et al.. La pathologie cancéreuse pulmonaire à l’heure de l’intelligence artificielle : entre espoir, désespoir et perspectives. Annales de Pathologie, Elsevier Masson, 2019, 39 (2), pp.130-136. ⟨10.1016/j.annpat.2019.01.003⟩. ⟨hal-02446712⟩



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