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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dc.citation.issue11
dc.citation.volume69
dc.contributor.authorZhang S
dc.contributor.authorYang B
dc.contributor.authorYang H
dc.contributor.authorZhao J
dc.contributor.authorZhang Y
dc.contributor.authorGao Y
dc.contributor.authorMonteiro O
dc.contributor.authorZhang K
dc.contributor.authorLiu B
dc.contributor.authorWang S
dc.coverage.spatialNetherlands
dc.date.accessioned2024-11-19T21:59:12Z
dc.date.available2024-11-19T21:59:12Z
dc.date.issued2024-06-15
dc.description.abstractAn 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.
dc.description.confidentialfalse
dc.format.pagination1748-1756
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/38702279
dc.identifier.citationZhang 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).
dc.identifier.doi10.1016/j.scib.2024.03.061
dc.identifier.eissn2095-9281
dc.identifier.elements-typejournal-article
dc.identifier.issn2095-9273
dc.identifier.piiS2095-9273(24)00217-2
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72029
dc.languageeng
dc.publisherElsevier B V on behalf of the Science China Press
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S2095927324002172
dc.relation.isPartOfSci Bull (Beijing)
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBreast neoplasms
dc.subjectCancer diagnosis
dc.subjectDeep learning
dc.subjectDynamic full-field optical coherence tomography
dc.subjectImage classification
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectTomography, Optical Coherence
dc.subjectDeep Learning
dc.subjectFemale
dc.subjectProspective Studies
dc.subjectMiddle Aged
dc.subjectCarcinoma, Ductal, Breast
dc.subjectAged
dc.subjectAdult
dc.subjectCarcinoma, Intraductal, Noninfiltrating
dc.subjectIntraoperative Period
dc.titlePotential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients
dc.typeJournal article
pubs.elements-id488894
pubs.organisational-groupOther
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