Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, California, USA.
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.
Biochim Biophys Acta Rev Cancer. 2021 Aug;1876(1):188555. doi: 10.1016/j.bbcan.2021.188555. Epub 2021 Apr 29.
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.
乳腺导管原位癌（DCIS）是一种侵袭前病变，被认为是浸润性乳腺癌的前兆。然而，并非所有的 DCIS 都会进展为浸润性癌。目前的组织病理学分类系统无法预测哪些病例会进展，哪些不会进展，因此许多患有 DCIS 的女性可能会过度治疗。基于人工智能（AI）的图像分析方法有可能识别和分析可能有助于肿瘤识别、疾病结局预测和治疗反应的新特征。事实上，这些方法在准确识别 DCIS 病变方面表现出了良好的效果，并在 DCIS 患者的治疗分层中显示出了潜在的临床应用价值。在这里，我们回顾了组织病理学中的 AI 技术如何帮助诊断和临床决策，以及这些技术如何被纳入临床实践。
