Artificial intelligence for multimodal data integration in oncology.

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.

An experimental and computational investigation of the effects of volumetric boundary conditions on the compressive mechanics of passive skeletal muscle.

Computational modeling, such as finite element analysis, is employed in a range of biomechanics specialties, including impact biomechanics and surgical planning. These models rely on accurate material properties for skeletal muscle, which comprises roughly 40% of the human body. Due to surrounding tissues, compressed skeletal muscle in vivo likely experiences a semi-confined state. Nearly all previous studies investigating passively compressed muscle at the tissue level have focused on muscle in unconfined compression. The goals of this study were to (1) examine the stiffness and time-dependent material properties of skeletal muscle subjected to both confined and unconfined compression (2) develop a model that captures passive muscle mechanics under both conditions and (3) determine the extent to which different assumptions of volumetric behavior affect model results. Muscle in confined compression exhibited stiffer behavior, agreeing with previous assumptions of near-incompressibility. Stress relaxation was found to be faster under unconfined compression, suggesting there may be different mechanisms that support load these two conditions. Finite element calibration was achieved through nonlinear optimization (normalized root mean square error <6%) and model validation was strong (normalized root mean square error <17%). Comparisons to commonly employed assumptions of bulk behavior showed that a simple one parameter approach does not accurately simulate confined compression. We thus recommend the use of a properly calibrated, nonlinear bulk constitutive model for modeling of skeletal muscle in vivo. Future work to determine mechanisms of passive muscle stiffness would enhance the efforts presented here.

Anisotropic and viscoelastic tensile mechanical properties of aponeurosis: Experimentation, modeling, and tissue microstructure.

Aponeuroses are stiff sheath-like components of the muscle-tendon unit that play a vital role in force transmission and thus locomotion. There is clear importance of the aponeurosis in musculoskeletal function, but there have been relatively few studies of aponeurosis material properties to date. The goals of this work were to: 1) perform tensile stress-relaxation tests, 2) perform planar biaxial tests, 3) employ computational modeling to the data from 1 to 2, and 4) perform scanning electron microscopy to determine collagen fibril organization for aponeurosis tissue. Viscoelastic modeling and statistical analysis of stress-relaxation data showed that while relaxation rate differed statistically between strain levels (p = 0.044), functionally the relaxation behavior was nearly the same. Biaxial testing and associated modeling highlighted the nonlinear (toe region of ~2-3% strain) and anisotropic (longitudinal direction linear modulus ~50 MPa, transverse ~2.5 MPa) tensile mechanical behavior of aponeurosis tissue. Comparisons of various constitutive formulations showed that a transversely isotropic Ogden approach balanced strong fitting (goodness of fit 0.984) with a limited number of parameters (five), while damage modeling parameters were also provided. Scanning electron microscopy showed a composite structure of highly aligned, partially wavy collagen fibrils with more random collagen cables for aponeurosis microstructure. Future work to expand microstructural analysis and use these data to inform computational modeling would benefit this work and the field.