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Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction

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Abstract
Background: Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking.

Purpose: To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance.

Materials and Methods: CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit.

Results: A total of 308 patients (mean age 6 standard deviation, 62 years 6 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years 6 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P < .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P < .001 for 5 mm).

Conclusion: Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. (C) RSNA, 2021
Author(s)
김우일도경현박소희박현호서준범이상민정규환
Issued Date
2021
Type
Article
Keyword
AgedDeep Learning*Computer-Assisted / methods*FemaleHumansLung Neoplasms / diagnostic imaging*MaleMiddle AgedMultiple Pulmonary Nodules / diagnostic imaging*Computer-Assisted / methodsRetrospective StudiesX-Ray Computed / methods*
DOI
10.1148/radiol.2021203387
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7340
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_crossref_primary_10_1148_radiol_2021203387&amp;context=PC&amp;vid=ULSAN&amp;lang=ko_KR&amp;search_scope=default_scope&amp;adaptor=primo_central_multiple_fe&amp;tab=default_tab&amp;query=any,contains,Computer-aided%20Detection%20of%20Subsolid%20Nodules%20at%20Chest%20CT:%20Improved%20Performance%20with%20Deep%20Learning-based%20CT%20Section%20Thickness%20Reduction&amp;offset=0&amp;pcAvailability=true
Publisher
RADIOLOGY
Location
미국
Language
영어
ISSN
0033-8419
Citation Volume
299
Citation Number
1
Citation Start Page
211
Citation End Page
219
Appears in Collections:
Medicine > Medicine
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