查看更多>>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.