Assessment of the deep resection margin during oral cancer surgery: A systematic review

The main challenge for radical resection in oral cancer surgery is to obtain adequate resection margins. Especially the deep margin, which can only be estimated based on palpation during surgery, is often reported inadequate. To increase the percentage of radical resections, there is a need for a quick, easy, minimal invasive method, which assesses the deep resection margin without interrupting or prolonging surgery. This systematic review provides an overview of technologies that are currently being studied with the aim of fulfilling this demand. A literature search was conducted through the databases Medline, Embase and the Cochrane Library. A total of 62 studies were included. The results were categorized according to the type of technique: 'Frozen Section Analysis', 'Fluorescence', 'Optical Imaging', 'Conventional imaging techniques', and 'Cytological assessment'. This systematic review gives for each technique an overview of the reported performance (accuracy, sensitivity, specificity, positive predictive value, negative predictive value, or a different outcome measure), acquisition time, and sampling depth. At the moment, the most prevailing technique remains frozen section analysis. In the search for other assessment methods to evaluate the deep resection margin, some technologies are very promising for future use when effectiveness has been shown in larger trials, e.g., fluorescence (real-time, sampling depth up to 6 mm) or optical techniques such as hyperspectral imaging (real-time, sampling depth few mm) for microscopic margin assessment and ultrasound (less than 10 min, sampling depth several cm) for assessment on a macroscopic scale.

Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation

OBJECTIVE

The utilization of hyperspectral imaging (HSI) in real-time tumor segmentation during a surgery have recently received much attention, but it remains a very challenging task.

METHODS

In this work, we propose semantic segmentation methods, and compare them with other relevant deep learning algorithms for tongue tumor segmentation. To the best of our knowledge, this is the first work using deep learning semantic segmentation for tumor detection in HSI data using channel selection, and accounting for more spatial tissue context, and global comparison between the prediction map, and the annotation per sample. Results, and Conclusion: On a clinical data set with tongue squamous cell carcinoma, our best method obtains very strong results of average dice coefficient, and area under the ROC-curve of [Formula: see text], and [Formula: see text], respectively on the original spatial image size. The results show that a very good performance can be achieved even with a limited amount of data. We demonstrate that important information regarding tumor decision is encoded in various channels, but some channel selection, and filtering is beneficial over the full spectra. Moreover, we use both visual (VIS), and near-infrared (NIR) spectrum, rather than commonly used only VIS spectrum; although VIS spectrum is generally of higher significance, we demonstrate NIR spectrum is crucial for tumor capturing in some cases.

SIGNIFICANCE

The HSI technology augmented with accurate deep learning algorithms has a huge potential to be a promising alternative to digital pathology or a doctors' supportive tool in real-time surgeries.

Electromagnetic surgical navigation in patients undergoing mandibular surgery

The purpose of this study was to evaluate the feasibility of electromagnetic (EM) navigation for guidance on osteotomies in patients undergoing oncologic mandibular surgery. Preoperatively, a 3D rendered model of the mandible was constructed from diagnostic computed tomography (CT) images. Cutting guides and patient specific reconstruction plates were designed and printed for intraoperative use. Intraoperative patient registration was performed using a cone beam CT scan (CBCT). The location of the mandible was tracked with an EM sensor fixated to the mandible. The real-time location of both the mandible and a pointer were displayed on the navigation system. Accuracy measurements were performed by pinpointing four anatomical landmarks and four landmarks on the cutting guide using the pointer on the patient and comparing these locations to the corresponding locations on the CBCT. Differences between actual and virtual locations were expressed as target registration error (TRE). The procedure was performed in eleven patients. TREs were 3.2 ± 1.1 mm and 2.6 ± 1.5 mm using anatomical landmarks and landmarks on the cutting guide, respectively. The navigation procedure added on average half an hour to the duration of the surgery. This is the first study that reports on the accuracy of EM navigation in patients undergoing mandibular surgery.

