Kayode Olowe

In vivo characterization of pancreatic and lymph node tissue by using EUS spectrum analysis: a validation study.

BACKGROUND Quantitative spectral analysis of the radiofrequency (RF) signals that underlie grayscale EUS images can be used to provide additional, objective information about tissue state. OBJECTIVE Our purpose was to validate RF spectral analysis as a method to distinguish between (1) benign and malignant lymph nodes and (2) normal pancreas, chronic pancreatitis, and pancreatic cancer. DESIGN AND SETTING A prospective validation study of eligible patients was conducted to compare with pilot study RF data. PATIENTS Forty-three patients underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes (19 from a previous pilot study and 24 additional patients). MAIN OUTCOME MEASUREMENTS Midband fit, slope, intercept, and correlation coefficient from a linear regression of the calibrated RF power spectra were determined. RESULTS Discriminant analysis of mean pilot-study parameters was then performed to classify validation-study parameters. For benign versus malignant lymph nodes, midband fit and intercept (both with t test P < .058) provided classification with 67% accuracy and area under the receiver operating curve (AUC) of 0.86. For diseased versus normal pancreas, midband fit and correlation coefficient (both with analysis of variance P < .001) provided 93% accuracy and an AUC of 0.98. For pancreatic cancer versus chronic pancreatitis, the same parameters provided 77% accuracy and an AUC of 0.89. Results improved further when classification was performed with all data. LIMITATIONS Moderate sample size and spatial averaging inherent to the technique. CONCLUSIONS This study confirms that mean spectral parameters provide a noninvasive method to quantitatively discriminate benign and malignant lymph nodes as well as normal and diseased pancreas.

Automated quantification of colonic crypt morphology using integrated microscopy and optical coherence tomography

Colonic crypt morphological patterns have shown a close correlation with histopathological diagnosis. Imaging technologies such as high-magnification chromoendoscopy and endoscopic optical coherence tomography (OCT) are capable of visualizing crypt morphology in vivo. We have imaged colonic tissue in vitro to simulate high-magnification chromoendoscopy and endoscopic OCT and demonstrate quantification of morphological features of colonic crypts using automated image analysis. 2-D microscopic images with methylene blue staining and correlated 3-D OCT volumes were segmented using marker-based watershed segmentation. 2-D and 3-D crypt morphological features were quantified. The accuracy of segmentation was validated, and measured features are in agreement with known crypt morphology. This work can enable studies to determine the clinical utility of high-magnification chromoendoscopy and endoscopic OCT, as well as studies to evaluate crypt morphology as a biomarker for colonic disease progression.

Characterization of pancreatic cancer and intra-abdominal lymph node malignancy using spectrum analysis of endoscopic ultrasound imaging

This study assessed the ability of spectral analysis of endoscopic ultrasound (EUS) RF signals acquired in humans in vivo to distinguish between (1) benign and malignant intraabdominal and mediastinal lymph nodes and (2) pancreatic cancer, chronic pancreatitis, and normal pancreas. Mean midband fit, slope, intercept, and correlation coefficient from a linear regression of the calibrated RF power spectra were computed over regions of interest defined by the endoscopist. Linear discriminant analysis was then performed to develop a classification of the resulting spectral parameters. For lymph nodes, classification based on the midband fit and intercept provided 67% sensitivity, 82% specificity, and 73% accuracy for malignant vs. benign nodes. For pancreas, classification based on midband fit and correlation coefficient provided 95% sensitivity, 93% specificity, and 93% accuracy for diseased vs. normal pancreas and 85% sensitivity, 71% specificity, and 85% accuracy for pancreatic cancer vs. chronic pancreatitis. These promising results suggest that mean spectral parameters can provide a non-invasive method to quantitatively characterize pancreatic cancer and lymph malignancy in vivo.

T1853 Morphological Feature Quantification of Colonic Crypt Patterns Using Microscope-Integrated OCT

Background: Optical coherence tomography examination of the upper and lower GI tract has shown promising technological advances over the last decade. However one limitation preventing implementation in clinical settings is the difficulty in interpreting images with large amounts of information in a timeframe suitable to facilitate an endoscopist. Reliable analytic methodologies to rapidly process and interpret images obtained at endoscopy do not currently exist. A computerized model to accurately quantify OCT obtained architectural features of colonic crypts could serve as a proof of principle that this concept is viable. Aim: To demonstrate the use of computerized algorithms to quantify the morphological features of colonic crypts. Methods: Samples of fresh colon tissues (10 normal and 10 aberrant crypt focus (ACF)) were obtained from colectomies. The samples were stained with methylene blue and microscopic images were recorded to approximately simulate In Vivo imaging with magnification chromoendoscopy. In addition, 3D OCT volumes were recorded from the same sites using an integrated OCT scanner. The crypts within the micrographs were automatically segmented using marker-based watershed morphological processing. The morphological features of the segmented crypts were extracted and quantified. For the correlated 3D OCT volumes, the crypts were first segmented in each en-face plane of the stack. Then the segmented crypts were visualized in 3D by volume rending. Finally 3D central axes of the crypts, called skeletons, were extracted and the orientations of the skeletons were quantified by centroid-searching. Results: For typical grossly normal colonic tissues, the mean area of segmented crypts was 4213μm2; the mean major axis length was 89μm; the mean minor axis length was 60μm; the mean eccentricity was 0.67; the mean standard deviation angle between the en-face plane and the skeletons was 2.5 degree. For typical aberrant crypt foci (ACF) colonic tissues, the mean area of segmented crypts was 10267μm2; the mean major axis length was 145μm; the mean minor axis length was 88μm; the mean eccentricity was 0.74; the mean standard deviation angle between the en-face plane and the skeletons was 12.7 degree. There were significant morphological feature differences between normal and ACF colonic crypts. Conclusion: These methods can quantify morphological features of colonic crypts, and the results correspond well to known differences between the crypt features of normal and aberrant crypts. Further development of this approach to OCT derived data analysis may facilitate the evolution of this technology into clinical practice.

Investigation of computer-aided colonic crypt pattern analysis

Colorectal cancer is the second leading cause of cancer-related death in the United States. Approximately 50% of these deaths could be prevented by earlier detection through screening. Magnification chromoendoscopy is a technique which utilizes tissue stains applied to the gastrointestinal mucosa and high-magnification endoscopy to better visualize and characterize lesions. Prior studies have shown that shapes of colonic crypts change with disease and show characteristic patterns. Current methods for assessing colonic crypt patterns are somewhat subjective and not standardized. Computerized algorithms could be used to standardize colonic crypt pattern assessment. We have imaged resected colonic mucosa in vitro (N = 70) using methylene blue dye and a surgical microscope to approximately simulate in vivo imaging with magnification chromoendoscopy. We have developed a method of computerized processing to analyze the crypt patterns in the images. The quantitative image analysis consists of three steps. First, the crypts within the region of interest of colonic tissue are semi-automatically segmented using watershed morphological processing. Second, crypt size and shape parameters are extracted from the segmented crypts. Third, each sample is assigned to a category according to the Kudo criteria. The computerized classification is validated by comparison with human classification using the Kudo classification criteria. The computerized colonic crypt pattern analysis algorithm will enable a study of in vivo magnification chromoendoscopy of colonic crypt pattern correlated with risk of colorectal cancer. This study will assess the feasibility of screening and surveillance of the colon using magnification chromoendoscopy.