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Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

Publication ,  Journal Article
Lyu, P; Neely, B; Solomon, J; Rigiroli, F; Ding, Y; Schwartz, FR; Thomsen, B; Lowry, C; Samei, E; Marin, D
Published in: Eur J Radiol
August 2021

OBJECTIVE: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. METHODS: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. RESULTS: For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. CONCLUSIONS: The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

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Published In

Eur J Radiol

DOI

EISSN

1872-7727

Publication Date

August 2021

Volume

141

Start / End Page

109825

Location

Ireland

Related Subject Headings

  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiation Dosage
  • Pancreatic Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Deep Learning
  • Algorithms
  • 3202 Clinical sciences
 

Citation

APA
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ICMJE
MLA
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Lyu, P., Neely, B., Solomon, J., Rigiroli, F., Ding, Y., Schwartz, F. R., … Marin, D. (2021). Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. Eur J Radiol, 141, 109825. https://doi.org/10.1016/j.ejrad.2021.109825
Lyu, Peijie, Ben Neely, Justin Solomon, Francesca Rigiroli, Yuqin Ding, Fides Regina Schwartz, Brian Thomsen, Carolyn Lowry, Ehsan Samei, and Daniele Marin. “Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.Eur J Radiol 141 (August 2021): 109825. https://doi.org/10.1016/j.ejrad.2021.109825.
Lyu P, Neely B, Solomon J, Rigiroli F, Ding Y, Schwartz FR, et al. Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. Eur J Radiol. 2021 Aug;141:109825.
Lyu, Peijie, et al. “Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.Eur J Radiol, vol. 141, Aug. 2021, p. 109825. Pubmed, doi:10.1016/j.ejrad.2021.109825.
Lyu P, Neely B, Solomon J, Rigiroli F, Ding Y, Schwartz FR, Thomsen B, Lowry C, Samei E, Marin D. Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. Eur J Radiol. 2021 Aug;141:109825.
Journal cover image

Published In

Eur J Radiol

DOI

EISSN

1872-7727

Publication Date

August 2021

Volume

141

Start / End Page

109825

Location

Ireland

Related Subject Headings

  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiation Dosage
  • Pancreatic Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • Deep Learning
  • Algorithms
  • 3202 Clinical sciences