Advancing prediction of bone marrow biopsy results from MRI in myeloma patients: A Neural Network Approach
Jessica Kächele, Markus Wennmann, Maximilian Fischer, Robin Peretzke, Tassilo Wald, Juliane Bernhard, Fabian Bauer, Sandra Sauer, Jens Hillengaß, Elias Karl Mai, Niels Weinhold, Hartmut Goldschmidt, Marc‐Steffen Raab, Heinz-Peter Schlemmer, Stefan Delorme, Klaus Hermann Maier-Hein, Peter Neher · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Motivation: While Radiomics analysis has shown predictive power for plasma cell infiltration (PCI) from MRI in Myeloma patients, convolutional neural networks (CNNs) offer an opportunity for improved performance and generalizability. Goal(s): Our objective was to develop a predictive model for PCI using CNNs while addressing the challenges posed by limited dataset size. Approach: CNNs were trained on MRI data of the pelvic bone marrow and its predictive capabilities were enriched by concatenating radiomic features in the latent space. Results: The findings revealed limitations due to the small dataset size. However, incorporating radiomic features enhanced prediction accuracy, aligning with radiomics and random forest-based methods. Impact: This study highlights the limitations of deep learning when using a small dataset. It underlines the importance of feature extraction and the need of dedicating substantial efforts to create large annotated datasets.