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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Date
2024-06-15
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B V on behalf of the Science China Press
Rights
(c) 2024 The Author/s
CC BY 4.0
CC BY 4.0
Abstract
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, independent 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 intraoperative 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 intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.
Description
Keywords
Breast neoplasms, Cancer diagnosis, Deep learning, Dynamic full-field optical coherence tomography, Image classification, Humans, Breast Neoplasms, Tomography, Optical Coherence, Deep Learning, Female, Prospective Studies, Middle Aged, Carcinoma, Ductal, Breast, Aged, Adult, Carcinoma, Intraductal, Noninfiltrating, Intraoperative Period
Citation
Zhang S, Yang B, Yang H, Zhao J, Zhang Y, Gao Y, Monteiro O, Zhang K, Liu B, Wang S. (2024). Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.. Sci Bull (Beijing). 69. 11. (pp. 1748-1756).