Cancer Medicine (Oct 2023)

Value of high‐resolution full‐field optical coherence tomography and dynamic cell imaging for one‐stop rapid diagnosis breast clinic

  • Alexis Simon,
  • Yasmina Badachi,
  • Jacques Ropers,
  • Isaura Laurent,
  • Lida Dong,
  • Elisabeth Da Maia,
  • Agnès Bourcier,
  • Geoffroy Canlorbe,
  • Catherine Uzan

DOI
https://doi.org/10.1002/cam4.6560
Journal volume & issue
Vol. 12, no. 19
pp. 19500 – 19511

Abstract

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Abstract Background Full‐field optical coherence tomography combined with dynamic cell imaging (D‐FFOCT) is a new, simple‐to‐use, nondestructive, quick technique that can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D‐FFOCT for one‐stop rapid diagnosis breast clinic. Methods Dynamic full‐field optical coherence tomography was applied to fresh, untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon, and pathologist) analyzed the samples without knowing final histological diagnosis or American College of Radiology classification. The results were compared to conventional processing and staining (hematoxylin–eosin). Results A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49.27%), 99 benign lesions (48.29%), 3 ductal in situ carcinoma (1.46%), and 2 atypias (0.98%). The diagnostic performance results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78], and NPV: 75% [0.72;0.78]. A large image atlas was created as well as a diagnosis algorithm from the readers' experience. Conclusion With 74% PPV and 75% NPV, D‐FFOCT is not yet ready to be used in clinical practice to identify breast cancer. This is mainly explained by the lack of experience and knowledge of this new technic by the four lectors. By training with the diagnosis algorithm and the image atlas, radiologists could have better outcomes allowing quick detection of breast cancer and lymph node involvement. Deep learning could also be used, and further investigation will follow.

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