Publications by authors named "Henricus Sterenborg"

112 Publications

Optical tissue measurements of invasive carcinoma and ductal carcinoma in situ for surgical guidance.

Breast Cancer Res 2021 05 22;23(1):59. Epub 2021 May 22.

Department of Surgery, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Postbus 90203, 1006, Amsterdam, BE, Netherlands.

Background: Although the incidence of positive resection margins in breast-conserving surgery has decreased, both incomplete resection and unnecessary large resections still occur. This is especially the case in the surgical treatment of ductal carcinoma in situ (DCIS). Diffuse reflectance spectroscopy (DRS), an optical technology based on light tissue interactions, can potentially characterize tissue during surgery thereby guiding the surgeon intraoperatively. DRS has shown to be able to discriminate pure healthy breast tissue from pure invasive carcinoma (IC) but limited research has been done on (1) the actual optical characteristics of DCIS and (2) the ability of DRS to characterize measurements that are a mixture of tissue types.

Methods: In this study, DRS spectra were acquired from 107 breast specimens from 107 patients with proven IC and/or DCIS (1488 measurement locations). With a generalized estimating equation model, the differences between the DRS spectra of locations with DCIS and IC and only healthy tissue were compared to see if there were significant differences between these spectra. Subsequently, different classification models were developed to be able to predict if the DRS spectrum of a measurement location represented a measurement location with "healthy" or "malignant" tissue. In the development and testing of the models, different definitions for "healthy" and "malignant" were used. This allowed varying the level of homogeneity in the train and test data.

Results: It was found that the optical characteristics of IC and DCIS were similar. Regarding the classification of tissue with a mixture of tissue types, it was found that using mixed measurement locations in the development of the classification models did not tremendously improve the accuracy of the classification of other measurement locations with a mixture of tissue types. The evaluated classification models were able to classify measurement locations with > 5% malignant cells with a Matthews correlation coefficient of 0.41 or 0.40. Some models showed better sensitivity whereas others had better specificity.

Conclusion: The results suggest that DRS has the potential to detect malignant tissue, including DCIS, in healthy breast tissue and could thus be helpful for surgical guidance.
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http://dx.doi.org/10.1186/s13058-021-01436-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141169PMC
May 2021

Experimental validation of a recently developed model for single-fiber reflectance spectroscopy.

J Biomed Opt 2021 02;26(2)

University of Amsterdam, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences,, The Netherlands.

Significance: We recently developed a model for the reflectance measured with (multi-diameter) single-fiber reflectance (SFR) spectroscopy as a function of the reduced scattering coefficient μs', the absorption coefficient μa, and the phase function parameter psb. We validated this model with simulations.

Aim: We validate our model experimentally. To prevent overfitting, we investigate the wavelength-dependence of psb and propose a parametrization with only three parameters. We also investigate whether this parametrization enables measurements with a single fiber, as opposed to multiple fibers used in multi-diameter SFR (MDSFR).

Approach: We validate our model on 16 phantoms with two concentrations of Intralipid-20% (μs'=13 and 21  cm  -  1 at 500 nm) and eight concentrations of Evans Blue (μa  =  1 to 20  cm  -  1 at 605 nm). We parametrize psb as 10  -  5  ·    (  p1  (  λ  /  650  )    +  p2(λ/650)2  +  p3(λ/650)3  )  .

Results: Average errors were 7% for μs', 11% for μa, and 16% with the parametrization of psb; and 7%, 17%, and 16%, respectively, without. The parametrization of psb improved the fit speed 25 times (94 s to <4  s). Average errors for only one fiber were 50%, 33%, and 186%, respectively.

Conclusions: Our recently developed model provides accurate results for MDSFR measurements but not for a single fiber. The psb parametrization prevents overfitting and speeds up the fit.
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http://dx.doi.org/10.1117/1.JBO.26.2.025004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913601PMC
February 2021

Toward improved endoscopic surveillance with multidiameter single fiber reflectance spectroscopy in patients with Barrett's esophagus.

J Biophotonics 2021 04 31;14(4):e202000351. Epub 2021 Jan 31.

Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, The Netherlands.

Patients with Barrett's esophagus are at an increased risk to develop esophageal cancer and, therefore, undergo regular endoscopic surveillance. Early detection of neoplasia enables endoscopic treatment, which improves outcomes. However, early Barrett's neoplasia is easily missed during endoscopic surveillance. This study investigates multidiameter single fiber reflectance spectroscopy (MDSFR) to improve Barrett's surveillance. Based on the concept of field cancerization, it may be possible to identify the presence of a neoplastic lesion from measurements elsewhere in the esophagus or even the oral cavity. In this study, MDSFR measurements are performed on non-dysplastic Barrett's mucosa, squamous mucosa, oral mucosa, and the neoplastic lesion (if present). Based on logistic regression analysis on the scattering parameters measured by MDSFR, a classifier is developed that can predict the presence of neoplasia elsewhere in the Barrett's segment from measurements on the non-dysplastic Barrett's mucosa (sensitivity 91%, specificity 71%, AUC = 0.77). Classifiers obtained from logistic regression analysis for the squamous and oral mucosa do not result in an AUC significantly different from 0.5.
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http://dx.doi.org/10.1002/jbio.202000351DOI Listing
April 2021

Subdiffuse scattering and absorption model for single fiber reflectance spectroscopy.

Biomed Opt Express 2020 Nov 22;11(11):6620-6633. Epub 2020 Oct 22.

Amsterdam UMC, University of Amsterdam, Department of Biomedical Engineering and Physics, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.

Single fiber reflectance (SFR) spectroscopy is a technique that is sensitive to small-scale changes in tissue. An additional benefit is that SFR measurements can be performed through endoscopes or biopsy needles. In SFR spectroscopy, a single fiber emits and collects light. Tissue optical properties can be extracted from SFR spectra and related to the disease state of tissue. However, the model currently used to extract optical properties was derived for tissues with modified Henyey-Greenstein phase functions only and is inadequate for other tissue phase functions. Here, we will present a model for SFR spectroscopy that provides accurate results for a large range of tissue phase functions, reduced scattering coefficients, and absorption coefficients. Our model predicts the reflectance with a median error of 5.6% compared to 19.3% for the currently used model. For two simulated tissue spectra, our model fit provides accurate results.
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http://dx.doi.org/10.1364/BOE.402466DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687961PMC
November 2020

Analytical model for diffuse reflectance in single fiber reflectance spectroscopy.

Opt Lett 2020 Apr;45(7):2078-2081

Cancer progression leads to changing scattering properties of affected tissues. Single fiber reflectance (SFR) spectroscopy detects these changes at small spatial scales, making it a promising tool for early in situ detection. Despite its simplicity and versatility, SFR signal modeling is hugely complicated so that, presently, only approximate models exist. We use a classic approach from geometrical probability to derive accurate analytical expressions for diffuse reflectance in SFR that shows a strong improvement over existing models. We consider the case of limited collection efficiency and the presence of absorption. A Monte Carlo light transport study demonstrates that we adequately describe the contribution of diffuse reflectance to the SFR signal. Additional steps are required to include semi-ballistic, non-diffuse reflectance also present in the SFR measurement.
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http://dx.doi.org/10.1364/OL.385845DOI Listing
April 2020

Subdiffuse scattering model for single fiber reflectance spectroscopy.

J Biomed Opt 2020 01;25(1):1-11

Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam Cardiovascular Sciences,, The Netherlands.

To detect small-scale changes in tissue with optical techniques, small sampling volumes are required. Single fiber reflectance (SFR) spectroscopy has a sampling depth of a few hundred micrometers. SFR spectroscopy uses a single fiber to emit and collect light. The only available model to determine optical properties with SFR spectroscopy was derived for tissues with modified Henyey-Greenstein phase functions. Previously, we demonstrated that this model is inadequate for other tissue phase functions. We develop a model to relate SFR measurements to scattering properties for a range of phase functions, in the absence of absorption. Since the source and detector overlap, the reflectance cannot be accurately described by diffusion theory alone: SFR measurements are subdiffuse. Therefore, we describe the reflectance as a combination of a diffuse and a semiballistic component. We use the model of Farrell et al. for the diffuse component, solved for an overlapping source and detector fiber. For the semiballistic component, we derive a new parameter, , which incorporates the integrals of the phase function over 1 deg in the backward direction and 23 deg in the forward direction. Our model predicts the reflectance with a median error of 2.1%, compared to 9.0% for the currently available model.
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http://dx.doi.org/10.1117/1.JBO.25.1.015001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7008500PMC
January 2020

Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra.

