Sage Journals: Discover world-class research

Abstract

Background

High-resolution computed tomography (HRCT) is essential in narrowing the possible differential diagnoses of diffuse and interstitial lung diseases.

Purpose

To investigate the value of a novel computer-based decision support system (CDSS) for facilitating diagnosis of diffuse lung diseases at HRCT.

Material and Methods

A CDSS was developed that includes about 100 different illustrations of the most common HRCT signs and patterns and describes the corresponding pathologies in detail. The logical set-up of the software facilitates a structured evaluation. By selecting one or more CT patterns, the program generates a ranked list of the most likely differential diagnoses. Three independent and blinded radiology residents initially evaluated 40 cases with different lung diseases alone; after at least 12 weeks, observers re-evaluated all cases using the CDSS.

Results

In 40 patients, a total of 113 HRCT patterns were evaluated. The percentage of correctly classified patterns was higher with CDSS (96.8%) compared to assessment without CDSS (90.3%; P < 0.01). Moreover, the percentage of correct diagnosis (81.7% vs. 64.2%) and differential diagnoses (89.2% vs. 38.3%) were superior with CDSS compared to evaluation without CDSS (both P < 0.01).

Conclusion

Addition of a CDSS using a structured approach providing explanations of typical HRCT patterns and graphical illustrations significantly improved the performance of trainees in characterizing and correctly identifying diffuse lung diseases.

Keywords

Interstitial lung disease diffuse lung disease computed tomography decision support system

Get full access to this article

View all access options for this article.

References

Jawad

Chung

Lynch

, et al. Radiological approach to interstitial lung disease: a guide for the nonradiologist. Clin Chest Med 2012; 33:11–26.

Wells

AU.

High-resolution computed tomography in the diagnosis of diffuse lung disease: a clinical perspective. Semin Respir Crit Care Med 2003; 24:347–356.

Horst

Gholipour

Nair

, et al. Differential diagnoses of fibrosing lung diseases. Br J Radiol Open 2019; 1:20190009.

Kim

Lee

Johkoh

, et al. Interstitial lung diseases associated with collagen vascular diseases: radiologic and histopathologic findings. Radiographics 2002; 22:151–165.

Tazelaar

Viggiano

Pickersgill

, et al. Interstitial lung disease in polymyositis and dermatomyositis. Clinical features and prognosis as correlated with histologic findings. Am Rev Respir Dis 1990; 141:727–733.

Raghu

Remy-Jardin

Myers

, et al. Diagnosis of Idiopathic Pulmonary Fibrosis. An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am J Respir Crit Care Med 2018; 198:e44–e68.

Huang

Park

Yan

, et al. Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology 2018; 286:286–295.

Liang

Tang

, et al. Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers. Radiology 2016; 281:279–288.

Firmino

Morais

Mendoca

, et al. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed Eng Online 2014; 13:41.

10.

Suzuki

Machine learning in computer-aided diagnosis of the thorax and colon in CT: A survey. IEICE Trans Inf Syst 2013;E96-D:772–783.

11.

Goo

JM.

A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. Korean J Radiol 2011; 12:145–155.

12.

Gurung

Maataoui

Khan

, et al. Automated detection of lung nodules in multidetector CT: influence of different reconstruction protocols on performance of a software prototype. Rofo 2006; 178:71–77.

13.

Madero Orozco

Vergara Villegas

Cruz

Sanchez VG

, et al. Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online 2015; 14:9.

14.

Chen

Suzuki

MacMahon

Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification. Med Phys 2011; 38:1844–1858.

15.

Way

Sahiner

Chan

, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys 2009; 36:3086–3098.

16.

Jun

Park

Seo

, et al. Development of a computer-aided differential diagnosis system to distinguish between usual interstitial pneumonia and non-specific interstitial pneumonia using texture- and shape-based hierarchical classifiers on HRCT images. J Digit Imaging 2018; 31:235–244.

17.

Sluimer

Prokop

Hartmann

, et al. Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung. Med Phys 2006; 33:2610–2620.

18.

Sluimer

van Waes

Viergever

, et al. Computer-aided diagnosis in high resolution CT of the lungs. Med Phys 2003; 30:3081–3090.

19.

Salisbury

Gross

Chughtai

, et al. Development and validation of a radiological diagnosis model for hypersensitivity pneumonitis. Eur Respir J 2018; 52:1800443.

20.

Elicker

Pereira

Webb

, et al. High-resolution computed tomography patterns of diffuse interstitial lung disease with clinical and pathological correlation. J Bras Pneumol 2008; 34:715–744.

21.

Antoniou

Margaritopoulos

Tomassetti

, et al. Interstitial lung disease. Eur Respir Rev 2014; 23:40–54.

22.

Collins

CT signs and patterns of lung disease. Radiol Clin North Am 2001; 39:1115–1135.

23.

Devaraj

Imaging: how to recognise idiopathic pulmonary fibrosis. Eur Respir Rev 2014; 23:215–219.

24.

Hering

Tuengerthal

Kraus

[ Standardized CT/HRCT-classification of the German Federal Republic for work and environmental related thoracic diseases]. Radiologe 2004; 44:500–511.

25.

Jacobi

Thalhammer

Grundmuster im CT der Lunge und ihre Differenzialdiagnose. Radiologie up2date 2006; 6:311–334.

26.

Gotway

Reddy

Webb

, et al. High-resolution CT of the lung: patterns of disease and differential diagnoses. Radiol Clin North Am 2005; 43:513–542, viii.

27.

Cicchetti

Sparrow

SA.

Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. Am J Ment Defic 1981; 86:127–137.

Development and evaluation of a computer-based decision support system for diffuse lung diseases at high-resolution computed tomography