Dataset Information

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery.

ABSTRACT:

Significance

Wavelength selection from a large diffuse reflectance spectroscopy (DRS) dataset enables removal of spectral multicollinearity and thus leads to improved understanding of the feature domain. Feature selection (FS) frameworks are essential to discover the optimal wavelengths for tissue differentiation in DRS-based measurements, which can facilitate the development of compact multispectral optical systems with suitable illumination wavelengths for clinical translation.

Aim

The aim was to develop an FS methodology to determine wavelengths with optimal discriminative power for orthopedic applications, while providing the frameworks for adaptation to other clinical scenarios.

Approach

An ensemble framework for FS was developed, validated, and compared with frameworks incorporating conventional algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), and backward interval partial least squares (biPLS).

Results

Via the one-versus-rest binary classification approach, a feature subset of 10 wavelengths was selected from each framework yielding comparable balanced accuracy scores (PCA: 94.8±3.47%, LDA: 98.2±2.02%, biPLS: 95.8±3.04%, and ensemble: 95.8±3.16%) to those of using all features (100%) for cortical bone versus the rest class labels. One hundred percent balanced accuracy scores were generated for bone cement versus the rest. Different feature subsets achieving similar outcomes could be identified due to spectral multicollinearity.

Conclusions

Wavelength selection frameworks provide a means to explore domain knowledge and discover important contributors to classification in spectroscopy. The ensemble framework generated a model with improved interpretability and preserved physical interpretation, which serves as the basis to determine illumination wavelengths in optical instrumentation design.

SUBMITTER: Li CL

PROVIDER: S-EPMC10479945 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery.

Li Celina L CL Fisher Carl J CJ Komolibus Katarzyna K Grygoryev Konstantin K Lu Huihui H Burke Ray R Visentin Andrea A Andersson-Engels Stefan S

Journal of biomedical optics 20230905 12

<h4>Significance</h4>Wavelength selection from a large diffuse reflectance spectroscopy (DRS) dataset enables removal of spectral multicollinearity and thus leads to improved understanding of the feature domain. Feature selection (FS) frameworks are essential to discover the optimal wavelengths for tissue differentiation in DRS-based measurements, which can facilitate the development of compact multispectral optical systems with suitable illumination wavelengths for clinical translation.<h4>Aim< ...[more]

PMID: 37674977

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Project description:SignificanceFiber-optic extended-wavelength diffuse reflectance spectroscopy (EWDRS) using both visible/near-infrared and shortwave-infrared detectors enables improved detection of spectral absorbances arising from lipids, water, and collagen and has demonstrated promise in a variety of applications, including detection of nerves and neurovascular bundles (NVB). Development of future applications of EWDRS for nerve detection could benefit from the use of model-based analyses including Monte Carlo (MC) simulations and evaluation of agreement between model systems and empirical measurements.AimThe aim of this work is to characterize agreement between EWDRS measurements and simulations and inform future applications of model-based studies of nerve-detecting applications.ApproachA model-based platform consisting of an ex vivo microsurgical nerve dissection model, unique two-layer optical phantoms, and MC model simulations of fiber-optic EWDRS spectroscopic measurements were used to characterize EWDRS and compare agreement across models. In addition, MC simulations of an EWDRS measurement scenario are performed to provide a representative example of future analyses.ResultsEWDRS studies performed in the common chicken thigh femoral nerve microsurgical dissection model indicate similar spectral features for classification of NVB versus adjacent tissues as reported in porcine models and human subjects. A comparison of measurements from unique EWDRS issue mimicking optical phantoms and MC simulations indicates high agreement between the two in homogeneous and two-layer optical phantoms, as well as in dissected tissues. Finally, MC simulations of measurement over a simulated NVB indicate the potential of future applications for measurement of nerve plexus.ConclusionsCharacterization of agreement between fiber-optic EWDRS measurements and MC simulations demonstrates strong agreement across a variety of tissues and optical phantoms, offering promise for further use to guide the continued development of EWDRS for translational applications.

Project description:BACKGROUND:Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real-time in vivo tissue characterization. METHODS:Evaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real-time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements. RESULTS:With DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen. CONCLUSIONS:It was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery. Trail registration NIH US National Library of Medicine-clinicaltrails.gov, NCT01730365. Registered: 10/04/2012 https://clinicaltrials.gov/ct2/show/study/NCT01730365.

Dataset Information

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery.

Significance

Aim

Approach

Results

Conclusions

Publications

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery.

Similar Datasets

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