Clinical Implementation of In-House Developed MR-Based Patient-Specific 3D Models of Liver Anatomy

Knowledge of patient-specific liver anatomy is key to patient safety during major hepatobiliary surgery. Three-dimensional (3D) models of patient-specific liver anatomy based on diagnostic MRI images can provide essential vascular and biliary anatomical insight during surgery. However, a method for generating these is not yet publicly available. This paper describes how these 3D models of the liver can be generated using open source software, and then subsequently integrated into a sterile surgical environment. The most common image quality aspects that degrade the quality of the 3D models as well possible ways of eliminating these are also discussed. Per patient, a single diagnostic multiphase MRI scan with hepatospecific contrast agent was used for automated segmentation of liver contour, arterial, portal, and venous anatomy, and the biliary tree. Subsequently, lesions were delineated manually. The resulting interactive 3D model could be accessed during surgery on a sterile covered tablet. Up to now, such models have been used in 335 surgical procedures. Their use simplified the surgical treatment of patients with a high number of liver metastases and contributed to the localization of vanished lesions in cases of a radiological complete response to neoadjuvant treatment. They facilitated perioperative verification of the relationship of tumors and the surrounding vascular and biliary anatomy, and eased decision-making before and during surgery.

Nonlinear multispectral imaging for tumor delineation

SIGNIFICANCE

In breast-preserving tumor surgery, the inspection of the excised tissue boundaries for tumor residue is too slow to provide feedback during the surgery. The discovery of positive margins requires a new surgery which is difficult and associated with low success. If the re-excision could be done immediately this is believed to improve the success rate considerably.

AIM

Our aim is for a fast microscopic analysis that can be done directly on the excised tissue in or near the operating theatre.

APPROACH

We demonstrate the combination of three nonlinear imaging techniques at selected wavelengths to delineate tumor boundaries. We use hyperspectral coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG), and two-photon excited fluorescence (TPF) on excised patient tissue.

RESULTS

We show the discriminatory power of each of the signals and demonstrate a sensitivity of 0.87 and a specificity of 0.95 using four CARS wavelengths in combination with SHG and TPF. We verify that the information is independent of sample treatment.

CONCLUSIONS

Nonlinear multispectral imaging can be used to accurately determine tumor boundaries. This demonstration using microscopy in the epi-direction directly on thick tissue slices brings this technology one step closer to clinical implementation.

Association of Image-Guided Navigation With Complete Resection Rate in Patients With Locally Advanced Primary and Recurrent Rectal Cancer: A Nonrandomized Controlled Trial

IMPORTANCE

The percentage of tumor-positive surgical resection margin rates in patients treated for locally advanced primary or recurrent rectal cancer is high. Image-guided navigation may improve complete resection rates.

OBJECTIVE

To ascertain whether image-guided navigation during rectal cancer resection improves complete resection rates compared with surgical procedures without navigation.

DESIGN, SETTING, AND PARTICIPANTS

This prospective single-center nonrandomized controlled trial was conducted at the Netherlands Cancer Institute-Antoni van Leeuwenhoek in Amsterdam, the Netherlands. The prospective or navigation cohort included adult patients with locally advanced primary or recurrent rectal cancer who underwent resection with image-guided navigation between February 1, 2016, and September 30, 2019, at the tertiary referral hospital. Clinical results of this cohort were compared with results of the historical cohort, which was composed of adult patients who received rectal cancer resection without image-guided navigation between January 1, 2009, and December 31, 2015.

INTERVENTION

Rectal cancer resection with image-guided navigation.

MAIN OUTCOMES AND MEASURES

The primary end point was the complete resection rate, measured by the amount of tumor-negative resection margin rates. Secondary outcomes were safety and usability of the system. Safety was evaluated by the number of navigation system-associated surgical adverse events. Usability was assessed from responses to a questionnaire completed by the participating surgeons after each procedure.

