Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules
- PMID: 10628457
- DOI: 10.2214/ajr.174.1.1740071
Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules
Abstract
Objective: We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html ) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis.
Materials and methods: One hundred and fifty-four conventional chest radiographs with a lung nodule and 93 radiographs without a nodule were selected from 14 medical centers and were digitized by a laser digitizer with a 2048 x 2048 matrix size (0.175-mm pixels) and a 12-bit gray scale. Lung nodule images were classified into five groups according to the degrees of subtlety shown. The observations of 20 participating radiologists were subjected to ROC analysis for detecting solitary pulmonary nodules. Experimental results (areas under the curve, Az) obtained from observer studies were used for characterization of five groups of lung nodules with different degrees of subtlety.
Results: ROC analysis showed that the database included a wide range of various nodules yielding Az values from 0.574 to 0.991 for the five categories of cases for different degrees of subtlety.
Conclusion: This database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.
Similar articles
-
[Comparison of digital radiography and conventional X-ray in the diagnosis of solitary pulmonary nodule with receiver operating characteristic analysis].Di Yi Jun Yi Da Xue Xue Bao. 2003 Jun;23(6):621-3. Di Yi Jun Yi Da Xue Xue Bao. 2003. PMID: 12810395 Chinese.
-
Bone suppressed images improve radiologists' detection performance for pulmonary nodules in chest radiographs.Eur J Radiol. 2013 Dec;82(12):2399-405. doi: 10.1016/j.ejrad.2013.09.016. Epub 2013 Sep 25. Eur J Radiol. 2013. PMID: 24113431
-
Detection of lung nodules on digital chest radiographs: potential usefulness of a new contralateral subtraction technique.Radiology. 2002 Apr;223(1):199-203. doi: 10.1148/radiol.2231010344. Radiology. 2002. PMID: 11930067
-
Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization.AJR Am J Roentgenol. 2011 May;196(5):W535-41. doi: 10.2214/AJR.10.4816. AJR Am J Roentgenol. 2011. PMID: 21512042
-
Receiver operating characteristic (ROC) curve: practical review for radiologists.Korean J Radiol. 2004 Jan-Mar;5(1):11-8. doi: 10.3348/kjr.2004.5.1.11. Korean J Radiol. 2004. PMID: 15064554 Free PMC article. Review.
Cited by
-
Detection of pulmonary nodules in chest radiographs: novel cost function for effective network training with purely synthesized datasets.Int J Comput Assist Radiol Surg. 2024 Oct;19(10):1991-2000. doi: 10.1007/s11548-024-03227-7. Epub 2024 Jul 13. Int J Comput Assist Radiol Surg. 2024. PMID: 39003437 Free PMC article.
-
Mathematical morphology-based approach to the enhancement of morphological features in medical images.J Clin Bioinforma. 2011 Dec 16;1:33. doi: 10.1186/2043-9113-1-33. J Clin Bioinforma. 2011. PMID: 22177340 Free PMC article.
-
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases.Comput Biol Med. 2021 May;132:104348. doi: 10.1016/j.compbiomed.2021.104348. Epub 2021 Mar 19. Comput Biol Med. 2021. PMID: 33774272 Free PMC article.
-
A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.Sci Rep. 2023 Jan 16;13(1):791. doi: 10.1038/s41598-023-27815-w. Sci Rep. 2023. PMID: 36646735 Free PMC article.
-
Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.Sensors (Basel). 2023 Jul 21;23(14):6585. doi: 10.3390/s23146585. Sensors (Basel). 2023. PMID: 37514877 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources