Mechanical Forces in Tumor Growth and Treatment: Perspectives From Biology, Physics, Engineering, and Mathematical Modeling
Bibliographic record
Abstract
The progression of tumors is influenced by mechanical forces and biological elements, such as hypoxia and angiogenesis. Mechanical factors, including stress, pressure, interstitial fluid pressure, and cellular traction forces, compromise normal tissue architecture, augmenting stiffness and thus promoting tumor growth and invasion. The selective elimination of specific tumor components can reduce growth-induced mechanical stress, thereby improving therapeutic efficacy. Furthermore, stress-relief drugs have the potential in enhancing chemotherapy outcomes. In this setting, computational modeling functions as an essential tool for quantitatively elucidating the mechanical principles underlying tumor formation. These models can precisely replicate the impact of mechanical pressures on solid tumors, offering insight into the regulation of tumor behavior by these forces. Tumor growth produces mechanical forces, including compression, displacement, and deformation, leading to irregular stress patterns, expedited tumor advancement, and reduced treatment efficacy. This review analyzes the impact of mechanical forces on carcinogenesis and solid tumor proliferation, emphasizing the significance of stress alleviation in regulating tumor growth. Furthermore, we investigate the influence of mechanical forces on tumor dissemination and emphasize the promise of integrating computational modeling with force-targeted cancer therapies to improve treatment efficacy by tackling the fundamental mechanics of tumor proliferation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".