Prospective development and validation of a volumetric laser endomicroscopy computer algorithm for detection of Barrett’s neoplasia
Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett’s esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to tr...
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| Vydáno v: | Gastrointestinal endoscopy Ročník 93; číslo 4; s. 871 - 879 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Inc
01.04.2021
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| ISSN: | 0016-5107, 1097-6779, 1097-6779 |
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| Abstract | Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett’s esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia.
The multicenter, VLE PREDICT study, prospectively enrolled 47 patients with BE. In total, 229 nondysplastic BE and 89 neoplastic (high-grade dysplasia/esophageal adenocarcinoma) targets were laser marked under VLE guidance and subsequently underwent a biopsy for histologic diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 nondysplastic BE and 38 neoplastic targets) and validated on a separate test set from patients 23 to 47 (95 nondysplastic BE and 51 neoplastic targets). The performance of the algorithm was benchmarked against the performance of 10 VLE experts.
Using the training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95%, and specificity of 92%. When performance was assessed on the test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%.
We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts. (The Netherlands National Trials Registry (NTR) number: NTR 6728.) |
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| AbstractList | Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett's esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia.
The multicenter, VLE PREDICT study, prospectively enrolled 47 patients with BE. In total, 229 nondysplastic BE and 89 neoplastic (high-grade dysplasia/esophageal adenocarcinoma) targets were laser marked under VLE guidance and subsequently underwent a biopsy for histologic diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 nondysplastic BE and 38 neoplastic targets) and validated on a separate test set from patients 23 to 47 (95 nondysplastic BE and 51 neoplastic targets). The performance of the algorithm was benchmarked against the performance of 10 VLE experts.
Using the training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95%, and specificity of 92%. When performance was assessed on the test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%.
We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts. (The Netherlands National Trials Registry (NTR) number: NTR 6728.). Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett's esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia.BACKGROUND AND AIMSVolumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett's esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia.The multicenter, VLE PREDICT study, prospectively enrolled 47 patients with BE. In total, 229 nondysplastic BE and 89 neoplastic (high-grade dysplasia/esophageal adenocarcinoma) targets were laser marked under VLE guidance and subsequently underwent a biopsy for histologic diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 nondysplastic BE and 38 neoplastic targets) and validated on a separate test set from patients 23 to 47 (95 nondysplastic BE and 51 neoplastic targets). The performance of the algorithm was benchmarked against the performance of 10 VLE experts.METHODSThe multicenter, VLE PREDICT study, prospectively enrolled 47 patients with BE. In total, 229 nondysplastic BE and 89 neoplastic (high-grade dysplasia/esophageal adenocarcinoma) targets were laser marked under VLE guidance and subsequently underwent a biopsy for histologic diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 nondysplastic BE and 38 neoplastic targets) and validated on a separate test set from patients 23 to 47 (95 nondysplastic BE and 51 neoplastic targets). The performance of the algorithm was benchmarked against the performance of 10 VLE experts.Using the training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95%, and specificity of 92%. When performance was assessed on the test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%.RESULTSUsing the training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95%, and specificity of 92%. When performance was assessed on the test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%.We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts. (The Netherlands National Trials Registry (NTR) number: NTR 6728.).CONCLUSIONSWe developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts. (The Netherlands National Trials Registry (NTR) number: NTR 6728.). |
