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

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.

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.

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.

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.

Optimal endobronchial tool sizes for targeting lung lesions based on 3D modeling.

BACKGROUND: For patients with suspicious lung lesions found on chest x-ray or CT, endo/trans- bronchial biopsy of the lung is the preferred method for obtaining a diagnosis. With the addition of new screening programs, a higher number of patients will require diagnostic biopsy which will prove even more challenging due to the small size of lesions found with screening. There are many endobronchial tools available on the market today and a wide range of new tools under investigation to improve diagnostic yield. However, there is little information available about the optimal tool size required to reach the majority of lesions, especially peripheral ones. In this manuscript we investigate the percentage of lesions that can be reached for various diameter tools if the tools remain inside the airways (i.e. endobronchial biopsy) and the distance a tool must travel "off-road" (or outside of the airways) to reach all lesions. METHODS AND FINDINGS: To further understand the distribution of lung lesions with respect to airway sizes and distances from the airways, six 3D models of the lung were generated. The airways were modeled at two different respiratory phases (inspiration and expiration). Three sets of 1,000 lesions were randomly distributed throughout the lung for each respiratory phase. The simulations showed that the percentage of reachable lesions decreases with increasing tool diameter and decreasing lesion diameter. A 1mm diameter tool will reach <25% of 1cm lesions if it remains inside the airways. To reach all 1cm lesions this 1mm tool would have to navigate through the parenchyma up to 8.5mm. CT scans of 21 patient lesions confirm these results reasonably well. CONCLUSIONS: The smaller the tool diameter the more likely it will be able to reach a lung lesion, whether it be for diagnostic biopsy, ablation, or resection. However, even a 1mm tool is not small enough to reach the majority of small (1-2cm) lesions. Therefore, it is necessary for endobronchial tools to be able to navigate through the parenchyma to reach the majority of lesions.

Use of a handheld terahertz pulsed imaging device to differentiate benign and malignant breast tissue.

Since nearly 20% of breast-conserving surgeries (BCS) require re-operation, there is a clear need for developing new techniques to more accurately assess tumor resection margins intraoperatively. This study evaluates the diagnostic accuracy of a handheld terahertz pulsed imaging (TPI) system to discriminate benign from malignant breast tissue ex vivo. Forty six freshly excised breast cancer samples were scanned with a TPI handheld probe system, and histology was obtained for comparison. The image pixels on TPI were classified using (1) parameters in combination with support vector machine (SVM) and (2) Gaussian wavelet deconvolution in combination with Bayesian classification. The results were an accuracy, sensitivity, specificity of 75%, 86%, 66% for method 1, and 69%, 87%, 54% for method 2 respectively. This demonstrates the probe can discriminate invasive breast cancer from benign breast tissue with an encouraging degree of accuracy, warranting further study.