RESULTS

In total, 33 patients with locally advanced or recurrent rectal cancer were included (23 men [69.7%]; median [interquartile range] age at start of treatment, 61 [55.0-69.0] years). With image-guided navigation, a radical resection (R0) was achieved in 13 of 14 patients (92.9%; 95% CI, 66.1%-99.8%) after primary resection of locally advanced tumors and in 15 of 19 patients (78.9%; 95% CI, 54.4%-94.0%) after resection of recurrent rectal cancer. No navigation system-associated complications occurred before or during surgical procedures. In the historical cohort, 142 patients who underwent resection without image-guided navigation were included (95 men [66.9%]; median [interquartile range] age at start of treatment, 64 [55.0-70.0] years). In these patients, an R0 resection was accomplished in 85 of 101 patients (84.2%) with locally advanced rectal cancer and in 20 of 41 patients (48.8%) with recurrent rectal cancer. A significant difference was found between the navigation and historical cohorts after recurrent rectal cancer resection (21.1% vs 51.2%; P = .047). For locally advanced primary tumor resection, the difference was not significant (7.1% vs 15.8%; P = .69). Surgeons stated in completed questionnaires that the navigation system improved decisiveness and helped with tumor localization.

CONCLUSIONS AND RELEVANCE

Findings of this study suggest that image-guided navigation used during rectal cancer resection is safe and intuitive and may improve tumor-free resection margin rates in recurrent rectal cancer.

TRIAL REGISTRATION

Netherlands Trial Register Identifier: NTR7184.

Accurate surgical navigation with real-time tumor tracking in cancer surgery

In the past decades, image-guided surgery has evolved rapidly. In procedures with a relatively fixed target area, like neurosurgery and orthopedics, this has led to improved patient outcomes. In cancer surgery, intraoperative guidance could be of great benefit to secure radical resection margins since residual disease is associated with local recurrence and poor survival. However, most tumor lesions are mobile with a constantly changing position. Here, we present an innovative technique for real-time tumor tracking in cancer surgery. In this study, we evaluated the feasibility of real-time tumor tracking during rectal cancer surgery. The application of real-time tumor tracking using an intraoperative navigation system is feasible and safe with a high median target registration accuracy of 3 mm. This technique allows oncological surgeons to obtain real-time accurate information on tumor location, as well as critical anatomical information. This study demonstrates that real-time tumor tracking is feasible and could potentially decrease positive resection margins and improve patient outcome.

A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images

Accurate assessment of 3D models of patient-specific anatomy of the liver, including underlying hepatic and biliary tree, is critical for preparation and safe execution of complex liver resections, especially due to high variability of biliary and hepatic artery anatomies. Dynamic MRI with hepatospecific contrast agents is currently the only type of diagnostic imaging that provides all anatomical information required for generation of such a model, yet there is no information in the literature on how the complete 3D model can be generated automatically. In this work, a new automated segmentation workflow for extraction of patient-specific 3D model of the liver, hepatovascular and biliary anatomy from a single multiphase MRI acquisition is developed and quantitatively evaluated. The workflow incorporates course 4D k-means clustering estimation and geodesic active contour refinement of the liver boundary, based on organ's characteristic uptake of gadolinium contrast agents overtime. Subsequently, hepatic vasculature and biliary ducts segmentations are performed using multiscale vesselness filters. The algorithm was evaluated using 15 test datasets of patients with liver malignancies of various histopathological types. It showed good correlation with expert manual segmentation, resulting in an average of 1.76 ± 2.44 mm Hausdorff distance for the liver boundary, and 0.58 ± 0.72 and 1.16 ± 1.98 mm between centrelines of biliary ducts and liver veins, respectively. A workflow for automatic segmentation of the liver, hepatic vasculature and biliary anatomy from a single diagnostic MRI acquisition was developed. This enables automated extraction of 3D models of patient-specific liver anatomy, and may facilitating better perception of organ's anatomy during preparation and execution of liver surgeries. Additionally, it may help to reduce the incidence of intraoperative biliary duct damage due to an unanticipated variation in the anatomy.