Biomed Opt Express 2019 Dec 5;10(12):6096-6113. Epub 2019 Nov 5.

Department of Surgery, Antoni van Leeuwenhoek Hospital - The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.

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.
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http://dx.doi.org/10.1364/BOE.10.006096DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913395PMC
December 2019

Multidiameter single-fiber reflectance spectroscopy of heavily pigmented skin: modeling the inhomogeneous distribution of melanin.

J Biomed Opt 2019 12;24(12):1-11

Amsterdam UMC, University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands.

When analyzing multidiameter single-fiber reflectance (MDSFR) spectra, the inhomogeneous distribution of melanin pigments in skin tissue is usually not accounted for. Especially in heavily pigmented skins, this can result in bad fits and biased estimation of tissue optical properties. A model is introduced to account for the inhomogeneous distribution of melanin pigments in skin tissue. visible MDSFR measurements were performed on heavily pigmented skin of type IV to VI. Skin tissue optical properties and related physiological properties were extracted from the measured spectra using the introduced model. The absorption of melanin pigments described by the introduced model demonstrates a good correlation with the co-localized measurement of the well-known melanin index.
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http://dx.doi.org/10.1117/1.JBO.24.12.127001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7006040PMC
December 2019

Using Diffuse Reflectance Spectroscopy to Distinguish Tumor Tissue From Fibrosis in Rectal Cancer Patients as a Guide to Surgery.

Lasers Surg Med 2020 09 3;52(7):604-611. Epub 2019 Dec 3.

Department of Surgery, Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, 1066 CX, The Netherlands.

Background And Objectives: In patients with rectal cancer who received neoadjuvant (chemo)radiotherapy, fibrosis is induced in and around the tumor area. As tumors and fibrosis have similar visual and tactile feedback, they are hard to distinguish during surgery. To prevent positive resection margins during surgery and spare healthy tissue, it would be of great benefit to have a real-time tissue classification technology that can be used in vivo.

Study Design/materials And Methods: In this study diffuse reflectance spectroscopy (DRS) was evaluated for real-time tissue classification of tumor and fibrosis. DRS spectra of fibrosis and tumor were obtained on excised rectal specimens. After normalization using the area under the curve, a support vector machine was trained using a 10-fold cross-validation.

Results: Using spectra of pure tumor tissue and pure fibrosis tissue, we obtained a mean accuracy of 0.88. This decreased to a mean accuracy of 0.61 when tumor measurements were used in which a layer of healthy tissue, mainly fibrosis, was present between the tumor and the measurement surface.

Conclusion: It is possible to distinguish pure fibrosis from pure tumor. However, when the measurements on tumor also involve fibrotic tissue, the classification accuracy decreases. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/lsm.23196DOI Listing
September 2020

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information.

Biomed Opt Express 2019 Sep 7;10(9):4496-4515. Epub 2019 Aug 7.

Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650 nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome.
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http://dx.doi.org/10.1364/BOE.10.004496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757478PMC
September 2019

Toward assessment of resection margins using hyperspectral diffuse reflection imaging (400-1,700 nm) during tongue cancer surgery.

Lasers Surg Med 2020 07 15;52(6):496-502. Epub 2019 Sep 15.

Department of Surgery, Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, The Netherlands.

Background And Objectives: There is a clinical need to assess the resection margins of tongue cancer specimens, intraoperatively. In the current ex vivo study, we evaluated the feasibility of hyperspectral diffuse reflectance imaging (HSI) for distinguishing tumor from the healthy tongue tissue.

