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OtherAI/Advanced Image Analysis
Open Access

18F-FDG PET maximum intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients

Kibrom Berihu Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont and Irène Buvat
Journal of Nuclear Medicine June 2022, jnumed.121.263501; DOI: https://doi.org/10.2967/jnumed.121.263501
Kibrom Berihu Girum
1 LITO laboratory, UMR 1288 Inserm, Institut Curie, University Paris Saclay, France;
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Louis Rebaud
2 LITO laboratory, UMR 1288 Inserm, Institut Curie, University Paris Saclay; Research and Clinical Collaborations, Siemens Medical Solutions USA, 810 Innovation Dr, Knoxville, TN 37932, United states, France;
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Anne-Ségolène Cottereau
3 LITO laboratory, UMR 1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France; Department of Nuclear Medicine, Cochin Hospital, AP-HP, Paris Descartes University, Paris, France, France;
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Michel Meignan
4 Lysa Imaging, Henri Mondor University Hospital, AP-HP, University Paris East, Creteil, France, France;
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Jérôme Clerc
5 Department of Nuclear Medicine, Cochin Hospital, AP-HP, Paris Descartes University, Paris, France, France;
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Laetitia Vercellino
6 Department of Nuclear Medicine, Saint-Louis Hospital, AP-HP, Paris, France, France;
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Olivier Casasnovas
7 Department of Hematology, University Hospital of Dijon, Dijon, France, France;
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Franck Morschhauser
8 Department of Hematology, Claude Huriez hospital, University Lille, EA 7365, Research Group on Injectable Forms and Associated Technologies, Lille, France, France;
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Catherine Thieblemont
9 Department of Hematology, Saint Louis Hospital, AP-HP, Paris, France, France
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Irène Buvat
1 LITO laboratory, UMR 1288 Inserm, Institut Curie, University Paris Saclay, France;
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Abstract

Background: Total metabolic tumor volume (TMTV) and tumor dissemination (Dmax) calculated from baseline 18F-FDG PET/CT images are prognostic biomarkers in Diffuse Large B-cell lymphoma (DLBCL) patients. Yet, their automated calculation remains challenging. Purpose: To investigate whether TMTV and Dmax features could be replaced by surrogate features automatically calculated using an artificial intelligence (AI) algorithm from only two maximum intensity projections (MIP) of the whole-body 18F-FDG PET images. Methods: Two cohorts of DLBCL patients from the REMARC (NCT01122472) and LNH073B (NCT00498043) trials were retrospectively analyzed. Experts delineated lymphoma lesions from the baseline whole-body 18F-FDG PET/CT images, from which TMTV and Dmax were measured. Coronal and sagittal MIP images and associated 2D reference lesion masks were calculated. An AI algorithm was trained on the REMARC MIP data to segment lymphoma regions. The AI algorithm was then used to estimate surrogate TMTV (sTMTV) and surrogate Dmax (sDmax) on both datasets. The ability of the original and surrogate TMTV and Dmax to stratify patients was compared. Results: 382 patients (mean age, 62.1 years ±13.4 [standard deviation]; 207 men) were evaluated. sTMTV was highly correlated with TMTV for REMARC and LNH073B datasets (Spearman r=0.878 and r=0.752 respectively), and so were sDmax and Dmax (r=0.709 and r=0.714 respectively). The hazard ratios (HR) for progression free survival of volume and MIP-based features derived using AI were similar, e.g., TMTV: 11.24 (95% confidence interval (CI): 2.10-46.20), sTMTV: 11.81 (95% CI: 3.29-31.77), and Dmax: 9.0 (95% CI: 2.53-23.63), sDmax: 12.49 (95% CI: 3.42-34.50). Conclusion: Surrogate TMTV and Dmax calculated from only 2 PET MIP images are prognostic biomarkers in DLBCL patients and can be automatically estimated using an AI algorithm.

  • Image Processing
  • Oncology: Lymphoma
  • PET/CT
  • 18F FDG PET/CT
  • Artificial intelligence
  • DLBCL
  • Dissemination
  • Metabolic tumor volume

Footnotes

  • Immediate Open Access: Creative Commons Attribution 4.0 International License (CC BY) allows users to share and adapt with attribution, excluding materials credited to previous publications. License: https://creativecommons.org/licenses/by/4.0/. Details: https://jnm.snmjournals.org/page/permissions.

  • Copyright © 2022 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

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Journal of Nuclear Medicine: 63 (7)
Journal of Nuclear Medicine
Vol. 63, Issue 7
July 1, 2022
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18F-FDG PET maximum intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients
Kibrom Berihu Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat
Journal of Nuclear Medicine Jun 2022, jnumed.121.263501; DOI: 10.2967/jnumed.121.263501

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18F-FDG PET maximum intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients
Kibrom Berihu Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat
Journal of Nuclear Medicine Jun 2022, jnumed.121.263501; DOI: 10.2967/jnumed.121.263501
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Keywords

  • Image Processing
  • Oncology: Lymphoma
  • PET/CT
  • 18F FDG PET/CT
  • Artificial intelligence
  • DLBCL
  • Dissemination
  • metabolic tumor volume
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