Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data
Bibliographic record
Abstract
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent advances in machine learning (ML), we aimed to develop a noninvasive tool using demographic, clinical, laboratory, and B-mode ultrasound (US) features to predict significant fibrosis (METAVIR≥F2). METHODS: We used a nested 10-fold cross-validation approach with grid-search for hyperparameter fine-tuning to train an artificial neural network (ANN) and a support vector machine (SVM) to classify mild fibrosis (F0-F1) and significant fibrosis (F2-F4) on 1131 patients. We calculated Shapley values to identify top-ranked features, determining the contribution of each feature to model predictions. For the imaging-based model, we used 4819 images with 892 studies trained on the residual network 18 (ResNet18) model to classify F0-F1 versus F3-F4. RESULTS: We determined the ANN performed the best when compared to the SVM and standard biomarkers, with an AUC ranging from 0.77 to 0.81. The ResNet18 model was unable to diagnose advanced GF, leading to the training AUCs ranging from 0.89 to 0.97, while the validation and testing AUCs were 0.43-0.63. Shapley analysis highlighted the following top-ranked features associated with significant GF: hepatitis C at transplant, recipient age, recipient sex, and certain blood markers such as creatinine and hemoglobin. CONCLUSION: Noninvasive approaches using ML for predicting significant GF perform well when considering demographic, clinical, and laboratory data; however, this performance is not enhanced with the use of US images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".