Abstract #3799

Advancing prediction of bone marrow biopsy results from MRI in myeloma patients: A Neural Network Approach

Jessica Kächele^1,2, Markus Wennmann³, Maximilian Fischer^1,2,4, Robin Peretzke^1,4, Tassilo Wald^1,5, Juliane K. Bernhard^1,6, Fabian Bauer^3,7, Sandra Sauer⁸, Jens Hillengass⁹, Elias K. Mai⁸, Niels Weinhold⁸, Hartmut Goldschmidt^10,11, Marc-Steffen Raab⁸, Heinz-Peter Schlemmer¹¹, Stefan Delorme³, Klaus Maier-Hein^1,11,12, and Peter Neher^1,12,13

¹German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany, ²German Cancer Consortium (DKTK), DKFZ, core center, Heidelberg, Germany, ³German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany, ⁴Medical Faculty, Heidelberg University, Heidelberg, Germany, ⁵Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁶Medical Faculty, University of Regensburg, Regensburg, Germany, ⁷Medical Faculty, University of Heidelberg, Heidelberg, Germany, ⁸Heidelberg Myeloma Center, Department of Medicine V, University Hospital Heidelberg, Heidelberg, Germany, ⁹Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States, ¹⁰Department of Medicine V, GMMG-Studygroup, University Hospital Heidelberg, Heidelberg, Germany, ¹¹National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany, ¹²Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany, ¹³German Cancer Consortium (DKTK), DKFZ, core center, Heidelberg, Germany

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Regression, CNN, Radiomics

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.

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