Author:
Wennmann Markus,Ming Wenlong,Bauer Fabian,Chmelik Jiri,Klein André,Uhlenbrock Charlotte,Grözinger Martin,Kahl Kim-Celine,Nonnenmacher Tobias,Debic Manuel,Hielscher Thomas,Thierjung Heidi,Rotkopf Lukas T.,Stanczyk Nikolas,Sauer Sandra,Jauch Anna,Götz Michael,Kurz Felix T.,Schlamp Kai,Horger Marius,Afat Saif,Besemer Britta,Hoffmann Martin,Hoffend Johannes,Kraemer Doris,Graeven Ullrich,Ringelstein Adrian,Bonekamp David,Kleesiek Jens,Floca Ralf O.,Hillengass Jens,Mai Elias K.,Weinhold Niels,Weber Tim F.,Goldschmidt Hartmut,Schlemmer Heinz-Peter,Maier-Hein Klaus,Delorme Stefan,Neher Peter
Abstract
Objectives
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).
Materials and Methods
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.
Results
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.
Conclusions
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.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Radiology, Nuclear Medicine and imaging,General Medicine