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. 2022 Jan;17(1):145-174.
doi: 10.1016/j.cpet.2021.09.006.

Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions)

Navid Hasani  1 , Sriram S Paravastu  2 , Faraz Farhadi  2 , Fereshteh Yousefirizi  3 , Michael A Morris  4 , Arman Rahmim  5 , Mark Roschewski  6 , Ronald M Summers  7 , Babak Saboury  8
Affiliations

Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions)

Navid Hasani et al. PET Clin. 2022 Jan.

Abstract

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

Keywords: Artificial intelligence; Deep learning; Detection; Lymphoma; Positron emission tomography (PET); Radiomics; Radiophenomics; Segmentation.

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Conflict of interest statement

Disclosure This research was supported by the Intramural Research Program of the NIH, Clinical Center and NIDCR. The opinions expressed in this publication are the author's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.

Figures

Fig. 1.
Fig. 1.
Examples of different sizes and distributions of tumor in 5 patients with diffuse large B-cell lymphoma. (From Barrington SF, Meignan M. Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden. J Nucl Med. 2019;60(8):1096–1102: under Open Access Creative Commons License https://creativecommons.org/licenses/by/4.0/) 1. Download : Download high-res image (173KB) 2. Download : Download full-size image
Fig. 2.
Fig. 2.
Demonstrates the summary of the literature search strategies and the results at each stage. 1. Download : Download high-res image (331KB) 2. Download : Download full-size image
Fig. 3.
Fig. 3.
Maximum intensity projection 18F-FDG PET/CT images were processed in 2 patients using the constructed CNN. The test data consists of patients with both lung cancer and lymphoma; the detected lesions are color coded accordingly. IASLC is the abbreviation for the International Association for the Study of Lung Cancer. (From Sibille L, Seifert R, Avramovic N, et al. 18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks. Radiology. 2020;294(2):445–452; with permission.) 1. Download : Download high-res image (584KB) 2. Download : Download full-size image
Fig. 4.
Fig. 4.
Schematic of a proposed cascaded model for PET tumor segmentation; Module 1, classifies the axial slices to suspicious and non-suspicious ones; Module 2, detects the lymphoma lesions in axial slices that are a candidate by Module 1. In Module 3, the 3D PET image and detection results are given to the tumor segmentation algorithm to segment the lesions inside the bounding boxes provided by Module 2. 1. Download : Download high-res image (278KB) 2. Download : Download full-size image
Fig. 5.
Fig. 5.
CT and coronal PET multicenter images are input to 3 segment layers, there are then 8 convolution layers and two fully connected layers that subsequently generate a probability map for lymphoma lesions as shown in purple. (From Weisman AJ, Kim J, Lee I, et al. Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients. EJNMMI Phys. 2020;7(1):76: under Open Access Creative Commons License http://creativecommons.org/licenses/by/4.0/.) 1. Download : Download high-res image (533KB) 2. Download : Download full-size image
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References

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