Effect of material property heterogeneity on biomechanical modeling of prostate under deformation
Bibliographic record
Abstract
Biomechanical model based deformable image registration has been widely used to account for prostate deformation in various medical imaging procedures. Biomechanical material properties are important components of a biomechanical model. In this study, the effect of incorporating tumor-specific material properties in the prostate biomechanical model was investigated to provide insight into the potential impact of material heterogeneity on the prostate deformation calculations. First, a simple spherical prostate and tumor model was used to analytically describe the deformations and demonstrate the fundamental effect of changes in the tumor volume and stiffness in the modeled deformation. Next, using a clinical prostate model, a parametric approach was used to describe the variations in the heterogeneous prostate model by changing tumor volume, stiffness, and location, to show the differences in the modeled deformation between heterogeneous and homogeneous prostate models. Finally, five clinical prostatectomy examples were used in separately performed homogeneous and heterogeneous biomechanical model based registrations to describe the deformations between 3D reconstructed histopathology images and ex vivo magnetic resonance imaging, and examine the potential clinical impact of modeling biomechanical heterogeneity of the prostate. The analytical formulation showed that increasing the tumor volume and stiffness could significantly increase the impact of the heterogeneous prostate model in the calculated displacement differences compared to the homogeneous model. The parametric approach using a single prostate model indicated up to 4.8 mm of displacement difference at the tumor boundary compared to a homogeneous model. Such differences in the deformation of the prostate could be potentially clinically significant given the voxel size of the ex vivo MR images (0.3 × 0.3 × 0.3 mm). However, no significant changes in the registration accuracy were observed using heterogeneous models for the limited number of clinical prostatectomy patients modeled and evaluated in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".