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Japanese Journal of Radiology
Published in: Japanese Journal of Radiology 1/2024

Open Access 01-08-2023 | Positron Emission Tomography | Invited Review

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Authors: Masatoyo Nakajo, Megumi Jinguji, Soichiro Ito, Atushi Tani, Mitsuho Hirahara, Takashi Yoshiura

Published in: Japanese Journal of Radiology | Issue 1/2024

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Abstract

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.
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Metadata
Title
Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology
Authors
Masatoyo Nakajo
Megumi Jinguji
Soichiro Ito
Atushi Tani
Mitsuho Hirahara
Takashi Yoshiura
Publication date
01-08-2023
Publisher
Springer Nature Singapore
Published in
Japanese Journal of Radiology / Issue 1/2024
Print ISSN: 1867-1071
Electronic ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-023-01476-1

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