Literature DB >> 19034026

Efficacy of computer-aided detection system and thin-slab maximum intensity projection technique in the detection of pulmonary nodules in patients with resected metastases.

Eun-Ah Park1, Jin Mo Goo, Jeong Won Lee, Chang Hyun Kang, Hyun Ju Lee, Chang Hyun Lee, Chang Min Park, Ho Yun Lee, Jung-Gi Im.   

Abstract

OBJECTIVES: To evaluate the efficacy of the computer-aided detection (CAD) system and thin-slab maximum intensity projection (MIP) technique in the detection of pulmonary nodules at multidetector computed tomography (CT) in patients who underwent metastatectomy.
MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and patients' informed consent was waived. Forty-nine consecutive patients who underwent pulmonary metastatectomy were enrolled. Four chest radiologists analyzed preoperative 1-mm section CT images and recorded the locus of each nodule candidate. Afterward, they reevaluated the images once using CAD software and once with thin-slab MIP given the results of 1-mm section CT alone. The reference standard for nodule presence was established by a consensus panel and pathologic records for malignant nodules.
RESULTS: A total of 514 nodules were identified by a consensus panel. Of 212 nodules surgically removed, 121 nodules were malignant. The sensitivity of each observer in detecting malignant nodules with thin-section CT scans alone was 91%, 88%, 87%, and 86% for observers A- to D, respectively. With CAD, sensitivity increased significantly to 95%, 95%, 94%, and 95% (P< 0.05 for observer B-D), and using MIP increased to 94%, 96%, 91%, and 92% (P < 0.05 for observer B-D), respectively. There were no significant differences in sensitivity between CAD and MIP for the detection of malignant nodules. The average number of false-positive findings per patient was 0.8 with thin-section CT alone, 1.1 with CAD, and 1.4 with MIP.
CONCLUSIONS: In candidates for metastatectomy, reading with the aid of either CAD or MIP significantly improved the detection of malignant nodules compared with using thin-section CT alone.

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Year:  2009        PMID: 19034026     DOI: 10.1097/RLI.0b013e318190fcfc

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  11 in total

1.  Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

Authors:  Luca Bogoni; Jane P Ko; Jeffrey Alpert; Vikram Anand; John Fantauzzi; Charles H Florin; Chi Wan Koo; Derek Mason; William Rom; Maria Shiau; Marcos Salganicoff; David P Naidich
Journal:  J Digit Imaging       Date:  2012-12       Impact factor: 4.056

2.  [Image post-processing, part 1: visualization and segmentation].

Authors:  T Baumann; M Langer
Journal:  Radiologe       Date:  2013-09       Impact factor: 0.635

Review 3.  Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening.

Authors:  Chara E Rydzak; Samuel G Armato; Ricardo S Avila; James L Mulshine; David F Yankelevitz; David S Gierada
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

4.  Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

Authors:  Kyung Nyeo Jeon; Jin Mo Goo; Chang Hyun Lee; Youkyung Lee; Ji Yung Choo; Nyoung Keun Lee; Mi-Suk Shim; In Sun Lee; Kwang Gi Kim; David S Gierada; Kyongtae T Bae
Journal:  Invest Radiol       Date:  2012-08       Impact factor: 6.016

5.  [Minimally invasive chest surgery. Is palpation control still necessary with modern computed tomography?].

Authors:  M Krüger; N Zinne; H Shin; R Zhang; C Biancosino; I Kropivnitskaja; F Länger; A Haverich; S Dettmer
Journal:  Chirurg       Date:  2016-02       Impact factor: 0.955

Review 6.  Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology.

Authors:  Douglas E Wood; Ella A Kazerooni; Scott L Baum; George A Eapen; David S Ettinger; Lifang Hou; David M Jackman; Donald Klippenstein; Rohit Kumar; Rudy P Lackner; Lorriana E Leard; Inga T Lennes; Ann N C Leung; Samir S Makani; Pierre P Massion; Peter Mazzone; Robert E Merritt; Bryan F Meyers; David E Midthun; Sudhakar Pipavath; Christie Pratt; Chakravarthy Reddy; Mary E Reid; Arnold J Rotter; Peter B Sachs; Matthew B Schabath; Mark L Schiebler; Betty C Tong; William D Travis; Benjamin Wei; Stephen C Yang; Kristina M Gregory; Miranda Hughes
Journal:  J Natl Compr Canc Netw       Date:  2018-04       Impact factor: 11.908

Review 7.  A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective.

Authors:  Jin Mo Goo
Journal:  Korean J Radiol       Date:  2011-03-03       Impact factor: 3.500

8.  Digital tomosynthesis for evaluating metastatic lung nodules: nodule visibility, learning curves, and reading times.

Authors:  Kyung Hee Lee; Jin Mo Goo; Sang Min Lee; Chang Min Park; Young Eun Bahn; Hyungjin Kim; Yong Sub Song; Eui Jin Hwang
Journal:  Korean J Radiol       Date:  2015-02-27       Impact factor: 3.500

9.  Efficacy of Maximum Intensity Projection of Contrast-Enhanced 3D Turbo-Spin Echo Imaging with Improved Motion-Sensitized Driven-Equilibrium Preparation in the Detection of Brain Metastases.

Authors:  Yun Jung Bae; Byung Se Choi; Kyung Mi Lee; Yeon Hong Yoon; Leonard Sunwoo; Cheolkyu Jung; Jae Hyoung Kim
Journal:  Korean J Radiol       Date:  2017-05-19       Impact factor: 3.500

10.  Diagnostic Accuracy of Maximum Intensity Projection in Diagnosis of Malignant Pulmonary Nodules.

Authors:  Naila Jabeen; Ruby Qureshi; Amjad Sattar; Musarat Baloch
Journal:  Cureus       Date:  2019-11-11
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