Study Design/materials And Methods: Fresh surgical specimens (n = 14) of squamous cell carcinoma of the tongue were scanned with two hyperspectral cameras that cover the visible and near-infrared spectrum (400-1,700 nm). Each pixel of the hyperspectral image represents a measure of the diffuse optical reflectance. A neural network was used for tissue-type prediction of the hyperspectral images of the visual and near-infrared data sets separately as well as both data sets combined.

Results: HSI was able to distinguish tumor from muscle with a good accuracy. The diagnostic performance of both wavelength ranges (sensitivity/specificity of visual and near-infrared were 84%/80% and 77%/77%, respectively) appears to be comparable and there is no additional benefit of combining the two wavelength ranges (sensitivity and specificity were 83%/76%).

Conclusions: HSI has a strong potential for intra-operative assessment of tumor resection margins of squamous cell carcinoma of the tongue. This may optimize surgery, as the entire resection surface can be scanned in a single run and the results can be readily available. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/lsm.23161DOI Listing
July 2020

Method for coregistration of optical measurements of breast tissue with histopathology: the importance of accounting for tissue deformations.

J Biomed Opt 2019 07;24(7):1-12

The Netherlands Cancer Institute, The Netherlands.

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use.
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http://dx.doi.org/10.1117/1.JBO.24.7.075002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6995961PMC
July 2019

Imaging depth variations in hyperspectral imaging: Development of a method to detect tumor up to the required tumor-free margin width.

J Biophotonics 2019 11 23;12(11):e201900086. Epub 2019 Jul 23.

Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands.

Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.
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http://dx.doi.org/10.1002/jbio.201900086DOI Listing
November 2019

Modeling subdiffusive light scattering by incorporating the tissue phase function and detector numerical aperture (Erratum).

J Biomed Opt 2019 06;24(6):1-2

Amsterdam UMC, Univ. of Amsterdam, Netherlands.

This erratum corrects an error in "Modeling subdiffusive light scattering by incorporating the tissue phase function and detector numerical aperture."
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http://dx.doi.org/10.1117/1.JBO.24.6.069801DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6977397PMC
June 2019

Refractive index measurement using single fiber reflectance spectroscopy.

J Biophotonics 2019 07 3;12(7):e201900019. Epub 2019 Apr 3.

Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.

A method using single fiber reflectance spectroscopy to measure the refractive indices of transparent and turbid media over a broad wavelength range is presented and tested. For transparent liquid samples, the accuracy is within 0.2%, and the accuracy increases with increasing wavelength. For liquid turbid media, the accuracy is within 0.3% and increases with decreasing wavelength. For solid turbid samples, such as human skin, the accuracy critically depends on the optical contact between the fiber and sample surface. It is demonstrated that this technique has the potential to measure refractive indices of biological tissue in vivo.
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http://dx.doi.org/10.1002/jbio.201900019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7065624PMC
July 2019

Hyperspectral Imaging for Resection Margin Assessment during Cancer Surgery.

Clin Cancer Res 2019 06 18;25(12):3572-3580. Epub 2019 Mar 18.

Department of Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands.

Purpose: Complete tumor removal during cancer surgery remains challenging due to the lack of accurate techniques for intraoperative margin assessment. This study evaluates the use of hyperspectral imaging for margin assessment by reporting its use in fresh human breast specimens.

Experimental Design: Hyperspectral data were first acquired on tissue slices from 18 patients after gross sectioning of the resected breast specimen. This dataset, which contained over 22,000 spectra, was well correlated with histopathology and was used to develop a support vector machine classification algorithm and test the classification performance. In addition, we evaluated hyperspectral imaging in clinical practice by imaging the resection surface of six lumpectomy specimens. With the developed classification algorithm, we determined if hyperspectral imaging could detect malignancies in the resection surface.

