Literature DB >> 2783501

The diagnosis of pulmonary nodules: comparison between standard and inverse digitized images and conventional chest radiographs.

M E Sheline1, I Brikman, D M Epstein, J L Mezrich, H L Kundel, R L Arenson.   

Abstract

We compared plain chest radiographs, standard (bones white) digitized images, and inverse-intensity (bones black) images to determine their ability to identify pathologically confirmed malignant pulmonary nodules. The images were digitized by using a photo-optical laser scanner and were displayed on a 1024 x 1024 x 8 bit system capable of operator-controlled magnification (2x or 4x) and nonlinear (logarithmic/exponential) contrast transformation in both standard and inverse-intensity modes. Receiver-operator curve analysis was used to study the detection performance of six observers who viewed 40 images obtained in 15 normal subjects and 25 abnormal subjects. There was no statistically significant difference in the area under the ROC curve between the standard digital images and the plain chest radiographs. However, ROC areas were significantly greater (p less than or equal to .05) for inverse-intensity digital images when compared with either standard-intensity digital images or plain chest radiographs. These results suggest that inverse-intensity images may have some advantages in the detection of pulmonary nodules.

Mesh:

Year:  1989        PMID: 2783501     DOI: 10.2214/ajr.152.2.261

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  9 in total

Review 1.  Literature review: picture archiving and communication system.

Authors:  U P Schmiedl; A H Rowberg
Journal:  J Digit Imaging       Date:  1990-11       Impact factor: 4.056

2.  The digital imaging workstation. 1990.

Authors:  Ronald L Arenson; Dev P Chakraborty; Sridhar B Seshadri; Harold L Kundel
Journal:  J Digit Imaging       Date:  2003-03       Impact factor: 4.056

3.  Grey-scale inversion improves detection of lung nodules.

Authors:  J W Robinson; J T Ryan; M F McEntee; S J Lewis; M G Evanoff; L A Rainford; P C Brennan
Journal:  Br J Radiol       Date:  2012-05-09       Impact factor: 3.039

Review 4.  Missed lung cancer: when, where, and why?

Authors:  Annemilia Del Ciello; Paola Franchi; Andrea Contegiacomo; Giuseppe Cicchetti; Lorenzo Bonomo; Anna Rita Larici
Journal:  Diagn Interv Radiol       Date:  2017 Mar-Apr       Impact factor: 2.630

5.  Grey-scale inversion improves detection of lung nodules.

Authors:  J W Robinson; J T Ryan; M F McEntee; S J Lewis; M G Evanoff; L A Rainford; P C Brennan
Journal:  Br J Radiol       Date:  2013-01       Impact factor: 3.039

6.  Grayscale inversion radiographic view provided improved intra- and inter-observer reliabilities in measuring spinopelvic parameters in asymptomatic adult population.

Authors:  Weixiang Sun; Jin Zhou; Xiaodong Qin; Leilei Xu; Xinxin Yuan; Yang Li; Yong Qiu; Zezhang Zhu
Journal:  BMC Musculoskelet Disord       Date:  2016-10-03       Impact factor: 2.362

7.  Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method.

Authors:  Akitoshi Shimazaki; Daiju Ueda; Antoine Choppin; Akira Yamamoto; Takashi Honjo; Yuki Shimahara; Yukio Miki
Journal:  Sci Rep       Date:  2022-01-14       Impact factor: 4.379

8.  The efficacy of the reverse contrast mode in digital radiography for the detection of proximal dentinal caries.

Authors:  Shimasadat Miri; Sandra Mehralizadeh; Donya Sadri; Mahmood Reza Kalantar Motamedi; Parisa Soltani
Journal:  Imaging Sci Dent       Date:  2015-09-09

9.  The diagnostic value of grey-scale inversion technique in chest radiography.

Authors:  Roberta Eufrasia Ledda; Mario Silva; Nicole McMichael; Carlotta Sartorio; Cristina Branchi; Gianluca Milanese; Sundeep M Nayak; Nicola Sverzellati
Journal:  Radiol Med       Date:  2022-01-18       Impact factor: 3.469

  9 in total

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