A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F22%3A10457475" target="_blank" >RIV/00064165:_____/22:10457475 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/22:10457475
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VWbC2zwy6F" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VWbC2zwy6F</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/jha2.421" target="_blank" >10.1002/jha2.421</a>
Alternative languages
Result language
angličtina
Original language name
A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA
Original language description
Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.
Czech name
—
Czech description
—
Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
—
OECD FORD branch
30205 - Hematology
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
EJHaem [online]
ISSN
2688-6146
e-ISSN
2688-6146
Volume of the periodical
3
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
406-414
UT code for WoS article
—
EID of the result in the Scopus database
—