The effectiveness of antibody therapeutics relies on in vivo drug pharmacology, intrinsic parameters of tumor cells, and tumor microenvironment factors. An understanding of the antibody-target-microenvironment interactions will improve patient selection and development of new targeted therapeutics. Using optically labeled therapeutic antibodies systemically delivered to patients prior to surgical resection, we were able to develop a novel analytical method to measure therapeutic behavior of these agents and their cellular targets at single cell resolution within intact human tumors. We identified two major subtypes of CAFs as well a unique enrichment of extracellular matrix components with the tumor. The spatial arrangement of ECM proteins were also associated with reduced therapeutic antibody penetration. Our findings were further supported by spatial transcriptomics of adjacent tissue slices and public scRNA seq data. This study provides a new framework for interrogating drug pharmacology in conjunction with tumor biology, opening new avenues for dosing optimization, biomarker identification, and the development of new stromal-targeting therapies to improve treatment outcomes.
Surgery and systemic therapy are the backbone treatment methods for solid tumors. However, incomplete surgical resection and poor response to systemic therapy have resulted in substantial cancer-related mortality and morbidity worldwide. Antibody-based fluorescence imaging holds great potential to enable precise surgical resection and improve our understanding of the mechanism driving resistance to systemic therapy in clinical tumors. In this talk, I will present our recent work on translating fluorescently labeled therapeutic antibodies for surgical navigation and for quantitating antibody delivery in solid tumors in first-in-human trials.
Introduction: Glioblastoma (GBM) is the most common and devastating primary brain tumor. The recurrence rate remains high with a median survival of 15 months. GBM’s infiltrative nature results in ill-defined margins that makes maximal tumor resection with minimal morbidity a challenge. Epidermal growth factor receptor (EGFR) is the most frequently amplified gene in GBM (35-45% of tumors) and is associated with overexpression in about 40-98% of cases, a characteristic of more aggressive phenotypes. We hypothesize that fluorescence labeled anti-EGFR monoclonal antibodies (mAb), panitumumab-IRDye800 (pan800) and cetuximab-IRDye800 (cet800), could be leveraged to enhance tumor contrast during surgical resection and improve patient outcome.
Methods: 50mg fluorescently labeled corresponding study drugs, pan800 and cet800 respectively, were administered 1-2 days in glioblastoma patients with contrast enhancing (CE) tumors prior to surgery following 100 mg loading dose of unlabeled cetuximab or panitumumab. Near-infrared fluorescence imaging of tumor and histologically negative peri-tumoral tissue was performed intraoperatively and ex vivo. Fluorescence was measured as mean fluorescence intensity (MFI), and tumor-to-background ratios (TBRs) were calculated by comparing MFIs of tumor and histologically uninvolved tissue.
Results: Despite heterogeneous drug uptake across all resected brain tissues, mean fluorescence intensity (MFI) correlated strongly (R^2=0.97) with tumor volume among histologically confirmed tumor tissues. The smallest detectable tumor size in a closed-field setting was 4.2 x 2.7 mm^2 (8.2 mg) for pan800 and 8.5 x 6.6 mm^2 (70mg) for cet800. Tumor tissues from pan800 infusion had significantly higher mean TBR (8.1 ± 4.6) than cet800 infused ones in intraoperative imaging (3.3 ± 2.7; P = 0.004). NIR fluorescence from both test drugs provided high contrast to identify as few as a cluster of (5 ± 1) tumor cells in macroscopic imaging of whole sections of paraffin embedded tissues. Sensitivity and specificity of MFI for viable tumor detection was calculated and fluorescence was found to be highly sensitive (64.4% for pan800, 73.0% for cet800) and specific (98.0% for pan800, 66.3% for cet800) for viable tumor tissue while normal peri-tumoral tissue showed minimal fluorescence. No related grade-2 adverse events were observed 30 days beyond the infusion of either study drugs.
Conclusion: EGFR antibody based imaging for contrast-enhanced glioblastomas proved safe in human patients and specific intratumoral delivery of NIR fluorescence provided high optical contrast and resolution for intraoperative image-guided resection. Fully humanized panitumumab-IRDye800 demonstrated superior detection sensitivity and tumor specificity over the chimeric cetuximab-IRDye800.
Background: Pediatric High-grade gliomas (pHGGs) are the No.1 cause of cancer-related deaths in children with median survival of less than a year. pHGGs tend to be infiltrative and appear irregularly shaped with ill-defined borders difficult to be distinguished from surrounding normal brain tissue. As the extent of surgical resection predicts survival, precise tumor removal with more accurate margin delineation means better treatment outcome and less loss of vital functions. While EGFR is one of the most commonly amplified genes in pHGGs, its protein-level expression is not as well characterized as in adult HGGs. Previously, near-infrared (NIR) dye labeled epidermal growth factor receptor (EGFR) antibody has served as contrast agent in fluorescence-guided surgery of head and neck cancer. However, it must overcome the blood-brain barrier (BBB) for effective intratumoral delivery in the case of brain cancer. Therefore, the latest advancement in reversible BBB opening with tight junction protein modulation has the potential to enable the molecular targeted imaging guidance of pHGG resection.
Aims: The current study aimed to improve intratumoral delivery of NIR fluorescent EGFR antibody via reversible BBB permeability enhancement with siRNA modulation of tight junction protein in an orthotopic xenograft animal model of high-grade glioma with EGFR overexpression. Furthermore, resected pHGGs were examined for EGFR expression in order to stratify patient subpopulation most likely to benefit from intraoperative molecular imaging strategy that targets EGFR.
Methods: An orthotopic high-grade glioma xenograft model was established in 6-15 week old mice (n=3) by intracranial injection of 10^6 EGFR-overexpressing high-grade glioma cells (D270, 10ul) 3mm below the surface of brain. Subsequently, the exposed brain was covered with a glass plate secured to the skull with cyanoacrylate glue. siRNA was selected from those targeting conserved regions of the mouse claudin-5 cDNA sequence. 20μg of claudin-5 siRNA was injected intravenously via the tail vein in an in vivo-Jet-PEI solution (Polyplus Transfection) at a rate of 0.2 ml/sec 10 days post tumor implant. 0.1mL tetramethylrhodamine (250kDa) and various sized FITC-dextran (4.4-150kDa) solutions were injected intravenously to visualize blood vessels and assess extravasation distance through cranial window via 2-photo microscopy. Enhanced permeability of BBB was characterized by increase in KTrans on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the tumor region. Mean fluorescence intensity at 800nm was measured through cranial window with an in vivo NIR imager (Pearl Impulse, LI-COR Biosciences) 0-72 hours following tail vein injection of 200ug panitumumab-IRDye800 (pan800). Immunohistochemical analysis of EGFR expression was performed on surgically resected de novo primary pHGG tumors, from seven GBM and three anaplastic ependymoma patients respectively.
Results: The siRNA has shown a reversible 80% suppression of claudin-5 at 48-hrs post-injection that returned to normal levels at 72 hours. More than three-fold increase in penetration distance of 70kDa enhancing agent was observed in extravascular space and a 74% increase in intratumoral permeability was observed on DCE-MRI. Intratumoral delivery of fluorescent EGFR antibody (panitumumab-IRDye800) occurred at 6 hours and peaked at 48 hours post systemic injection following BBB opening. Positive EGFR expression was found in 70% of all surgically removed high-grade pediatric brain tumor samples. The median age of patients with positive EGFR expression was 15 (IQR = 12.75 to 16.50), significantly higher (P = 0.018) than that of EGFR negative patients (median = 0.75, IQR = 0.47 to 5.38).
Conclusions: We provided proof-of-concept evidence that the enabling technology of transient BBB modulation and fluorescence-guided imaging with EGFR targeting antibody has great potential for clinical translation to improve surgery outcome by providing tumor-specific precision resection to a significant subpopulation of young patients with pHGGs
Low response rates in solid tumors including head and neck cancers (HNCs) have been attributed to failure of the drug to reach its intended target. However, investigation of drug delivery has been limited due to difficulties in measuring concentrations in the tumor and the ability to localizing drugs in human tissues. Factors determining intratumoral antibody distribution in primary tumor and metastatic lymph nodes have not been well-studied in human patients. To address this challenge, we propose to leverage fluorescently labeled antibodies to investigate antibody delivery into HNCs.
To this end, we have conducted a first-in-human clinical trial to assess the delivery of panitumumab-IRDye800 in HNCs. Twenty-two patients enrolled in this study received intravenous administration of panitumumab-IRDye800 at multiple subtherapeutic doses: (1) 0.06mg/kg, (2) 0.5 mg/kg, (3) 1 mg/kg, (4) 50 mg flat dose, (5) 25 mg flat dose. To quantify the antibody delivery, fresh tumor samples were procured and the amount of antibody in the tumor was quantified as ng/mg of tissue, which was then correlated with tumor characteristics. Immunohistochemistry of multiple protein markers, including EGFR, ERG, cytokeratin, Ki67, alpha-smooth muscle actin, etc., have been implemented in serial sections of primary tumors and metastatic lymph nodes. A quantitative image analysis pipeline was developed to analyze these IHC images and score the staining on both global and local scale. A predictive model was built to identify the most important predictors for antibody penetration from pharmacological factors, tumor pathophysiological factors, and tumor microenvironmental factors.
