Pathophysiological and anatomical basis of lymphatic transit of cancer cells and role of the lymphatic system: a review of published literature
Bibliographic record
Abstract
Cancer metastasis involves dissemination of malignant cells from the primary tumor, via local lymphatic vessels to gain access to systemic circulation, while evading the destruction by immune cells, followed by successful extravasation and initiation of malignant growth in the distant organ. Despite the obvious contribution of the lymphatic system to the metastatic spread of cancer, basic research regarding the mechanisms leading to tumor dissemination via lymphatic vessels has been limited for years. Recent identification of molecular growth factors of lymphangiogenesis have led to new advances in our understanding of the underlying mechanisms of tumor metastasis. These growth factors have enabled scientists to better identify peritumoral lymphatics and to visualize precisely the ingrowth of tumor cells into the lumen of lymphatic capillaries. Moreover, it has been shown that these molecular markers secreted from a primary tumor can induce lymphangiogenesis in close regional lymph nodes, even prior to tumor cells arrival, which may facilitate metastasis spread. Comprehensive research of the multiplex interactions between tumor cells, lymphatics and the immune system will be crucial to further enhance the development of therapeutic and prognostic approaches to cancer. This review presents the ontogeny and anatomy of the lymphatic vasculature, discuss the immunological, molecular and physiological control of lymphatic vessel function, and explore the contribution of the lymphatic system to the development of metastases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".