Computer‐aided diagnosis for estimating the malignancy grade of hepatocellular carcinoma using contrast‐enhanced ultrasound: an <scp>ROC</scp> observer study
Bibliographic record
Abstract
BACKGROUND & AIMS: We are developing a computer-aided diagnosis (CAD) system for estimating the malignancy grade of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). In this study, observers estimated the malignancy grade of HCC with and without the cues provided by CAD. MATERIALS AND METHODS: Institutional review board approval was obtained and informed consent was waived. A total of 232 histologically confirmed HCCs were studied: 76 well-differentiated HCC (w-HCC), 133 moderately differentiated HCC (m-HCC), and 23 poorly differentiated HCC (p-HCC). In this observer study, CEUS vascular images acquired using the maximum intensity projection technique were displayed together with static B-mode and Kupffer-phase (defined as 10 min after injection) images. Five hepatologists independently assigned confidence ratings for the malignancy grade of each HCC. Each hepatologist first read each case without CAD and then immediately afterwards with CAD. The observers' rating data were evaluated by multireader multicase receiver operating characteristic (ROC) analysis. RESULTS: The overall sensitivity of our CAD system for discrimination between three histological differentiation grades of HCC was 87.5% (203/232). For discrimination between w-HCC and m/p-HCC, the mean area under the ROC curve (AUC) for the five observers was significantly improved from 0.779 ± 0.074 to 0.872 ± 0.090 with CAD (P = 0.0069). For discrimination between m-HCC and p-HCC, the mean AUC was also significantly improved from 0.713 ± 0.107 to 0.863 ± 0.101 with CAD (P = 0.0321). CONCLUSION: The use of our CAD system can significantly improve the diagnostic performance of hepatologists in discriminating between three histological differentiation grades of HCC using CEUS.
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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 | 0.000 | 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".