Results: The diagnostic performance of hyperspectral imaging on the tissue slices was high; invasive carcinoma, ductal carcinoma , connective tissue, and adipose tissue were correctly classified as tumor or healthy tissue with accuracies of 93%, 84%, 70%, and 99%, respectively. These accuracies increased with the size of the area, consisting of one tissue type. The entire resection surface was imaged within 10 minutes, and data analysis was performed fast, without the need of an experienced operator. On the resection surface, hyperspectral imaging detected 19 of 20 malignancies that, according to the available histopathology information, were located within 2 mm of the resection surface.

Conclusions: These findings show the potential of using hyperspectral imaging for margin assessment during breast-conserving surgery to improve surgical outcome.
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http://dx.doi.org/10.1158/1078-0432.CCR-18-2089DOI Listing
June 2019

Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery.

J Biomed Opt 2019 01;24(1):1-9

Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands.

In the last decades, laparoscopic surgery has become the gold standard in patients with colorectal cancer. To overcome the drawback of reduced tactile feedback, real-time tissue classification could be of great benefit. In this ex vivo study, hyperspectral imaging (HSI) was used to distinguish tumor tissue from healthy surrounding tissue. A sample of fat, healthy colorectal wall, and tumor tissue was collected per patient and imaged using two hyperspectral cameras, covering the wavelength range from 400 to 1700 nm. The data were randomly divided into a training (75%) and test (25%) set. After feature reduction, a quadratic classifier and support vector machine were used to distinguish the three tissue types. Tissue samples of 32 patients were imaged using both hyperspectral cameras. The accuracy to distinguish the three tissue types using both hyperspectral cameras was 0.88 (STD  =  0.13) on the test dataset. When the accuracy was determined per patient, a mean accuracy of 0.93 (STD  =  0.12) was obtained on the test dataset. This study shows the potential of using HSI in colorectal cancer surgery for fast tissue classification, which could improve clinical outcome. Future research should be focused on imaging entire colon/rectum specimen and the translation of the technique to an intraoperative setting.
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http://dx.doi.org/10.1117/1.JBO.24.1.016002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985687PMC
January 2019

Towards the use of diffuse reflectance spectroscopy for real-time in vivo detection of breast cancer during surgery.

J Transl Med 2018 12 19;16(1):367. Epub 2018 Dec 19.

Department of Surgery, the Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, Postbus 90203, 1066 CX, Amsterdam, The Netherlands.

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.
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http://dx.doi.org/10.1186/s12967-018-1747-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299954PMC
December 2018

In vivo nerve identification in head and neck surgery using diffuse reflectance spectroscopy.

Laryngoscope Investig Otolaryngol 2018 Oct 9;3(5):349-355. Epub 2018 Aug 9.

Department of Surgery The Netherlands Cancer Institute-Antoni van Leeuwenhoek Amsterdam the Netherlands.

Background: Careful identification of nerves during head and neck surgery is essential to prevent nerve damage. Currently, nerves are identified based on anatomy and appearance, optionally combined with electromyography (EMG). In challenging cases, nerve damage is reported in up to 50%. Recently, optical techniques, like diffuse reflectance spectroscopy (DRS) and fluorescence spectroscopy (FS) show potential to improve nerve identification.

Methods: 212 intra-operative DRS/FS measurements were performed. Small nerve branches (1-3 mm), on near-nerve adipose tissue, muscle and subcutaneous fat were measured during 11 surgical procedures. Tissue identification was based on quantified concentrations of optical absorbers and scattering parameters.

Results: Clinically comprehensive parameters showed significant differences (<0.05) between the tissues. Classification using k-Nearest Neighbor resulted in 100% sensitivity and a specificity of 83% (accuracy 91%), for the identification of nerve against surrounding tissues.

Conclusions: DRS/FS is a potentially useful intraoperative tool for identification of nerves from adjacent tissues.

Level Of Evidence: Observational proof of principle study.
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http://dx.doi.org/10.1002/lio2.174DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209613PMC
October 2018

Letter to the editor regarding 'Improvement of local microcirculation through intermittent Negative Pressure Wound Therapy (NPWT)'.

J Tissue Viability 2019 02 24;28(1):46-47. Epub 2018 Oct 24.

