SNFGE SNFGE
 
Thématique :
- Endoscopie - Imagerie
Originalité :
Très original
Solidité :
Intermédiaire
Doit faire évoluer notre pratique :
Pas encore
 
 
Nom du veilleur :
Professeur Emmanuel CORON
Coup de coeur :
 
 
Gastroenterology
  2018/02  
 
  2018 Feb;154(3):568-575.  
  doi: 10.1053/j.gastro.2017.10.010.  
 
  Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.  
 
  Chen PJ, Lin MC, Lai MJ, Lin JC, Lu HH, Tseng VS  
  https://www.ncbi.nlm.nih.gov/pubmed/29042219  
 
 

Abstract

BACKGROUND & AIMS:

Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps.

METHODS:

We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test.

RESULTS:

In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 ± 0.07 seconds-shorter than the time required by experts (1.54 ± 1.30 seconds) and nonexperts (1.77 ± 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists.

CONCLUSIONS:

We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images.

 

 
Question posée
 
L’analyse des polypes par ordinateur est-elle plus fiable que par l’œil humain ?
 
Question posée
 
Les auteurs de ce travail taïwanais ont développé un algorithme pour prédire l’histologie des polypes coliques à partir de 1476 images de polypes adénomateux et 681 images de polypes hyperplasiques en narrow band imaging. L’ordinateur avait de meilleures performances diagnostiques et en un temps plus court que les 6 endoscopistes qui ont participé à ce travail.
 
Commentaires

Au delà de l’excellente sensibilité (96,3%) et excellente valeur prédictive négative (91,5%), le plus impressionnant est l’indice kappa (=1) affiché par l’ordinateur. De plus, il est certain que ceci n’est que le début d’une aventure qui va repousser les limites de l’interprétation optique…

 
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