<sup>99m</sup>Tc-Mebrofenin Hepatobiliary Scintigraphy with SPECT for the Assessment of Hepatic Function and Liver Functional Volume Before Partial Hepatectomy
Bibliographic record
Abstract
UNLABELLED: Preoperative evaluation of future remnant liver (FRL) function is crucial in the determination of whether a patient can safely undergo liver resection. Although dynamic (99m)Tc-mebrofenin hepatobiliary scintigraphy (HBS) is used to measure FRL function, 2-dimensional planar images lack the ability to assess segmental liver function. Modern SPECT/CT cameras combine dynamic (99m)Tc-mebrofenin HBS with additional SPECT and the anatomic information of the CT scan. The aim of this study was to evaluate the additional value of (99m)Tc-mebrofenin SPECT for the measurement of segmental liver function and liver functional volume. METHODS: Preoperative CT volumetry and (99m)Tc-mebrofenin HBS with SPECT were performed in 36 patients undergoing liver resection. In 18 patients, postoperative (99m)Tc-mebrofenin HBS with SPECT was performed within 3 d after operation. Dual-head dynamic acquisitions were used to calculate FRL function using anterior and geometric mean (Gmean) datasets. Total and FRL functional liver volumes were measured by SPECT. RESULTS: Because of the anatomic position of the liver, the anterior projection resulted in an underestimation of FRL function in patients undergoing left hemihepatectomy. In patients with normal liver parenchyma, total functional liver volume was comparable to total liver volume measured by CT volumetry, indicating that (99m)Tc-mebrofenin SPECT is an accurate method to measure hepatic volume. In compromised livers, compared with normal livers, FRL function per cubic centimeter of liver volume was significantly less. In addition, liver function was not distributed homogeneously, with the segments to be resected relatively more affected. FRL function, measured by a combination of SPECT and dynamic HBS, was able to accurately predict actual postoperative remnant liver function. CONCLUSION: The Gmean dataset is recommended for the assessment of hepatic function by dynamic planar (99m)Tc-mebrofenin HBS. The combination of SPECT data with the dynamic uptake function measured by planar HBS provides valuable visible and quantitative information regarding segmental liver function and is an accurate measure for FRL function.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".