Development and clinical translation of OTIS: a wide-field OCT imaging device for ex-vivo tissue characterization

Munro, Elizabeth A.; Rempel, David; Danner, Christine; Atchia, Yaaseen; Valic, Michael; Berkeley, Andrew; Davoudi, Bahar; Magnin, Paul A.; Akens, Margarete K.; Done, Susan J.; Kulkarni, Supriya; Leong, Wey-Liang; Wilson, Brian C.

doi:10.1117/12.2218467

Cited by 1 publication

(2 citation statements)

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“…reported an average image acquisition time of

, with a maximum of 27 min. 21 This is comparable to conventional frozen section histological examinations, taking approximate 20 to 30 min, 17 so this device might not reduce the length of a surgical procedure. In addition, it has been reported that the interpretation of optical coherence tomography images suffers from reader variability.…”

Section: Introductionmentioning

confidence: 90%

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Multivariate analysis of breast tissue using optical parameters extracted from a combined time-resolved fluorescence and diffuse reflectance system for tumor margin detection

Dao,

Gohla,

Williams

et al. 2023

J. Biomed. Opt.

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Breast conservation therapy is the preferred technique for treating primary breast cancers. However, breast tumor margins are hard to determine as tumor borders are often ill-defined. As such, there exists a need for a clinically compatible tumor margin detection system.Aim: A combined time-resolved fluorescence and diffuse reflectance (TRF-DR) system has been developed to determine the optical properties of breast tissue. This study aims to improve tissue classification to aid in surgical decision making.Approach: Normal and tumor breast tissue were collected from 80 patients with invasive ductal carcinoma and measured in the optical system. Optical parameters were extracted, and the tissue underwent histopathological examination. In total, 761 adipose, 77 fibroglandular, and 347 tumor spectra were analyzed. Principal component analysis and decision tree modeling were performed using only TRF optical parameters, only DR optical parameters, and using the combined datasets. Results:The classification modeling using TRF data alone resulted in a tumor margin detection sensitivity of 72.3% and specificity of 88.3%. Prediction modeling using DR data alone resulted in greater sensitivity and specificity of 80.4% and 94.0%, respectively. Combining both datasets resulted in the improved sensitivity and specificity of 85.6% and 95.3%, respectively. While both sensitivity and specificity improved with the combined modeling, further study of fibroglandular tissue could result in improved classification. Conclusion:The combined TRF-DR system showed greater tissue classification capability than either technique alone. Further work studying more fibroglandular tissue and tissue of mixed composition would develop this system for intraoperative use for tumor margin detection.

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“…reported an average image acquisition time of

Section: Introductionmentioning

confidence: 90%

“…The pilot study conducted by Munro et al. reported an average image acquisition time of 18.3±5.3 min, with a maximum of 27 min 21 . This is comparable to conventional frozen section histological examinations, taking approximate 20 to 30 min, 17 so this device might not reduce the length of a surgical procedure.…”

Section: Introductionmentioning

confidence: 92%