Literature DB >> 27666609

Computer-aided diagnosis (CAD) of subsolid nodules: Evaluation of a commercial CAD system.

Joseph Benzakoun1, Sébastien Bommart2, Joël Coste3, Guillaume Chassagnon4, Mathieu Lederlin5, Samia Boussouar6, Marie-Pierre Revel7.   

Abstract

OBJECTIVES: To evaluate the performance of a commercially available CAD system for automated detection and measurement of subsolid nodules.
MATERIALS AND METHODS: The CAD system was tested on 50 pure ground-glass and 50 part-solid nodules (median diameter: 17mm) previously found on standard-dose CT scans in 100 different patients. True nodule detection and the total number of CAD marks were evaluated at different sensitivity settings. The influence of nodule and CT acquisition characteristics was analyzed with logistic regression. Software and manually measured diameters were compared with Spearman and Bland-Altman methods.
RESULTS: With sensitivity adjusted for 3-mm nodule detection, 50/100 (50%) subsolid nodules were detected, at the average cost of 17 CAD marks per CT. These figures were respectively 26/100 (26%) and 2 at the 5-mm setting. At the highest sensitivity setting (2-mm nodule detection), the average number of CAD marks per CT was 41 but the nodule detection rate only increased to 54%. Part-solid nodules were better detected than pure ground glass nodules: 36/50 (72%) versus 14/50 (28%) at the 3-mm setting (p<0.0001), with no influence of the solid component size. Except for the type (i.e. part solid or pure ground glass), no other nodule characteristic influenced the detection rate. High-quality segmentation was obtained for 79 nodules, which for automated measurements correlated well with manual measurements (rho=0.90[0.84-0.93]). All part-solid nodules had software-measured attenuation values above -671Hounsfield units (HU).
CONCLUSION: The detection rate of subsolid nodules by this CAD system was insufficient, but high-quality segmentation was obtained in 79% of cases, allowing automated measurement of size and attenuation.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Computer-aided diagnosis; Image interpretation; Lung neoplasm; Solitary pulmonary nodule

Mesh:

Year:  2016        PMID: 27666609     DOI: 10.1016/j.ejrad.2016.07.011

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  9 in total

Review 1.  Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.

Authors:  Mario Silva; Gianluca Milanese; Valeria Seletti; Alarico Ariani; Nicola Sverzellati
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

2.  Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach.

Authors:  Heewon Chung; Hoon Ko; Se Jeong Jeon; Kwon-Ha Yoon; Jinseok Lee
Journal:  IEEE J Transl Eng Health Med       Date:  2018-05-18       Impact factor: 3.316

3.  Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Fatemeh Homayounieh; Chayanin Nitiwarangkul; Shaunagh McDermott; Brent P Little; Inga T Lennes; Jo-Anne O Shepard; Subba R Digumarthy
Journal:  Quant Imaging Med Surg       Date:  2021-04

4.  Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector.

Authors:  Rui Hao; Yan Qiang; Xiaofei Yan
Journal:  Comput Math Methods Med       Date:  2018-01-08       Impact factor: 2.238

5.  Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

Authors:  Mizuho Nishio; Mitsuo Nishizawa; Osamu Sugiyama; Ryosuke Kojima; Masahiro Yakami; Tomohiro Kuroda; Kaori Togashi
Journal:  PLoS One       Date:  2018-04-19       Impact factor: 3.240

6.  Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom.

Authors:  Seongmin Kang; Tae Hoon Kim; Jae Min Shin; Kyunghwa Han; Ji Young Kim; Baeggi Min; Chul Hwan Park
Journal:  PLoS One       Date:  2020-05-22       Impact factor: 3.240

7.  Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.

Authors:  Chou-Chin Lan; Min-Shiau Hsieh; Jong-Kai Hsiao; Chih-Wei Wu; Hao-Hsiang Yang; Yi Chen; Po-Chun Hsieh; I-Shiang Tzeng; Yao-Kuang Wu
Journal:  Int J Med Sci       Date:  2022-03-06       Impact factor: 3.738

8.  Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.

Authors:  John T Murchison; Gillian Ritchie; David Senyszak; Jeroen H Nijwening; Gerben van Veenendaal; Joris Wakkie; Edwin J R van Beek
Journal:  PLoS One       Date:  2022-05-05       Impact factor: 3.752

9.  [Chinese Experts Consensus on Artificial Intelligence Assisted Management for 
Pulmonary Nodule (2022 Version)].

Authors: 
Journal:  Zhongguo Fei Ai Za Zhi       Date:  2022-03-28
  9 in total

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