| Author | Lightdale, Charles J. Meijer, Sybren L. Konda, Vani J.A. Curvers, Wouter L. Kahn, Allon Bergman, Jacques J. Vieth, Michael Ganguly, Eric K. Struyvenberg, Maarten R. de With, Peter H.N. Schoon, Erik J. Trindade, Arvind J. Sethi, Amrita Tearney, Gary J. de Groof, Albert J. Pleskow, Douglas K. Leggett, Cadman L. Fonollà, Roger Wallace, Michael B. Pouw, Roos E. van der Sommen, Fons Weusten, Bas L.A.M. Smith, Michael S. Wolfsen, Herbert C. |
| Author_xml | – sequence: 1 givenname: Maarten R. surname: Struyvenberg fullname: Struyvenberg, Maarten R. organization: Department of Gastroenterology and Hepatology, Amsterdam UMC, location AMC, Amsterdam, the Netherlands – sequence: 2 givenname: Albert J. surname: de Groof fullname: de Groof, Albert J. organization: Department of Gastroenterology and Hepatology, Amsterdam UMC, location AMC, Amsterdam, the Netherlands – sequence: 3 givenname: Roger surname: Fonollà fullname: Fonollà, Roger organization: Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, the Netherlands – sequence: 4 givenname: Fons surname: van der Sommen fullname: van der Sommen, Fons organization: Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, the Netherlands – sequence: 5 givenname: Peter H.N. surname: de With fullname: de With, Peter H.N. organization: Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, the Netherlands – sequence: 6 givenname: Erik J. surname: Schoon fullname: Schoon, Erik J. organization: Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, the Netherlands – sequence: 7 givenname: Bas L.A.M. surname: Weusten fullname: Weusten, Bas L.A.M. organization: Department of Gastroenterology and Hepatology, St. Antonius Hospital, Nieuwegein, the Netherlands – sequence: 8 givenname: Cadman L. surname: Leggett fullname: Leggett, Cadman L. organization: Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA – sequence: 9 givenname: Allon surname: Kahn fullname: Kahn, Allon organization: Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona, USA – sequence: 10 givenname: Arvind J. surname: Trindade fullname: Trindade, Arvind J. organization: Division of Gastroenterology and Hepatology, Zucker School of Medicine at Hofstra/Northwell. Long Island Jewish Medical Center, New Hyde Park, New York, USA – sequence: 11 givenname: Eric K. surname: Ganguly fullname: Ganguly, Eric K. organization: Department of Gastroenterology and Hepatology, University of Vermont Medical Center, Burlington, Vermont, USA – sequence: 12 givenname: Vani J.A. surname: Konda fullname: Konda, Vani J.A. organization: Department of Gastroenterology and Hepatology, Baylor University Medical Center at Dallas, Dallas, Texas, USA – sequence: 13 givenname: Charles J. surname: Lightdale fullname: Lightdale, Charles J. organization: Division of Gastroenterology and Hepatology, New York-Presbyterian Hospital, New York, New York, USA – sequence: 14 givenname: Douglas K. surname: Pleskow fullname: Pleskow, Douglas K. organization: Department of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA – sequence: 15 givenname: Amrita surname: Sethi fullname: Sethi, Amrita organization: Department of Gastroenterology and Hepatology, Columbia University Medical Center, New York, New York, USA – sequence: 16 givenname: Michael S. surname: Smith fullname: Smith, Michael S. organization: Division of Gastroenterology and Hepatology, Mount Sinai West & Mount Sinai St. Luke's Hospitals, New York, New York, USA – sequence: 17 givenname: Michael B. surname: Wallace fullname: Wallace, Michael B. organization: Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida, USA – sequence: 18 givenname: Herbert C. surname: Wolfsen fullname: Wolfsen, Herbert C. organization: Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida, USA – sequence: 19 givenname: Gary J. surname: Tearney fullname: Tearney, Gary J. organization: Department of Pathology, Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, USA – sequence: 20 givenname: Sybren L. surname: Meijer fullname: Meijer, Sybren L. organization: Department of Pathology, Amsterdam UMC, location AMC, Amsterdam, the Netherlands – sequence: 21 givenname: Michael surname: Vieth fullname: Vieth, Michael organization: Institute of Pathology, Bayreuth Clinic, Bayreuth, Germany – sequence: 22 givenname: Roos E. surname: Pouw fullname: Pouw, Roos E. organization: Department of Gastroenterology and Hepatology, Amsterdam UMC, location AMC, Amsterdam, the Netherlands – sequence: 23 givenname: Wouter L. surname: Curvers fullname: Curvers, Wouter L. organization: Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, the Netherlands – sequence: 24 givenname: Jacques J. surname: Bergman fullname: Bergman, Jacques J. organization: Department of Gastroenterology and Hepatology, Amsterdam UMC, location AMC, Amsterdam, the Netherlands |
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