Synthetic PET from CT improves diagnosis and prognosis for lung cancer : Proof of concept
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved..
[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
Cell reports. Medicine - 5(2024), 3 vom: 19. März, Seite 101463 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Salehjahromi, Morteza [VerfasserIn] |
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| Links: |
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| Themen: |
0Z5B2CJX4D |
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| Anmerkungen: |
Date Completed 22.03.2024 Date Revised 03.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.xcrm.2024.101463 |
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| Förderinstitution / Projekttitel: |
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| 100 | 1 | |a Salehjahromi, Morteza |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Synthetic PET from CT improves diagnosis and prognosis for lung cancer |b Proof of concept |
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| 500 | |a Date Revised 03.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a [18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT | ||
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| 650 | 4 | |a prognostic marker | |
| 650 | 4 | |a radiogenomics | |
| 650 | 4 | |a radiomics | |
| 650 | 4 | |a screening | |
| 650 | 4 | |a synthetic PET | |
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| 700 | 1 | |a Karpinets, Tatiana V |e verfasserin |4 aut | |
| 700 | 1 | |a Sujit, Sheeba J |e verfasserin |4 aut | |
| 700 | 1 | |a Qayati, Mohamed |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Pingjun |e verfasserin |4 aut | |
| 700 | 1 | |a Aminu, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Saad, Maliazurina B |e verfasserin |4 aut | |
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