Abstract:This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2… Show more
“…() using the obtained results for and µ a from the DRS measurements and resulted in an Rd ABS value of 0.81. These DRS measurements are associated with some degree of uncertainty . Rd was therefore set as a fit parameter to match the fluence rate as measured by all isotropic detectors ( i.e.…”
Section: Resultsmentioning
confidence: 99%
“…Three solid phantoms were printed in white ABS (Materialise, Leuven, Belgium). The optical properties, with respect to the reduced scattering coefficients, approximate that of tissue and were determined by three sequential spatially resolved DRS measurements on a solid bulky section of the phantom . In addition, DRS measurements were performed on a volunteer’s oral buccal mucosa.…”
Previous dosimetric studies during photodynamic therapy (PDT) of superficial lesions within a cavity such as the nasopharynx, demonstrated significant intra‐ and interpatient variations in fluence rate build‐up as a result of tissue surface re‐emitted and reflected photons, which depends on the optical properties. This scattering effect affects the response to PDT. Recently, a meta‐tetra(hydroxyphenyl)chlorin‐mediated PDT study of malignancies in the paranasal sinuses after salvage surgery was initiated. These geometries are complex in shape, with spatially varying optical properties. Therefore, preplanning and in vivo dosimetry is required to ensure an effective fluence delivered to the tumor. For this purpose, two 3D light distribution models were developed: first, a simple empirical model that directly calculates the fluence rate at the cavity surface using a simple linear function that includes the scatter contribution as function of the light source to surface distance. And second, an analytical model based on Lambert’s cosine law assuming a global diffuse reflectance constant. The models were evaluated by means of three 3D printed optical phantoms and one porcine tissue phantom. Predictive fluence rate distributions of both models are within ± 20% accurate and have the potential to determine the optimal source location and light source output power settings.
“…() using the obtained results for and µ a from the DRS measurements and resulted in an Rd ABS value of 0.81. These DRS measurements are associated with some degree of uncertainty . Rd was therefore set as a fit parameter to match the fluence rate as measured by all isotropic detectors ( i.e.…”
Section: Resultsmentioning
confidence: 99%
“…Three solid phantoms were printed in white ABS (Materialise, Leuven, Belgium). The optical properties, with respect to the reduced scattering coefficients, approximate that of tissue and were determined by three sequential spatially resolved DRS measurements on a solid bulky section of the phantom . In addition, DRS measurements were performed on a volunteer’s oral buccal mucosa.…”
Previous dosimetric studies during photodynamic therapy (PDT) of superficial lesions within a cavity such as the nasopharynx, demonstrated significant intra‐ and interpatient variations in fluence rate build‐up as a result of tissue surface re‐emitted and reflected photons, which depends on the optical properties. This scattering effect affects the response to PDT. Recently, a meta‐tetra(hydroxyphenyl)chlorin‐mediated PDT study of malignancies in the paranasal sinuses after salvage surgery was initiated. These geometries are complex in shape, with spatially varying optical properties. Therefore, preplanning and in vivo dosimetry is required to ensure an effective fluence delivered to the tumor. For this purpose, two 3D light distribution models were developed: first, a simple empirical model that directly calculates the fluence rate at the cavity surface using a simple linear function that includes the scatter contribution as function of the light source to surface distance. And second, an analytical model based on Lambert’s cosine law assuming a global diffuse reflectance constant. The models were evaluated by means of three 3D printed optical phantoms and one porcine tissue phantom. Predictive fluence rate distributions of both models are within ± 20% accurate and have the potential to determine the optimal source location and light source output power settings.
“…Diffuse reflectance spectroscopy (DRS) is an optical technique that has been used for tissue recognition in cancer diagnostics [3][4][5][6][7][8][9][10]. Light over a broad wavelength range is sent into the tissue, where the light interacts by scattering and absorption.…”
Section: Introductionmentioning
confidence: 99%
“…First of all, direct comparison of single features or simple combinations of features, has been performed to distinguish different tissues [4,[30][31][32][33][34]. In addition, several machine learning techniques have been used for the tissue classification such as classification and regression tree algorithms [5,6,[35][36][37], linear discriminant analysis (LDA) [24,[38][39][40] and support vector machines (SVM) [3,20,26,37,38,[41][42][43].…”
Diffuse reflectance spectroscopy can be used in colorectal cancer surgery for tissue classification. The main challenge in the classification task is to separate healthy colorectal wall from tumor tissue. In this study, four normalization techniques, four feature extraction methods and five classifiers are applied to nine datasets, to obtain the optimal method to separate spectra measured on healthy colorectal wall from spectra measured on tumor tissue. All results are compared to the use of the entire non-normalized spectra. It is found that the most optimal classification approach is to apply a feature extraction method on non-normalized spectra combined with support vector machine or neural network classifier.
“…DRS has already been successfully used in several different cancer types to distinguish between tumor and healthy tissue, e.g. in lung, breast, liver and head and neck cancer with accuracies of at least 77% [8–13]. Fig.…”
Background
In colorectal cancer surgery there is a delicate balance between complete removal of the tumor and sparing as much healthy tissue as possible. Especially in rectal cancer, intraoperative tissue recognition could be of great benefit in preventing positive resection margins and sparing as much healthy tissue as possible. To better guide the surgeon, we evaluated the accuracy of diffuse reflectance spectroscopy (DRS) for tissue characterization during colorectal cancer surgery and determined the added value of DRS when compared to clinical judgement.
Methods
DRS spectra were obtained from fat, healthy colorectal wall and tumor tissue during colorectal cancer surgery and results were compared to histopathology examination of the measurement locations. All spectra were first normalized at 800 nm, thereafter two support vector machines (SVM) were trained using a tenfold cross-validation. With the first SVM fat was separated from healthy colorectal wall and tumor tissue, the second SVM distinguished healthy colorectal wall from tumor tissue.
Results
Patients were included based on preoperative imaging, indicating advanced local stage colorectal cancer. Based on the measurement results of 32 patients, the classification resulted in a mean accuracy for fat, healthy colorectal wall and tumor of 0.92, 0.89 and 0.95 respectively. If the classification threshold was adjusted such that no false negatives were allowed, the percentage of false positive measurement locations by DRS was 25% compared to 69% by clinical judgement.
Conclusion
This study shows the potential of DRS for the use of tissue classification during colorectal cancer surgery. Especially the low false positive rate obtained for a false negative rate of zero shows the added value for the surgeons.
Trail registration This trail was performed under approval from the internal review board committee (Dutch Trail Register NTR5315), registered on 04/13/2015, https://www.trialregister.nl/trial/5175.
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