Department of Surgery, Amsterdam Gastroenterology and Metabolism, Amsterdam Infection & Immunity, Amsterdam UMC, University of Amsterdam, the Netherlands. Electronic address:

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http://dx.doi.org/10.1016/j.jtv.2018.10.002DOI Listing
February 2019

Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe.

J Biomed Opt 2018 10;23(12):1-8

Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Amsterdam, The Netherlands.

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 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- and second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.
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http://dx.doi.org/10.1117/1.JBO.23.12.121611DOI Listing
October 2018

Nerve detection during surgery: optical spectroscopy for peripheral nerve localization.

Lasers Med Sci 2018 Apr 2;33(3):619-625. Epub 2018 Feb 2.

Department of Surgery, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.

Precise nerve localization is of major importance in both surgery and regional anesthesia. Optically based techniques can identify tissue through differences in optical properties, like absorption and scattering. The aim of this study was to evaluate the potential of optical spectroscopy (diffuse reflectance spectroscopy) for clinical nerve identification in vivo. Eighteen patients (8 male, 10 female, age 53 ± 13 years) undergoing inguinal lymph node resection or resection or a soft tissue tumor in the groin were included to measure the femoral or sciatic nerve and the surrounding tissues. In vivo optical measurements were performed using Diffuse Reflectance Spectroscopy (400-1600 nm) on nerve, near nerve adipose tissue, muscle, and subcutaneous fat using a needle-shaped probe. Model-based analyses were used to derive verified quantitative parameters as concentrations of optical absorbers and several parameters describing scattering. A total of 628 optical spectra were recorded. Measured spectra reveal noticeable tissue specific characteristics. Optical absorption of water, fat, and oxy- and deoxyhemoglobin was manifested in the measured spectra. The parameters water and fat content showed significant differences (P < 0.005) between nerve and all surrounding tissues. Classification using k-Nearest Neighbor based on the derived parameters revealed a sensitivity of 85% and a specificity of 79%, for identifying nerve from surrounding tissues. Diffuse Reflectance Spectroscopy identifies peripheral nerve bundles. The differences found between tissue groups are assignable to the tissue composition and structure.
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http://dx.doi.org/10.1007/s10103-017-2433-1DOI Listing
April 2018

Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment.

J Biomed Opt 2017 12;23(2):1-10

Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands.

The biannual International Conference on Biophotonics was recently held on April 30 to May 1, 2017, in Fremantle, Western Australia. This continuing conference series brought together key opinion leaders in biophotonics to present their latest results and, importantly, to participate in discussions on the future of the field and what opportunities exist when we collectively work together for using biophotonics for biological discovery and medical applications. One session in this conference, entitled "Tumor Margin Identification: Critiquing Technologies," challenged invited speakers and attendees to review and critique representative label-free optical imaging technologies and their application for intraoperative assessment and guidance in surgical oncology. We are pleased to share a summary in this outlook paper, with the intent to motivate more research inquiry and investigations, to challenge these and other optical imaging modalities to evaluate and improve performance, to spur translation and adoption, and ultimately, to improve the care and outcomes of patients.
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http://dx.doi.org/10.1117/1.JBO.23.2.021104DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5747261PMC
December 2017

Nerve detection using optical spectroscopy, an evaluation in four different models: In human and swine, in-vivo, and post mortem.

Lasers Surg Med 2018 03 21;50(3):253-261. Epub 2017 Nov 21.

Department of Surgery, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

Objective: Identification of peripheral nerve tissue is crucial in both surgery and regional anesthesia. Recently, optical tissue identification methods are presented to facilitate nerve identification in transcutaneous procedures and surgery. Optimization and validation of such techniques require large datasets. The use of alternative models to human in vivo, like human post mortem, or swine may be suitable to test, optimize and validate new optical techniques. However, differences in tissue characteristics and thus optical properties, like oxygen saturation and tissue perfusion are to be expected. This requires a structured comparison between the models.

Study Design: Comparative observational study.

Methods: Nerve and surrounding tissues in human (in vivo and post mortem) and swine (in vivo and post mortem) were structurally compared macroscopically, histologically, and spectroscopically. Diffuse reflective spectra were acquired (400-1,600 nm) after illumination with a broad band halogen light. An analytical model was used to quantify optical parameters including concentrations of optical absorbers.

