Dongsuk Shin1, Michelle H Lee2, Alexandros D Polydorides3, Mark C Pierce4, Peter M Vila5, Neil D Parikh6, Daniel G Rosen7, Sharmila Anandasabapathy2, Rebecca R Richards-Kortum8. 1. Department of Bioengineering, Rice University, Houston, Texas, USA; Department of Neurosurgery, The University of Texas Medical School at Houston, Houston, Texas, USA. 2. Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA. 3. Department of Pathology, The Mount Sinai Medical Center, New York, New York, USA. 4. Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA. 5. Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA; Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA. 6. Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA; Division of Digestive Diseases, Yale-New Haven Hospital, New Haven, Connecticut, USA. 7. Department of Pathology, Baylor College of Medicine, Houston, Texas, USA. 8. Department of Bioengineering, Rice University, Houston, Texas, USA.
Abstract
BACKGROUND AND AIMS: Previous studies show that microendoscopic images can be interpreted visually to identify the presence of neoplasia in patients with Barrett's esophagus (BE), but this approach is subjective and requires clinical expertise. This study describes an approach for quantitative image analysis of microendoscopic images to identify neoplastic lesions in patients with BE. METHODS: Images were acquired from 230 sites from 58 patients by using a fiberoptic high-resolution microendoscope during standard endoscopic procedures. Images were analyzed by a fully automated image processing algorithm, which automatically selected a region of interest and calculated quantitative image features. Image features were used to develop an algorithm to identify the presence of neoplasia; results were compared with a histopathology diagnosis. RESULTS: A sequential classification algorithm that used image features related to glandular and cellular morphology resulted in a sensitivity of 84% and a specificity of 85%. Applying the algorithm to an independent validation set resulted in a sensitivity of 88% and a specificity of 85%. CONCLUSIONS: This pilot study demonstrates that automated analysis of microendoscopic images can provide an objective, quantitative framework to assist clinicians in evaluating esophageal lesions from patients with BE. ( CLINICAL TRIAL REGISTRATION NUMBER: NCT01384227 and NCT02018367.).
BACKGROUND AND AIMS: Previous studies show that microendoscopic images can be interpreted visually to identify the presence of neoplasia in patients with Barrett's esophagus (BE), but this approach is subjective and requires clinical expertise. This study describes an approach for quantitative image analysis of microendoscopic images to identify neoplastic lesions in patients with BE. METHODS: Images were acquired from 230 sites from 58 patients by using a fiberoptic high-resolution microendoscope during standard endoscopic procedures. Images were analyzed by a fully automated image processing algorithm, which automatically selected a region of interest and calculated quantitative image features. Image features were used to develop an algorithm to identify the presence of neoplasia; results were compared with a histopathology diagnosis. RESULTS: A sequential classification algorithm that used image features related to glandular and cellular morphology resulted in a sensitivity of 84% and a specificity of 85%. Applying the algorithm to an independent validation set resulted in a sensitivity of 88% and a specificity of 85%. CONCLUSIONS: This pilot study demonstrates that automated analysis of microendoscopic images can provide an objective, quantitative framework to assist clinicians in evaluating esophageal lesions from patients with BE. ( CLINICAL TRIAL REGISTRATION NUMBER: NCT01384227 and NCT02018367.).
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