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Evaluation of a convolution neural network for baseline total tumor metabolic volume on [18F]FDG PET in diffuse large B cell lymphoma

  • Nuclear Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

New PET data-processing tools allow for automatic lesion selection and segmentation by a convolution neural network using artificial intelligence (AI) to obtain total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) routinely at the clinical workstation. Our objective was to evaluate an AI implemented in a new version of commercial software to verify reproducibility of results and time savings in a daily workflow.

Methods

Using the software to obtain TMTV and TLG, two nuclear physicians applied five methods to retrospectively analyze data for 51 patients. Methods 1 and 2 were fully automated with exclusion of lesions ≤ 0.5 mL and ≤ 0.1 mL, respectively. Methods 3 and 4 were fully automated with physician review. Method 5 was semi-automated and used as reference. Time and number of clicks to complete the measurement were recorded for each method. Inter-instrument and inter-observer variation was assessed by the intra-class coefficient (ICC) and Bland-Altman plots.

Results

Between methods 3 and 5, for the main user, the ICC was 0.99 for TMTV and 1.0 for TLG. Between the two users applying method 3, ICC was 0.97 for TMTV and 0.99 for TLG. Mean processing time (± standard deviation) was 20 s ± 9.0 for method 1, 178 s ± 125.7 for method 3, and 326 s ± 188.6 for method 5 (p < 0.05).

Conclusion

AI-enabled lesion detection software offers an automated, fast, reliable, and consistently performing tool for obtaining TMTV and TLG in a daily workflow.

Key Points

• Our study shows that artificial intelligence lesion detection software is an automated, fast, reliable, and consistently performing tool for obtaining total metabolic tumor volume and total lesion glycolysis in a daily workflow.

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Abbreviations

AI:

Artificial intelligence

CIER :

Internal Ethics and Research Committee

CNN:

Convolution neural network

DICOM:

Digital Imaging and COmmunications in Medicine

DLBCL :

Diffuse large cell lymphoma

FDG :

Fluorodeoxyglucose

GHICL:

Groupement des Hôpitaux de l'Institut Catholique de Lille

ICC :

Intra-class correlation coefficient

ICL:

Institut Catholique de Lille

IPI:

International Prognostic Index

MIP:

Maximum intensity projection

NCCN-IPI:

National Comprehensive Cancer Network- International Prognostic Index

NHL:

Non-Hodgkin’s lymphoma

PERCIST :

PET response criteria in solid tumors

PET:

Positron emission tomography

PFS:

Progression-free survival

ROI:

Region of interest

SD:

Standard deviation

SUV:

Standardized uptake value

TLG:

Total lesion glycolysis

TMTV:

Total metabolic tumor volume

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Nuclear Medicine Department, CHU Lille University Hospital, Lille, France

    Mourtaza Karimdjee, Gauthier Delaby, Damien Huglo, Clio Baillet & Simon Dujardin

  2. Hematology Department, Group of Hospitals of the Catholic Institute of Lille, Lille, France

    Alexandre Willaume

  3. Nuclear Medicine Department, Group of Hospitals of the Catholic Institute of Lille, Lille, France

    Alban Bailliez

Authors
  1. Mourtaza Karimdjee
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  2. Gauthier Delaby
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Correspondence to Mourtaza Karimdjee.

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The scientific guarantor of this publication is Mourtaza Karimdjee.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise. No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all patients in this study. Written informed consent was waived by the Institutional Review Board.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Karimdjee, M., Delaby, G., Huglo, D. et al. Evaluation of a convolution neural network for baseline total tumor metabolic volume on [18F]FDG PET in diffuse large B cell lymphoma. Eur Radiol 33, 3386–3395 (2023). https://doi.org/10.1007/s00330-022-09375-1

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