Imaging Evaluation of Living Liver Donor Candidates: Techniques, Protocols, and Anatomy
Bibliographic record
Abstract
The need for liver transplants is increasing because the prevalence of liver diseases and the indications for transplants are growing. In response to the shortage of grafts from deceased donors, more transplants are being performed worldwide with grafts from living donors. Radiologic evaluation is an integral component in the assessment of donor candidates to ensure their eligibility and to choose the most appropriate surgical approach. MRI is the preferred modality for evaluation of the liver parenchyma and biliary tree. In most centers, a combination of MRI and CT is used to take advantage of the higher spatial resolution of CT for evaluation of arteries. However, MRI-only assessment is feasible. In addition to assessment of the liver parenchyma for abnormalities such as steatosis, a detailed evaluation of the hepatic vascular and biliary system for pertinent anatomic variants is crucial, because these variants can affect surgical techniques and outcomes in both recipients and donors. In this pictorial article, after a brief review of the most common surgical techniques and postsurgical liver anatomy, the biliary and vascular anatomy are discussed, with specific attention paid to the variants that are pertinent to this surgical procedure. The roles of liver segmentation and volumetric assessment and current imaging techniques and protocols are also discussed. Online supplemental material is available for this article. ©RSNA, 2021 This article was updated with funder information on September 7, 2022.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.004 |
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".