Literature DB >> 28122122

The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population.

Stefano Young1, Pechin Lo1, Grace Kim1, Matthew Brown1, John Hoffman1, William Hsu1, Wasil Wahi-Anwar1, Carlos Flores1, Grace Lee1, Frederic Noo2, Jonathan Goldin1, Michael McNitt-Gray1.   

Abstract

PURPOSE: Lung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population.
METHODS: The raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed.
RESULTS: From the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case.
CONCLUSIONS: CAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.
© 2017 American Association of Physicists in Medicine.

Entities:  

Mesh:

Year:  2017        PMID: 28122122      PMCID: PMC5719865          DOI: 10.1002/mp.12128

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  18 in total

1.  Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial.

Authors:  Frederick J Larke; Randell L Kruger; Christopher H Cagnon; Michael J Flynn; Michael M McNitt-Gray; Xizeng Wu; Phillip F Judy; Dianna D Cody
Journal:  AJR Am J Roentgenol       Date:  2011-11       Impact factor: 3.959

Review 2.  Iterative reconstruction methods in X-ray CT.

Authors:  Marcel Beister; Daniel Kolditz; Willi A Kalender
Journal:  Phys Med       Date:  2012-02-10       Impact factor: 2.685

3.  Performance evaluation of a computer-aided detection algorithm for solid pulmonary nodules in low-dose and standard-dose MDCT chest examinations and its influence on radiologists.

Authors:  M Das; G Mühlenbruch; S Heinen; A H Mahnken; M Salganicoff; S Stanzel; R W Günther; J E Wildberger
Journal:  Br J Radiol       Date:  2008-11       Impact factor: 3.039

4.  CT dose index and patient dose: they are not the same thing.

Authors:  Cynthia H McCollough; Shuai Leng; Lifeng Yu; Dianna D Cody; John M Boone; Michael F McNitt-Gray
Journal:  Radiology       Date:  2011-05       Impact factor: 11.105

5.  Computer-aided detection of artificial pulmonary nodules using an ex vivo lung phantom: influence of exposure parameters and iterative reconstruction.

Authors:  Mark O Wielpütz; Jacek Wroblewski; Mathieu Lederlin; Julien Dinkel; Monika Eichinger; M Koenigkam-Santos; Jürgen Biederer; Hans-Ulrich Kauczor; Michael U Puderbach; Bertram J Jobst
Journal:  Eur J Radiol       Date:  2015-02-16       Impact factor: 3.528

6.  Variability in CT lung-nodule volumetry: Effects of dose reduction and reconstruction methods.

Authors:  Stefano Young; Hyun J Grace Kim; Moe Moe Ko; War War Ko; Carlos Flores; Michael F McNitt-Gray
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

7.  A low dose simulation tool for CT systems with energy integrating detectors.

Authors:  Stanislav Zabić; Qiu Wang; Thomas Morton; Kevin M Brown
Journal:  Med Phys       Date:  2013-03       Impact factor: 4.071

8.  Toward clinically usable CAD for lung cancer screening with computed tomography.

Authors:  Matthew S Brown; Pechin Lo; Jonathan G Goldin; Eran Barnoy; Grace Hyun J Kim; Michael F McNitt-Gray; Denise R Aberle
Journal:  Eur Radiol       Date:  2014-07-24       Impact factor: 5.315

9.  Electronic noise in CT detectors: Impact on image noise and artifacts.

Authors:  Xinhui Duan; Jia Wang; Shuai Leng; Bernhard Schmidt; Thomas Allmendinger; Katharine Grant; Thomas Flohr; Cynthia H McCollough
Journal:  AJR Am J Roentgenol       Date:  2013-10       Impact factor: 3.959

10.  Use of Water Equivalent Diameter for Calculating Patient Size and Size-Specific Dose Estimates (SSDE) in CT: The Report of AAPM Task Group 220.

Authors:  Cynthia McCollough; Donovan M Bakalyar; Maryam Bostani; Samuel Brady; Kristen Boedeker; John M Boone; H Heather Chen-Mayer; Olav I Christianson; Shuai Leng; Baojun Li; Michael F McNitt-Gray; Roy A Nilsen; Mark P Supanich; Jia Wang
Journal:  AAPM Rep       Date:  2014-09
View more
  5 in total

1.  Technical Note: FreeCT_ICD: An open-source implementation of a model-based iterative reconstruction method using coordinate descent optimization for CT imaging investigations.

Authors:  John M Hoffman; Frédéric Noo; Stefano Young; Scott S Hsieh; Michael McNitt-Gray
Journal:  Med Phys       Date:  2018-06-01       Impact factor: 4.071

2.  Technical Note: Design and implementation of a high-throughput pipeline for reconstruction and quantitative analysis of CT image data.

Authors:  John Hoffman; Nastaran Emaminejad; Muhammad Wahi-Anwar; Grace H Kim; Matthew Brown; Stefano Young; Michael McNitt-Gray
Journal:  Med Phys       Date:  2019-04-03       Impact factor: 4.071

3.  Vasculature surrounding a nodule: A novel lung cancer biomarker.

Authors:  Xiaohua Wang; Joseph K Leader; Renwei Wang; David Wilson; James Herman; Jian-Min Yuan; Jiantao Pu
Journal:  Lung Cancer       Date:  2017-10-27       Impact factor: 5.705

4.  A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.

Authors:  Lorenzo Vassallo; Alberto Traverso; Michelangelo Agnello; Christian Bracco; Delia Campanella; Gabriele Chiara; Maria Evelina Fantacci; Ernesto Lopez Torres; Antonio Manca; Marco Saletta; Valentina Giannini; Simone Mazzetti; Michele Stasi; Piergiorgio Cerello; Daniele Regge
Journal:  Eur Radiol       Date:  2018-06-15       Impact factor: 5.315

5.  Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters.

Authors:  Nastaran Emaminejad; Muhammad Wasil Wahi-Anwar; Grace Hyun J Kim; William Hsu; Matthew Brown; Michael McNitt-Gray
Journal:  Med Phys       Date:  2021-04-13       Impact factor: 4.506

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.