Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures
Bibliographic record
Abstract
A computational model is proposed to demonstrate the feasibility of characterizing the motion of lung tumors caused by respiratory diaphragm forces using a tissue biomechanics approach. Compensating for such motion is very important for developing effective systems of minimally invasive tumor ablative procedures, e.g., Low Dose Rate (LDR) lung brachytherapy. To minimize the effects of respiratory motion, the target lung is almost completely deflated before starting such procedures. However, a significant amount of motion persists in the target lung due to the diaphragm contact forces required for the other lung's respiration. In this study, a model pipeline was developed which inputs a pre-operative 4D-CT image sequence of the lung to output the predicted 3D motion trajectory of the tumor over the respiratory cycle. A finite element method was used in this pipeline to model the lung tissue deformation in order to predict the tumor motion. Experiments were conducted on an ex vivo porcine lung to demonstrate the performance and assess the accuracy of the proposed pipeline. The resultant tumor motion trajectory obtained from the biomechanical model of the lung was compared to the experimental trajectory obtained from CT imaging. Results were promising, suggesting that tissue mechanics-based modeling can be employed for effective characterization of lung tumor respiratory motion to improve accuracy in lung tumor ablative procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".