The application of intraoperative specimen mammography for margin status assessment in breast‐conserving surgery: A single‐center retrospective study

Cc, Lin; Wang, Kai-yue; Xü, Fang; Liu, Yan; Li, Jin‐fan; Chi, Pan; Hu, Kaimin; Xu, Wenhong; Chen, Zhigang; Chen, Yi‐ding

doi:10.1111/tbj.13835

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…Several researches demonstrated that the use of X-rays for specimen imaging can improve the targeting of sampling and lead to a significant reduction in positive margins (119)(120)(121)(122). For example, Zhang et al (122) used a breast pathology cabinet X-ray system (CXS) to assist in breast cancer tumor bed identification.…”

Section: Surgical Margin Assessmentmentioning

confidence: 99%

Progress on deep learning in digital pathology of breast cancer: a narrative review

Zhu¹,

Liu²,

Li³

2022

Gland Surg

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Background and Objective: Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis.However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.Methods: A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. Key Content and Findings: DL-based computerized image analysis has obtained impressive achievementsin breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.Conclusions: Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

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