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Häggström I, Leithner D, Alvén J, Campanella G, Abusamra M, Zhang H, Chhabra S, Beer L, Haug A, Salles G, Raderer M, Staber PB, Becker A, Hricak H, Fuchs TJ, Schöder H, Mayerhoefer ME. Deep learning for [ 18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. Lancet Digit Health 2024; 6:e114-e125. [PMID: 38135556 DOI: 10.1016/s2589-7500(23)00203-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 12/24/2023]
Abstract
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.
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Affiliation(s)
- Ida Häggström
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Doris Leithner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, NYU Langone Health, Grossman School of Medicine, New York, NY, USA
| | - Jennifer Alvén
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Gabriele Campanella
- Hasso Plattner Institute for Digital Health, Mount Sinai Medical School, New York, NY, USA; Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Murad Abusamra
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Honglei Zhang
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shalini Chhabra
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucian Beer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexander Haug
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilles Salles
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Markus Raderer
- Department of Medicine I, Medical University of Vienna, Vienna, Austria
| | - Philipp B Staber
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anton Becker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA; Department of Radiology, NYU Langone Health, Grossman School of Medicine, New York, NY, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Thomas J Fuchs
- Hasso Plattner Institute for Digital Health, Mount Sinai Medical School, New York, NY, USA; Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Marius E Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Weill Cornell Medical College, Cornell University, New York, NY, USA; Department of Radiology, NYU Langone Health, Grossman School of Medicine, New York, NY, USA.
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