Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images

Christe, Andreas; Peters, Alan Arthur; Drakopoulos, Dionysios; Heverhagen, Johannes; Geiser, Thomas; Stathopoulou, Thomai; Christodoulidis, Stergios; Anthimopoulos, Marios; Mougiakakou, Stavroula; Ebner, Lukas (2019). Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Investigative radiology, 54(10), pp. 627-632. Wolters Kluwer Health 10.1097/RLI.0000000000000574

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Pneumology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology

UniBE Contributor:

Christe, Andreas; Peters, Alan Arthur; Heverhagen, Johannes; Geiser, Thomas; Stathopoulou, Thomai; Christodoulidis, Stergios; Anthimopoulos, Marios; Mougiakakou, Stavroula and Ebner, Lukas

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

0020-9996

Publisher:

Wolters Kluwer Health

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

19 Jul 2019 06:59

Last Modified:

24 Oct 2019 20:20

Publisher DOI:

10.1097/RLI.0000000000000574

BORIS DOI:

10.7892/boris.131176

URI:

https://boris.unibe.ch/id/eprint/131176

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