Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement.

Eur Radiol

Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, Korea.

Published: February 2022


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Objectives: To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization.

Methods: Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics.

Results: The five readers reported 139-151 negative screening results without CAD and 126-142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (p < 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).

Conclusions: Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases.

Key Points: • Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65. • The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%). • In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00330-021-08202-3DOI Listing

Publication Analysis

Top Keywords

inter-reader agreement
24
cad
16
application cad
16
lung-rads categorization
12
cad cad
12
risk-dominant nodules
12
lung-rads category
12
fleiss kappa
12
substantial management
12
lung-rads
9

Similar Publications

3D isotropic FastView MRI localizer allows reliable torsion measurements of the lower limb.

Eur Radiol Exp

September 2025

Department of Orthopaedics and Trauma Surgery, Orthopaedic Oncology, Musculoskeletal University Center Munich (MUM), University Hospital, LMU Munich, Munich, Germany.

Computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used to assess femoral and tibial torsion. While CT offers high spatial resolution, it involves ionizing radiation. MRI avoids radiation but requires multiple sequences and extended acquisition time.

View Article and Find Full Text PDF

Objective: To evaluate multiparametric MRI features of pediatric soft-tissue sarcomas, comparing pre-treatment and post-treatment features, and assessing correlation with clinical outcomes.

Materials And Methods: Retrospective cohort study, including pediatric patients (≤ 18 years) with histologically-confirmed soft-tissue sarcomas who underwent MRI with anatomic and functional sequences in consecutive series. Post-treatment MRI was available for a subset, and features were recorded by two readers.

View Article and Find Full Text PDF

Introduction: Precise prediction of pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT) in rectal cancer may identify candidates for non-operative management. The optimal selection of diagnostic tools is therefore of major clinical importance.

Methods: Clinical, laboratory, endoscopic and radiological data of patients with rectal cancer treated with nCRT and surgery at an academic medical center from 2010 to 2020 were retrospectively collected.

View Article and Find Full Text PDF

Deep learning-based detection of ascending aortic dilatation on chest radiographs: A diagnostic study.

Eur J Radiol

August 2025

Department of Radiology, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Beomeo-ri, Mulgeum-eup, Yangsan-si 626-770 Gyeongsangnam-do, Republic of Korea. Electronic address: kschoo061

Objectives: This study externally tests the performance of an artificial intelligence algorithm (AI) for diagnosing ascending aortic dilatation (AAD) using PA view chest radiography (PA CXR).

Materials And Methods: Two retrospectively collected cohorts with paired CXR/CT within 30 days (Group 1) and 90 days (Group 2) were gathered as external test sets. The performance of AI (DeepCatch X Aorta v1.

View Article and Find Full Text PDF

Purpose: This study evaluated inter-/intra-reader agreement with the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 to improve the detection rate of prostate cancer.

Materials And Methods: We enrolled 210 patients who underwent multiparametric magnetic resonance imaging (mpMRI) for clinically suspected or diagnosed prostate cancer.

View Article and Find Full Text PDF