A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma.
Sci Rep
; 9(1): 1322, 2019 02 04.
Article
en En
| MEDLINE
| ID: mdl-30718585
ABSTRACT
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUVmax). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Enfermedad de Hodgkin
/
Carga Tumoral
/
Tomografía Computarizada por Tomografía de Emisión de Positrones
/
Neoplasias del Mediastino
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Adolescent
/
Adult
/
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Sci Rep
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos