• Je něco špatně v tomto záznamu ?

A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA

L. Kostakoglu, F. Dalmasso, P. Berchialla, LA. Pierce, U. Vitolo, M. Martelli, LH. Sehn, M. Trněný, TG. Nielsen, CR. Bolen, D. Sahin, C. Lee, TC. El-Galaly, F. Mattiello, PE. Kinahan, S. Chauvie

. 2022 ; 3 (2) : 406-414. [pub] 20220324

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc22023899

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc22023899
003      
CZ-PrNML
005      
20221031095324.0
007      
ta
008      
221010s2022 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1002/jha2.421 $2 doi
035    __
$a (PubMed)35846039
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Kostakoglu, Lale $u Department of Radiology and Medical Imaging University of Virginia Charlottesville Virginia USA $1 https://orcid.org/000000022419348X
245    12
$a A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA / $c L. Kostakoglu, F. Dalmasso, P. Berchialla, LA. Pierce, U. Vitolo, M. Martelli, LH. Sehn, M. Trněný, TG. Nielsen, CR. Bolen, D. Sahin, C. Lee, TC. El-Galaly, F. Mattiello, PE. Kinahan, S. Chauvie
520    9_
$a 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.
655    _2
$a časopisecké články $7 D016428
700    1_
$a Dalmasso, Federico $u Medical Physics Division Santa Croce e Carle Hospital Cuneo Italy
700    1_
$a Berchialla, Paola $u Department of Clinical and Biological Sciences University of Turin Turin Italy
700    1_
$a Pierce, Larry A $u Department of Radiology University of Washington Seattle Washington USA
700    1_
$a Vitolo, Umberto $u Multidisciplinary Oncology Outpatient Clinic Candiolo Cancer Institute Candiolo Italy
700    1_
$a Martelli, Maurizio $u Hematology Department of Translational and Precision Medicine Sapienza University Rome Italy
700    1_
$a Sehn, Laurie H $u BC Cancer Center for Lymphoid Cancer and the University of British Columbia Vancouver British Columbia Canada
700    1_
$a Trněný, Marek $u 1st Faculty of Medicine Charles University General Hospital Prague Czech Republic
700    1_
$a Nielsen, Tina G $u F. Hoffmann-La Roche Ltd Basel Switzerland
700    1_
$a Bolen, Christopher R $u Genentech, Inc. South San Francisco California USA
700    1_
$a Sahin, Deniz $u F. Hoffmann-La Roche Ltd Basel Switzerland
700    1_
$a Lee, Calvin $u Genentech, Inc. South San Francisco California USA
700    1_
$a El-Galaly, Tarec Christoffer $u F. Hoffmann-La Roche Ltd Basel Switzerland $u Department of Hematology Aalborg University Hospital Aalborg Denmark $1 https://orcid.org/000000024406380X
700    1_
$a Mattiello, Federico $u F. Hoffmann-La Roche Ltd Basel Switzerland
700    1_
$a Kinahan, Paul E $u Department of Radiology University of Washington Seattle Washington USA
700    1_
$a Chauvie, Stephane $u Department of Clinical and Biological Sciences University of Turin Turin Italy
773    0_
$w MED00209731 $t EJHaem $x 2688-6146 $g Roč. 3, č. 2 (2022), s. 406-414
856    41
$u https://pubmed.ncbi.nlm.nih.gov/35846039 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y - $z 0
990    __
$a 20221010 $b ABA008
991    __
$a 20221031095322 $b ABA008
999    __
$a ind $b bmc $g 1854083 $s 1175187
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2022 $b 3 $c 2 $d 406-414 $e 20220324 $i 2688-6146 $m EJHaem $n EJHaem $x MED00209731
LZP    __
$a Pubmed-20221010

Najít záznam

Citační ukazatele

Možnosti archivace