Assessing tumor angiogenesis in colorectal cancer by quantitative contrast-enhanced endoscopic ultrasound and molecular and immunohistochemical analysis.

BACKGROUND AND OBJECTIVES: Data on contrast-enhanced endoscopic ultrasound (CE-EUS) for colorectal cancer (CRC) evaluation are scarce. Therefore, we aimed to assess the vascular perfusion pattern in CRC by quantitative CE-EUS and compare it to immunohistochemical and genetic markers of angiogenesis. PATIENTS AND METHODS: We performed a retrospective analysis of CE-EUS examinations of 42 CRC patients, before any therapy. CE-EUS movies were processed using a dedicated software. Ten parameters were automatically generated from the time-intensity curve (TIC) analysis: peak enhancement (PE), rise time (RT), mean transit time, time to peak (TTP), wash-in area under the curve (WiAUC), wash-in rate (WiR), wash-in perfusion index (WiPI), wash-out AUC (WoAUC), and wash-in and wash-out AUC (WiWoAUC). The expression levels of the vascular endothelial growth factor receptor 1 (VEGFR1) and VEGFR2 genes were assessed from biopsy samples harvested during colonoscopy. Microvascular density and vascular area were calculated after CD31 and CD105 immunostaining. RESULTS: Forty-two CE-EUS video sequences were analyzed. We found positive correlations between the parameters PE, WiAUC, WiR, WiPI, WoAUC, WiWoAUC, and N staging (Spearman r = 0.437, r = 0.336, r = 0.462, r = 0.437, r = 0.358, and r = 0.378, respectively, P < 0.05), and also between RT and TTP and CD31 vascular area (r = 0.415, and r = 0.421, respectively, P < 0.05). VEGFR1 and VEGFR2 expression did not correlate with any of the TIC parameters. CONCLUSIONS: CE-EUS with TIC analysis enables minimally invasive assessment of CRC angiogenesis and may provide information regarding the lymph nodes invasion. However, further studies are needed for defining its role in the evaluation of CRC patients.

Narrow band imaging with near-focus mode for colorectal polyps' characterization.

Conventional white light endoscopy is far from being an ideal tool to detect, characterize, and confirm the nature of colorectal lesions in order to indicate targeted biopsies or polyp resections only when necessary. Minimally invasive imaging techniques have gradually emerged to reveal previously unseen abnormalities to the operator during endoscopic examination. In this respect, technology and applications of narrow band imaging (NBI) are rapidly evolving. Magnification using NBI with near-focus mode has been introduced recently to enable closer examination under the control of a single button. The aim of this article is to offer an in-depth overview of this topic with emphasis on colorectal polyps through a literature review by using PubMed search tools including full-text articles, up-to-date guidelines and recent abstracts with obvious conclusions.

Computer Aided Diagnosis for Confocal Laser Endomicroscopy in Advanced Colorectal Adenocarcinoma.

INTRODUCTION: Confocal laser endomicroscopy (CLE) is becoming a popular method for optical biopsy of digestive mucosa for both diagnostic and therapeutic procedures. Computer aided diagnosis of CLE images, using image processing and fractal analysis can be used to quantify the histological structures in the CLE generated images. The aim of this study is to develop an automatic diagnosis algorithm of colorectal cancer (CRC), based on fractal analysis and neural network modeling of the CLE-generated colon mucosa images. MATERIALS AND METHODS: We retrospectively analyzed a series of 1035 artifact-free endomicroscopy images, obtained during CLE examinations from normal mucosa (356 images) and tumor regions (679 images). The images were processed using a computer aided diagnosis (CAD) medical imaging system in order to obtain an automatic diagnosis. The CAD application includes image reading and processing functions, a module for fractal analysis, grey-level co-occurrence matrix (GLCM) computation module, and a feature identification module based on the Marching Squares and linear interpolation methods. A two-layer neural network was trained to automatically interpret the imaging data and diagnose the pathological samples based on the fractal dimension and the characteristic features of the biological tissues. RESULTS: Normal colon mucosa is characterized by regular polyhedral crypt structures whereas malignant colon mucosa is characterized by irregular and interrupted crypts, which can be diagnosed by CAD. For this purpose, seven geometric parameters were defined for each image: fractal dimension, lacunarity, contrast correlation, energy, homogeneity, and feature number. Of the seven parameters only contrast, homogeneity and feature number were significantly different between normal and cancer samples. Next, a two-layer feed forward neural network was used to train and automatically diagnose the malignant samples, based on the seven parameters tested. The neural network operations were cross-entropy with the results: training: 0.53, validation: 1.17, testing: 1.17, and percent error, resulting: training: 16.14, validation: 17.42, testing: 15.48. The diagnosis accuracy error was 15.5%. CONCLUSIONS: Computed aided diagnosis via fractal analysis of glandular structures can complement the traditional histological and minimally invasive imaging methods. A larger dataset from colorectal and other pathologies should be used to further validate the diagnostic power of the method.

Endoscopic ultrasound: current role and future perspectives in managing rectal cancer patients.

As therapeutic regimens for rectal cancer have seen considerable changes, an accurate staging is mandatory for choosing the adequate strategy. Locoregional staging is the decisive factor in selecting patients for neoadjuvant chemoradiation therapy and for determining the extent of surgery. Endoscopic ultrasound (endorectal ultrasound--ERUS) is a very effective method for assessing the local extent of rectal cancer, especially regarding the depth of tumor infiltration. Although a significant limitation is represented by its lower accuracy for diagnosis of lymph node metastases, this is still a point of concern for other imaging tests as well. In this review we report the current data on ERUS, presenting both its advantages and limitations, and making a comparison to other staging methods. Recent developments of the technology that might enhance staging accuracy are also discussed.