Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
Artikel i vetenskaplig tidskrift, 2024

Background: The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites.
Methods: In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1–3 vs 4–5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated.
Findings: In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942–0·956), accuracy of 0·890 (0·879–0·901), sensitivity of 0·868 (0·851–0·885), and specificity of 0·913 (0·899–0·925); LARS-max achieved an AUC of 0·949 (0·942–0·956), accuracy of 0·868 (0·858–0·879), sensitivity of 0·909 (0·896–0·924), and specificity of 0·826 (0·808–0·843); and LARS-ptct achieved an AUC of 0·939 (0·930–0·948), accuracy of 0·875 (0·864–0·887), sensitivity of 0·836 (0·817–0·855), and specificity of 0·915 (0·901–0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938–0·966), accuracy of 0·907 (0·888–0·925), sensitivity of 0·874 (0·843–0·904), and specificity of 0·949 (0·921–0·960); LARS-max achieved an AUC of 0·952 (0·937–0·965), accuracy of 0·898 (0·878–0·916), sensitivity of 0·899 (0·871–0·926), and specificity of 0·897 (0·871–0·922); and LARS-ptct achieved an AUC of 0·932 (0·915–0·948), accuracy of 0·870 (0·850–0·891), sensitivity of 0·827 (0·793–0·863), and specificity of 0·913 (0·889–0·937).
Interpretation: Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. Funding: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.

Författare

Ida Häggström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Memorial Sloan-Kettering Cancer Center

Doris Leithner

Memorial Sloan-Kettering Cancer Center

New York University

Jennifer Alvén

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Gabriele Campanella

Icahn School of Medicine at Mount Sinai

Murad Abusamra

Memorial Sloan-Kettering Cancer Center

Honglei Zhang

Memorial Sloan-Kettering Cancer Center

Shalini Chhabra

Memorial Sloan-Kettering Cancer Center

Lucian Beer

Medizinische Universität Wien

Alexander Haug

Medizinische Universität Wien

Gilles Salles

Memorial Sloan-Kettering Cancer Center

Weill Cornell Medical College

Markus Raderer

Medizinische Universität Wien

Philipp B. Staber

Memorial Sloan-Kettering Cancer Center

Anton Becker

New York University

Weill Cornell Medical College

Memorial Sloan-Kettering Cancer Center

Hedvig Hricak

Weill Cornell Medical College

Memorial Sloan-Kettering Cancer Center

Thomas J. Fuchs

Icahn School of Medicine at Mount Sinai

Heiko Schöder

Memorial Sloan-Kettering Cancer Center

Weill Cornell Medical College

Marius E. Mayerhoefer

New York University

Medizinische Universität Wien

Memorial Sloan-Kettering Cancer Center

Weill Cornell Medical College

The Lancet Digital Health

25897500 (eISSN)

Vol. 6 2 e114-e125

Ämneskategorier

Radiologi och bildbehandling

Cancer och onkologi

DOI

10.1016/S2589-7500(23)00203-0

PubMed

38135556

Mer information

Senast uppdaterat

2024-03-07