Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Christe, Andreas; Peters, Alan A.; Drakopoulos, Dionysios; Heverhagen, Johannes T.; Geiser, Thomas; Stathopoulou, Thomai; Christodoulidis, Stergios; Anthimopoulos, Marios; Mougiakakou, Stavroula G.; Ebner, Lukas
• Is Part Of: Investigative radiology. Volume 54:Issue 10(2019)
• Publication Date: 2019-10
• Subjects: idiopathic pulmonary fibrosis -- computed tomography -- nonspecific interstitial pneumonia -- usual interstitial pneumonia -- interstitial lung diseases -- artificial intelligence -- machine learning -- computer-assisted diagnosis; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000574
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objectives: The objective of this study is to assess the performance of a computer-aided diagnosis (CAD) system (INTACT system) for the automatic classification of high-resolution computed tomography images into 4 radiological diagnostic categories and to compare this with the performance of radiologists on the same task. Materials and Methods: For the comparison, a total of 105 cases of pulmonary fibrosis were studied (54 cases of nonspecific interstitial pneumonia and 51 cases of usual interstitial pneumonia). All diagnoses were interstitial lung disease board consensus diagnoses (radiologically or histologically proven cases) and were retrospectively selected from our database. Two subspecialized chest radiologists made a consensual ground truth radiological diagnosis, according to the Fleischner Society recommendations. A comparison analysis was performed between the INTACT system and 2 other radiologists with different years of experience (readers 1 and 2). The INTACT system consists of a sequential pipeline in which first the anatomical structures of the lung are segmented, then the various types of pathological lung tissue are identified and characterized, and this information is then fed to a random forest classifier able to recommend a radiological diagnosis. Results: Reader 1, reader 2, and INTACT achieved similar accuracy for classifying pulmonary fibrosis into the original 4 categories: 0.6, 0.54, and 0.56, respectively, with P > 0.45. The INTACT system achieved an F-score (harmonic mean for precision and recall) of 0.56, whereas the 2 readers, on average, achieved 0.57 ( P = 0.991). For the pooled classification (2 groups, with and without the need for biopsy), reader 1, reader 2, and CAD had similar accuracies of 0.81, 0.70, and 0.81, respectively. The F-score was again similar for the CAD system and the radiologists. The CAD system and the average reader reached F-scores of 0.80 and 0.79 ( P = 0.898). Conclusions: We found that a computer-aided detection algorithm based on machine learning was able to classify idiopathic pulmonary fibrosis with similar accuracy to a human reader.