Abstract
Cancer is the second-leading cause of death in the United State and surgery remains the primary treatment for most solid mass tumors. However, accurately identifying tumor margins in real-time remains a challenge. In this study, the design and testing of hyperspectral imaging (HSI) system based on a single-pixel camera engine is discussed. The primary advantage of a single pixel architecture over traditional scanning HSI techniques is its high sensitivity and potential to function at low light levels. The objective for the imaging system described here is to detect changes in the reflectance spectra of tissue and to use these differences to delineate tumor margins. This paper presents the results of a 19-patient pilot study that assesses the ability of the HSI system to use reflectance imaging to delineate adenocarcinoma tumor margins in human pancreatic tissue imaged ex vivo. Pancreatic tissue excised during pancreatectomy was imaged immediately after being sent to the pathology lab. A pathologist sectioned the tissue and placed samples into standard tissue embedding cassettes. These tissue samples were then imaged using the HSI system. After imaging, the samples were returned to the pathologist for processing and analysis. The HSI was later compared to the histological analysis. The spectral angle mapping (SAM) and support vector machine (SVM) algorithms were used to classify pixels in the HSI images as healthy or unhealthy in order to delineate margins. Good agreement between margins determined via HSI (using both SAM and SVM) and histology/white light imaging was found.
Highlights
The American Cancer Society estimates that more than 1.8 million people will be diagnosed with cancer and over 600,000 will die from the disease in 2020 [1]
This paper presents the results of a 19-patient pilot study that assesses the ability of the hyperspectral imaging (HSI) system to use reflectance imaging to delineate adenocarcinoma tumor margins in human pancreatic tissue imaged ex vivo
spectral angle mapping (SAM) has been used by other researchers for the classification of HSI including the identification of nerve fibers [36] and for the automatic detection of altered mucosa of the human larynx [37]
Summary
The American Cancer Society estimates that more than 1.8 million people will be diagnosed with cancer and over 600,000 will die from the disease in 2020 [1]. Multiple imaging modalities are routinely available for preoperative tumor diagnosis and surgical planning, including x-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT). These techniques are not usually available during surgery. Paraffin section of inked surgical margins is the gold standard for margin assessment This process is time-consuming and results are not available until several days after surgery. The long-term goal of this project is to provide an additional intraoperative imaging modality to clearly delineate tumor margins and identify areas of residual disease in real time
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