Adaptive region-oriented masked vision retentive network for predicting macrovascular invasion in hepatocellular carcinoma
Bibliographic record
Abstract
The aim of the present study was to develop the Adaptive Region-Oriented Masked Vision Retentive Network (AROMA ViR) model, which can efficiently learn the morphological structures of the liver, to predict macrovascular invasion (MI) in hepatocellular carcinoma (HCC). Retrospective CT images were obtained from the University of Texas MD Anderson Cancer Center, in accordance with The Cancer Imaging Archive data usage policy and restrictions. The image dataset comprised 51,968 slices taken during the arterial phase in 105 patients with HCC. We split the data patient-wise into training, validation, and test datasets in a 6:2:2 ratio after applying specific exclusion criteria. The AROMA ViR was designed to enhance the relevant areas in the retention map by incorporating spatial information for liver parenchyma, tumor, and portal vein. The model applied causal masks specialized for specific liver shapes for each slice image into retention encoders. We compared the proposed model with Residual Network 101, Vision Transformer, and Vision Retentive Network. We calculated the area under the receiver-operating characteristic curve (AUC-ROC) and that under the precision recall curve (AUC-PR). We also obtained accuracy, sensitivity, specificity, and F1-score using Youden’s index. AROMA ViR pretrained by ImageNet showed AUC-ROC of 0.860, AUC-PR of 0.790, accuracy of 0.853, sensitivity of 0.719, specificity of 0.902, and F1 score of 0.578.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".