Feasibility of Ex Vivo Margin Assessment with Hyperspectral Imaging during Breast-Conserving Surgery: From Imaging Tissue Slices to Imaging Lumpectomy Specimen

Kho, Esther; Dashtbozorg, Behdad; Sanders, Joyce; Peeters, Marie‐Jeanne T. F. D. Vrancken; Duijnhoven, Frederieke van; Sterenborg, Henricus J. C. M.; Ruers, Theo J.M.

doi:10.3390/app11198881

Cited by 6 publications

(14 citation statements)

References 27 publications

(46 reference statements)

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“…The simulated height differences were based on the height differences observed in the study of Kho et al 11 . Within a single specimen, height differences of ∼1 cm were observed, and between specimens, height differences are between 1 and 5 cm.…”

Section: Methodsmentioning

confidence: 99%

“…We simulated realistic white noise and added this to all the spectra to make our results more comparable to real-life measurements. From the clinical datasets of the breast 11 and colon 10 obtained previously within our group, a noise estimation was made. The noise estimation was done by taking the difference between a Savitzky–Golay smoothed reflectance spectrum and the unprocessed spectrum 50 .…”

Section: Methodsmentioning

confidence: 99%

“…2170 spectra were present in the combined dataset, of which 857 from fat, 563 from muscle, and 750 from tumor tissue. Using the same setup, Kho et al 11 imaged lumpectomy specimens and slices from resected breast tissue. Only the lumpectomy data were considered for this paper due to the reduced effect of height differences in the slices dataset.…”

Section: Clinical Datamentioning

confidence: 99%

“…By a literature search, we identified eight commonly used preprocessing algorithms applied to medical hyperspectral images: standard normal variate (SNV), 10 – 17 multiplicative scatter correction (MSC), 18 – 20 min-max normalization (MM), 16 , 21 – 29 mean centering (MC), 30 , 31 area under the curve normalization (AUC), 32 – 37 single wavelength normalization (SW), 1 , 38 first derivative (FD), 16 , 18 , 39 – 41 and second derivative (SD) 42 , 43 . The details of each algorithm are discussed in Sec.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, a lower overlap coefficient would likely improve the discrimination ability of tissue classification algorithms. Finally, we investigate the effect of the preprocessing algorithms on clinical data, measured on colon 10 and breast 11 tissues to determine whether the trends we identify based on the synthetic data translate to clinical applications.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

et al. 2022

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Significance: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.Aim: To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.Approach: We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.Conclusions: Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Clinical Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

et al. 2022

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Delineation and detection of breast cancer using novel label-free fluorescence

Mahmoud,

El-Sharkawy

2023

BMC Med Imaging

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Background Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. Methods Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440–460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. Results Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. Conclusions This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.

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Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging

Jong

Kruif

Geldof

et al. 2022

Biomed. Opt. Express

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Achieving an adequate resection margin during breast-conserving surgery remains challenging due to the lack of intraoperative feedback. Here, we evaluated the use of hyperspectral imaging to discriminate healthy tissue from tumor tissue in lumpectomy specimens. We first used a dataset obtained on tissue slices to develop and evaluate three convolutional neural networks. Second, we fine-tuned the networks with lumpectomy data to predict the tissue percentages of the lumpectomy resection surface. A MCC of 0.92 was achieved on the tissue slices and an RMSE of 9% on the lumpectomy resection surface. This shows the potential of hyperspectral imaging to classify the resection margins of lumpectomy specimens.

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Feasibility of Ex Vivo Margin Assessment with Hyperspectral Imaging during Breast-Conserving Surgery: From Imaging Tissue Slices to Imaging Lumpectomy Specimen

Cited by 6 publications

References 27 publications

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

Delineation and detection of breast cancer using novel label-free fluorescence

Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging

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