BACKGROUND: Presence of lymph node (LN) metastasis is considered the most important prognostic factor in patients with head and neck cancer, yet intraoperative identification of metastatic LNs is considered challenging. We propose the near-infrared fluorescently labeled epidermal growth factor receptor (EGFR) antibody panitumumab-IRDye800 for intraoperative metastatic LN identification.
METHODS: Patients were injected 2-5 days before surgery with panitumumab-IRDye800 (0.5 or 1.0 mg/kg). On the day of surgery, (excised) LN samples were evaluated on high sensitivity fluorescence imaging systems (SurgVision (SurgOptix), PINPOINT (Novadaq), and Pearl imager and Odyssey CLx (LI-COR Biosciences). Location and intensity of the fluorescence signal was correlated to the location of tumor as defined on the hematoxylin and eosin staining by the pathologist, and the EGFR expression pattern. We calculated the sensitivity, specificity, positive and negative predictive values of panitumumab-IRdye800 for metastatic LN identification.
RESULTS: We thus far included 9/27 patients in our ongoing phase I trial. 244 LNs were removed intraoperatively of which 8 were tumor-positive. Fluorescence imaging of panitumumab-IRdye800 revealed 236 true-negative nodes (not fluorescent, not tumor-positive), 8 true-positive nodes (fluorescent, tumor-positive), 0 false-positive nodes (fluorescent, not tumor-positive) and 0 false-negative nodes (not fluorescent, tumor-positive) resulting in a sensitivity of 100%, a specificity of 100%, and a positive and negative predictive value of 100% and 100%, respectively.
CONCLUSION: Preliminary results from our ongoing study suggest panitumumab-IRDye800 can identify metastatic LNs. Upon trial progression, if findings remain constant, it can open a whole new era for intraoperative metastatic LN identification.
Wide-field fluorescent imaging for fluorescence molecular guidance has become a promising technique for use in imaging guided surgical navigation, but quick and intuitive microscopic inspection of fluorescent hot spots is still needed to confirm local disease states of tissues. To address this unmet need, we have developed a clinically translatable dual-modality handheld surgical microscope that incorporates both, wide-field (mesoscopic) fluorescence imaging and high-resolution (microscopic) horizontal optical-sectioning. This is accomplished by integrating a commercially available wide-field fiberscope, modified for two-color (660nm and 785nm) fluorescent detection, into a compact package (5.5 mm dia.) which also contains a dual-axis confocal (DAC) microscope. DAC microscopy is a high-sensitivity, high-resolution fluorescent imaging technology that benefits from the specificity of molecular probes, and enables interrogation of deeper regions of tissue by performing optical-sectioning of tissue. The DAC microscope has been designed with custom catadioptric micro-lenses to provide broadband multispectral capability for fluorescence imaging of multiple fluorophores over a broad spectral range (VIS to NIR), and also uses a novel MEMS-based scanning system for horizontal sectioning, and thus enables access to deeper regions of tissue at resolutions comparable to histological analysis. Large field-of-view (mm scale) is further provided by image mosaicing. The instrument thus provides simultaneous mesoscopic and microscopic fluorescence imaging over a broad spectral range for intuitively performing fast in-vivo search and microscopic confirmation of optical molecular markers in tissue, which is a capability that will become increasingly important for precise tumor resection in oncology as more optical molecular markers become approved for human use.
A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.
Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.
Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.
We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94±6%, sensitivity of 94±6%, and specificity of 95±6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.
Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.
Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.
We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.
Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.
Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery.
Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.
KEYWORDS: Tumors, Tissues, Reflectivity, Hyperspectral imaging, Cancer, Green fluorescent protein, Image classification, Tissue optics, RGB color model, In vivo imaging
Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.
Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.
As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.
Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.
KEYWORDS: Tumors, Image registration, Hyperspectral imaging, Cancer, Tissues, Principal component analysis, In vivo imaging, Head, Surgery, Green fluorescent protein
The determination of tumor margins during surgical resection remains a challenging task. A complete removal of
malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient
satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant
and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical
tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of
cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral
images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper,
principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is
then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the
global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation.
Registration experiment was performed on animal hyperspectral and histological images. Experimental results from
animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.
Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
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