Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma.
Semin Nucl Med
; 53(3): 426-448, 2023 05.
Article
en En
| MEDLINE
| ID: mdl-36870800
ABSTRACT
Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Tomografía Computarizada por Tomografía de Emisión de Positrones
/
Linfoma
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Semin Nucl Med
Año:
2023
Tipo del documento:
Article