Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machine-learning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.
Neoadjuvant radiotherapy, as part of the conventional treatment of rectal cancer, can induce fibrotic tissue formation around the tumor. This complicates the exact determination of the tumor borders during surgery, which might increase the chance of positive resection margins. In a previous ex vivo study, we distinguished tumor tissue from healthy rectal wall and fat with an accuracy of 0.95, using diffuse reflectance spectroscopy (DRS). Since this study did not include fibrosis, the aim of the current ex vivo study was to examine whether differentiation of tumor and fibrosis with DRS is possible.
DRS measurements from freshly resected specimen of 16 patients were obtained. In eight patients fibrosis was measured, in the other eight patients tumor was measured. The measurements were performed using a DRS probe with a source-detector distance of 2 mm. The spectra were obtained in the wavelength range of 450-1600 nm. Classification of the measurements was done using a support vector machine (SVM) and a set of features extracted from the spectra. The SVM was evaluated using an eight-fold cross-validation, which was repeated ten times.
For all repetitions, the area under the ROC curve was greater than 0.85 (mean = 0.87, STD = 0.02). The mean sensitivity and specificity were 0.85 (STD = 0.03) and 0.88 (STD = 0.01) respectively. It can be concluded that tumor tissue can be distinguished from fibrosis based on spectral features from DRS measurements. The next step will be to conduct an in vivo study, to verify these results during 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.
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.
Positive tumor resection margins are reported in up to 45% of the patients undergoing surgery for tongue cancer. With the aim to develop a technique that can assess tumor resection margins intraoperatively, we conducted an ex vivo study to evaluate the feasibility of near infrared hyperspectral imaging for distinguishing tumor from healthy tongue tissue.
Fresh surgical specimens of squamous cell carcinoma of the tongue were scanned with a pushbroom camera. The acquired spectral hypercubes contain a measure of the diffuse light reflectance (wavelength range of 900-1700 nm) for each pixel of the hyperspectral image. Spectral bands were selected from the spectrum and used to classify spectra of tumor and healthy tissue. In this, a linear classifier was trained on 80% of the data and its performance in predicting the tissue type of the residual 20% of the data was measured. This was repeated five times and mean accuracy, sensitivity and specificity were used as output for this study.
A total of 463 spectra were obtained from tongue tumor tissue and 421 spectra from healthy tongue tissue. The spectral bands between 1060-1130 nm and 1150-1190 nm were used in the classification analysis. Mean accuracy, sensitivity and specificity were 89%±13, 94%±11 and 87%±21, respectively.
Near infrared hyperspectral imaging can discriminate tongue tumor tissue from healthy tongue tissue in an ex vivo setting by using specific bands of the reflectance spectrum. Further analyses will be done to assess whether using the whole spectrum can improve the classification results.
KEYWORDS: Tissues, Tumors, Surgery, Colorectal cancer, Tissue optics, Diffuse reflectance spectroscopy, Pathology, In vivo imaging, RGB color model, Cancer
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.
This ex vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy oral tissue, with the aim to develop a technique that can be used to determine a complete excision of tumor through intraoperative margin assessment.
DRS spectra were acquired on fresh surgical specimens from patients with an oral squamous cell carcinoma. The spectra represent a measure of diffuse light reflectance (wavelength range of 400-1600 nm), detected after illuminating tissue with a source fiber at 1.0 and 2.0 mm distances from a detection fiber. Spectra were obtained from 23 locations of tumor tissue and 16 locations of healthy muscle tissue. Biopsies were taken from all measured locations to facilitate an optimal correlation between spectra and pathological information.
The area under the spectrum was used as a parameter to classify spectra of tumor and healthy tissue. Next, a receiver operating characteristics (ROC) analysis was performed to provide the area under the receiver operating curve (AUROC) as a measure for discriminative power.
The area under the spectrum between 650 and 750 nm was used in the ROC analysis and provided AUROC values of 0.99 and 0.97, for distances of 1 mm and 2 mm between source and detector fiber, respectively.
DRS can discriminate tumor from healthy oral tissue in an ex vivo setting. More specimens are needed to further evaluate this technique with component analyses and classification methods, prior to in vivo patient measurements.
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