Predictions for Central Lymph Node Metastasis of Papillary Thyroid Carcinoma via CNN-Based Fusion Modeling of Ultrasound Images
Bibliographic record
Abstract
The diagnosis of central lymph node metastasis (CLNM) is very important for the treatment of papillary thyroid carcinoma (PTC), which remains highly subjective and depends on clinical experience. Traditional method based on radiomics tumor feature (RTF) extraction and classifications has its shortages to predict the CLNM and increase the possibility of over-diagnosis and over-treatment leading for PTC. In this paper, a convolutional neural network (CNN) based fusion modeling method is proposed for predictions of CLNM in ultrasound-negative patients with PTC. A CNN and a RTF extraction based random forest (RF) classifier are trained on the context image patches and tumor image patches, and the probability outputs from these two models are combined for predicting the CLNM. It is validated that the proposed method has better diagnostic performance than the conventional method on the test set. The area under the curve (AUC), accuracy, sensitivity, and specificity of the method in predicting CLNM are 0.9228, 83.09%, 86.17%, and 81.46%, respectively. It has the prospect to apply to diagnose ultrasound (US) images with the machine-learning diagnostic system.
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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".