Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning:A prospective cohort study in breast cancer patients

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外文摘要：An intraoperative diagnosis is critical for precise cancer surgery.However,traditional intraoperative assessments based on hematoxylin and eosin(H&E)histology,such as frozen section,are time-,resource-,and labor-intensive,and involve specimen-consuming concerns.Here,we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography(D-FFOCT),a label-free optical imaging method,and deep learning for bedside tumor diagnosis during surgery.To classify the benign and malignant breast tissues,we conducted a prospective cohort trial.In the modeling group(n=182),D-FFOCT images were captured from April 26 to June 20,2018,encompassing 48 benign lesions,114 invasive ductal carcinoma(IDC),10 invasive lobular carcinoma,4 ductal carcinoma in situ(DCIS),and 6 rare tumors.Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches.Subsequently,from June 22 to August 17,2018,indepen-dent tests(n=42)were conducted on 10 benign lesions,29 IDC,1 DCIS,and 2 rare tumors.The model yielded excellent performance,with an accuracy of 97.62％,sensitivity of 96.88％and specificity of 100％;only one IDC was misclassified.Meanwhile,the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures.In the simulated intraop-erative margin evaluation procedure,the time required for our novel workflow(approximately 3 min)was significantly shorter than that required for traditional procedures(approximately 30 min).These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraop-erative cancer diagnosis independently of traditional pathology laboratory procedures.

外文关键词：

Cancer diagnosisBreast neoplasmsDynamic full-field optical coherence tomographyDeep learningImage classification

作者：

Shuwei Zhang、Bin Yang、Houpu Yang、Jin Zhao、Yuanyuan Zhang、Yuanxu Gao、Olivia Monteiro、Kang Zhang、Bo Liu、Shu Wang

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作者单位：

Breast Center,Peking University People's Hospital,Beijing 100044,China

China ESG Institute,Capital University of Economics and Business,Beijing 100070,China

Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China

Department of Pathology,Peking University People's Hospital,Beijing 100044,China

Center for Biomedicine and Innovations,Faculty of Medicine,Macau University of Science and Technology,Macao 999078,China

College of Future Technology,Peking University,Beijing 100091,China

School of Mathematical and Computational Sciences,Massey University,Auckland 0745,New Zealand

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基金：

Capital's Funds for Health Improvement and ResearchBeijing Natural Science FoundationBeijing Municipal Science and Technology ProjectNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaMacau Science and Technology Development Fund,Macao,ChinaMacau Science and Technology Development Fund,Macao,ChinaMacao Young Scholars Program

项目编号：

CHF 2020-2Z-408127242281Z2011000055200812016YFC0901300620760150070/2020/A20003/2021/AKPAM2023024

出版年：

2024

DOI：

10.1016/j.scib.2024.03.061

科学通报(英文版)

中国科学院

科学通报(英文版)

CSTPCD

ISSN：1001-6538

年,卷(期)：2024.69(11)