Results: Several differences were found histologically and in the optical parameters. Histologically nerve and adipose tissue (subcutaneous fat and sliding fat) showed clear similarities between human and swine while human muscle enclosed more adipocytes and endomysial collagen. Optical parameters revealed model dependent differences in concentrations of β-carotene, water, fat, and oxygen saturation. The similarity between optical parameters is, however, sufficient to yield a strong positive correlation after cross model classification.

Conclusion: This study shows and discusses similarities and differences in nerve and surrounding tissues between human in vivo and post mortem, and swine in vivo and post mortem; this could support the discussion to use an alternative model to optimize and validate optical techniques for clinical nerve identification. Lasers Surg. Med. 50:253-261, 2018. © 2017 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/lsm.22755DOI Listing
March 2018

Single fiber reflectance spectroscopy calibration.

J Biomed Opt 2017 Oct;22(10):1-4

Academic Medical Center, University of Amsterdam, Department of Biomedical Engineering and Physics,, The Netherlands.

To accurately determine sample optical properties using single fiber reflectance spectroscopy (SFR), an absolute calibration of the reflectance is required. We investigated two SFR calibration methods, using a calibrated mirror and using the Fresnel reflection at the fiber tip as a reference. We compared these to commonly used calibration methods, using either Intralipid-20% in combination with Monte Carlo simulations or Spectralon as a reference. The Fresnel reflection method demonstrated the best reproducibility and yielded the most reliable result. We therefore recommend the Fresnel reflection method for the measured absolute reflectance calibration of SFR.
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http://dx.doi.org/10.1117/1.JBO.22.10.100502DOI Listing
October 2017

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery.

J Biomed Opt 2017 Oct;22(10):1-6

Antoni van Leeuwenhoek Hospital-The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands.

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to differentiate tumor tissue from healthy surrounding tissues in patients with colorectal neoplasia. DRS spectra were obtained from tumor tissue, healthy colon, or rectal wall and fat tissue, for every patient. Data were randomly divided into training (80%) and test (20%) sets. After spectral band selection, the spectra were classified using a quadratic classifier and a linear support vector machine. Of the 38 included patients, 36 had colorectal cancer and 2 had an adenoma. When the classifiers were applied to the test set, colorectal cancer could be discriminated from healthy tissue with an overall accuracy of 0.95 (±0.03). This study demonstrates the possibility to separate colorectal cancer from healthy surrounding tissue by applying DRS. High classification accuracies were obtained both in homogeneous and inhomogeneous tissues. This is a fundamental step toward the development of a tool for real-time in vivo tissue assessment during colorectal surgery.
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http://dx.doi.org/10.1117/1.JBO.22.10.106014DOI Listing
October 2017

Modeling subdiffusive light scattering by incorporating the tissue phase function and detector numerical aperture.

J Biomed Opt 2017 05;22(5):50501

University of Amsterdam, Academic Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands.

To detect small-scale changes in tissue with optical techniques, small sampling volumes and, therefore, short source–detector separations are required. In this case, reflectance measurements are not adequately described by the diffusion approximation. Previous studies related subdiffusive reflectance to ? or ? , which parameterize the phase function. Recently, it was demonstrated that ? predicts subdiffusive reflectance better than ? , and that ? becomes less predictive for lower numerical apertures (NAs). We derive and evaluate the parameter R p NA , which incorporates the NA of the detector and the integral of the phase function over the NA in the backward and forward directions. Monte Carlo simulations are performed for overlapping source/detector geometries for a range of phase functions, reduced scattering coefficients, NAs, and source/detector diameters. R p NA improves prediction of the measured reflectance compared to ? and ? . It is, therefore, expected that R p NA will improve derivation of optical properties from subdiffusive measurements.
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http://dx.doi.org/10.1117/1.JBO.22.5.050501DOI Listing
May 2017

Using DRS during breast conserving surgery: identifying robust optical parameters and influence of inter-patient variation.

