Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.

Autor: Hayward MK; Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, California, USA., Weaver VM; Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, California, USA; Department of Bioengineering and Therapeutic Sciences, and UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA; Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA. Electronic address: valerie.weaver@ucsf.edu.
Jazyk: angličtina
Zdroj: Biochimica et biophysica acta. Reviews on cancer [Biochim Biophys Acta Rev Cancer] 2021 Aug; Vol. 1876 (1), pp. 188555. Date of Electronic Publication: 2021 Apr 29.
DOI: 10.1016/j.bbcan.2021.188555
Abstrakt: Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.
(Copyright © 2021 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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