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

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.

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

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.

Influence of neoadjuvant chemotherapy on diffuse reflectance spectra of tissue in breast surgery specimens

Diffuse reflectance spectroscopy (DRS) can discriminate different tissue types based on optical characteristics. Since this technology has the ability to detect tumor tissue, several groups have proposed to use DRS for margin assessment during breast-conserving surgery for breast cancer. Nowadays, an increasing number of patients with breast cancer are being treated by neoadjuvant chemotherapy. Limited research has been published on the influence of neoadjuvant chemotherapy on the optical characteristics of the tissue. Hence, it is unclear whether margin assessment based on DRS is feasible in this specific group of patients. We investigate whether there is an effect of neoadjuvant chemotherapy on optical measurements of breast tissue. To this end, DRS measurements were performed on 92 ex-vivo breast specimens from 92 patients, treated with neoadjuvant chemotherapy and without neoadjuvant chemotherapy. Generalized estimating equation (GEE) models were generated, comparing the measurements of patients with and without neoadjuvant chemotherapy in datasets of different tissue types using a significance level of 5%. As input for the GEE models, either the intensity at a specific wavelength or a fit parameter, derived from the spectrum, was used. In the evaluation of the intensity, no influence of neoadjuvant chemotherapy was found, since none of the wavelengths were significantly different between the measurements with and the measurements without neoadjuvant chemotherapy in any of the datasets. These results were confirmed by the analysis of the fit parameters, which showed a significant difference for the amount of collagen in only one dataset. All other fit parameters were not significant for any of the datasets. These findings may indicate that assessment of the resection margin with DRS is also feasible in the growing population of breast cancer patients who receive neoadjuvant chemotherapy. However, it is possible that we did not detect neoadjuvant chemotherapy effect in the some of the datasets due to the small number of measurements in those datasets.

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

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.

Tissue diagnosis during colorectal cancer surgery using optical sensing: an in vivo study

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.

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

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.

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

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.

The future of surgical oncology

Fast moving specialty

Surgical removal of the index node marked using magnetic seed localization to assess response to neoadjuvant immunotherapy in patients with stage III melanoma

This pilot study explored the value of localized index node removal after neoadjuvant immunotherapy in patients with stage III melanoma, for use as a response indicator to guide the extent of completion lymph node dissection. Promising technology.

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

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.

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

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

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

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.

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

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.

Tumor Resection Margin Definitions in Breast-Conserving Surgery: Systematic Review and Meta-analysis of the Current Literature

Worldwide, various guidelines recommend what constitutes an adequate margin of excision for invasive breast cancer or for ductal carcinoma-in-situ (DCIS). We evaluated the use of different tumor resection margin guidelines and investigated their impact on positive margin rates (PMR) and reoperation rates (RR). Thirteen guidelines reporting on the extent of a positive margin were reviewed along with 31 studies, published between 2011 and 2016, reporting on a well-defined PMR. Studies were categorized according to the margin definition. Pooled PMR and RR were determined with random-effect models. For invasive breast cancer, most guidelines recommend a positive margin of tumor on ink. However, definitions of reported positive margins in the clinic vary from more than focally positive to the presence of tumor cells within 3 to 5 mm from the resection surface. Within the studies analyzed (59,979 patients), pooled PMRs for invasive breast cancer ranged from 9% to 36% and pooled RRs from 77% to 99%. For DCIS, guidelines vary between no DCIS on the resection surface to DCIS cells found within a distance of 2 mm from the resection edge. Pooled PMRs for DCIS varied from 4% to 23% (840 patients). Given the differences in tumor margin definition between countries worldwide, quality control data expressed as PMR or RR should be interpreted with caution. Furthermore, the overall definition for positive resection margins for both invasive disease and DCIS seems to have become more liberal.