TY  - JOUR
AU  - Wennmann, Markus
AU  - Ming, Wenlong
AU  - Bauer, Fabian
AU  - Chmelik, Jiri
AU  - Klein, André
AU  - Uhlenbrock, Charlotte
AU  - Grözinger, Martin
AU  - Kahl, Kim-Celine
AU  - Nonnenmacher, Tobias
AU  - Debic, Manuel
AU  - Hielscher, Thomas
AU  - Thierjung, Heidi
AU  - Rotkopf, Lukas T
AU  - Stanczyk, Nikolas
AU  - Sauer, Sandra
AU  - Jauch, Anna
AU  - Götz, Michael
AU  - Kurz, Felix T
AU  - Schlamp, Kai
AU  - Horger, Marius
AU  - Afat, Saif
AU  - Besemer, Britta
AU  - Hoffmann, Martin
AU  - Hoffend, Johannes
AU  - Kraemer, Doris
AU  - Graeven, Ullrich
AU  - Ringelstein, Adrian
AU  - Bonekamp, David
AU  - Kleesiek, Jens
AU  - Floca, Ralf O
AU  - Hillengass, Jens
AU  - Mai, Elias K
AU  - Weinhold, Niels
AU  - Weber, Tim F
AU  - Goldschmidt, Hartmut
AU  - Schlemmer, Heinz-Peter
AU  - Maier-Hein, Klaus
AU  - Delorme, Stefan
AU  - Neher, Peter
TI  - Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.
JO  - Investigative radiology
VL  - 58
IS  - 10
SN  - 0020-9996
CY  - [Erscheinungsort nicht ermittelbar]
PB  - Ovid
M1  - DKFZ-2023-01041
SP  - 754-765
PY  - 2023
N1  - #EA:E010#LA:E230#EA:E010#LA:E230# / 2023 Oct 1;58(10):754-765
AB  - In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI).This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively.A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated (P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets.The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.
LB  - PUB:(DE-HGF)16
C6  - pmid:37222527
DO  - DOI:10.1097/RLI.0000000000000986
UR  - https://inrepo02.dkfz.de/record/276232
ER  -