MR Lymphangiography in Children: Technique and Potential Applications
Bibliographic record
Abstract
The lymphatic system, an important component of the circulatory system with essential physiologic functions, can be affected by various disease processes. There has been a delay in the development of effective imaging methods for the lymphatic system due to its small size, which limits visualization as well as introduction of contrast material. Traditionally, the lymphatic system has been imaged by injecting contrast material or radiotracers into the feet or hands. This is not sufficient for assessment of the central conducting lymphatics (CCLs) (such as the thoracic duct or the cisterna chyli). Fluoroscopic intranodal lymphangiography with injection of oil-based contrast material into groin lymph nodes improves visualization of CCLs but is limited in practice owing to the use of radiation and the potential risk for paradoxical embolization in children with left-to-right shunt. Dynamic contrast material–enhanced (DCE) magnetic resonance (MR) lymphangiography, which is performed by injecting gadolinium-based contrast material into groin lymph nodes, overcomes these limitations. T2-weighted imaging plays a complementary role to DCE MR lymphangiography in the assessment of CCLs. DCE MR lymphangiography demonstrates preserved integrity or any abnormality of the CCLs (including blockage or leak). The technique has recently been used in evaluating pulmonary lymphatic perfusion syndrome in children with plastic bronchitis, neonatal lymphatic flow disorders, and nontraumatic chylothorax. It is useful in identification of the source of chylous ascites and contributes to understanding of the anatomy of lymphatic malformations. It is successfully used for planning of embolization of aberrant lymphatic channels in a variety of lymphatic flow disorders. This review discusses the anatomy and function of the lymphatic system, the evolution of imaging of the lymphatic system, and DCE MR lymphangiography technique and its applications in children. ©RSNA, 2017
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".