Biomed Opt Express 2016 Dec 17;7(12):5188-5200. Epub 2016 Nov 17.

Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam The Netherlands; MIRA Institute, University Twente, The Netherlands.

Successful breast conserving surgery consists of complete removal of the tumor while sparing healthy surrounding tissue. Despite currently available imaging and margin assessment tools, recognizing tumor tissue at a resection margin during surgery is challenging. Diffuse reflectance spectroscopy (DRS), which uses light for tissue characterization, can potentially guide surgeons to prevent tumor positive margins. However, inter-patient variation and changes in tissue physiology occurring during the resection might hamper this light-based technology. Here we investigate how inter-patient variation and tissue status ( vs ) affect the performance of the DRS optical parameters. and measurements of 45 breast cancer patients were obtained and quantified with an analytical model to acquire the optical parameters. The optical parameter representing the ratio between fat and water provided the best discrimination between normal and tumor tissue, with an area under the receiver operating characteristic curve of 0.94. There was no substantial influence of other patient factors such as menopausal status on optical measurements. Contrary to expectations, normalization of the optical parameters did not improve the discriminative power. Furthermore, measurements taken were not significantly different from the measurements taken . These findings indicate that DRS is a robust technology for the detection of tumor tissue during breast conserving surgery.
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http://dx.doi.org/10.1364/BOE.7.005188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5175562PMC
December 2016

Review: in vivo optical spectral tissue sensing-how to go from research to routine clinical application?

Lasers Med Sci 2017 Apr 2;32(3):711-719. Epub 2016 Dec 2.

Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, Amsterdam, The Netherlands.

Innovations in optical spectroscopy have helped the technology reach a point where performance previously seen only in laboratory settings can be translated and tested in real-world applications. In the field of oncology, spectral tissue sensing (STS) by means of optical spectroscopy is considered to have major potential for improving diagnostics and optimizing treatment outcome. The concept has been investigated for more than two decades and yet spectral tissue sensing is not commonly employed in routine medical practice. It is therefore important to understand what is needed to translate technological advances and insights generated through basic scientific research in this field into clinical practice. The aim of the discussion presented here is not to provide a comprehensive review of all work published over the last decades but rather to highlight some of the challenges found in literature and encountered by our group in the quest to translate optical technologies into useful clinical tools. Furthermore, an outlook is proposed on how translational researchers could proceed to eventually have STS incorporated in the process of clinical decision-making.
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http://dx.doi.org/10.1007/s10103-016-2119-0DOI Listing
April 2017

In vivo characterization of colorectal metastases in human liver using diffuse reflectance spectroscopy: toward guidance in oncological procedures.

J Biomed Opt 2016 09;21(9):97004

Netherlands Cancer Institute, Department of Surgery, Plesmanlaan 121, 1066CX Amsterdam, The NetherlandsfUniversity of Twente, MIRA Institute, Drienerlolaan 5, Zuidhorst ZH116, 7522 NB Enschede, The Netherlands.

There is a strong need to develop clinical instruments that can perform rapid tissue assessment at the tip of smart clinical instruments for a variety of oncological applications. This study presents the first in vivo real-time tissue characterization during 24 liver biopsy procedures using diffuse reflectance (DR) spectroscopy at the tip of a core biopsy needle with integrated optical fibers. DR measurements were performed along each needle path, followed by biopsy of the target lesion using the same needle. Interventional imaging was coregistered with the DR spectra. Pathology results were compared with the DR spectroscopy data at the final measurement position. Bile was the primary discriminator between normal liver tissue and tumor tissue. Relative differences in bile content matched with the tissue diagnosis based on histopathological analysis in all 24 clinical cases. Continuous DR measurements during needle insertion in three patients showed that the method can also be applied for biopsy guidance or tumor recognition during surgery. This study provides an important validation step for DR spectroscopy-based tissue characterization in the liver. Given the feasibility of the outlined approach, it is also conceivable to make integrated fiber-optic tools for other clinical procedures that rely on accurate instrument positioning.
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http://dx.doi.org/10.1117/1.JBO.21.9.097004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357329PMC
September 2016
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