A “calcification”-enhanced deep learning approach for precise differentiation of thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Calcification is one of the most valuable imaging features in the ultrasound diagnosis of thyroid nodules. A calcification-enhanced deep learning (DL) approach was developed in this study for the automatic detection of thyroid nodules and their intranodular calcifications from ultrasound images. Calcification features were integrated into the modeling process to improve the accuracy of benign-malignant differentiation for thyroid nodules. METHODS: A total of 6886 thyroid nodules from 3 hospitals, collected between January 2014 and March 2024, were retrospectively included in this study. These nodules were partitioned into training, validation, and test sets at a ratio of 7:1:2. All nodules had a clearly documented final clinical diagnosis of benign or malignant status. A DL model that integrates intranodular calcification features and nodule imaging features was constructed. The model was trained using the training set, hyperparameters were optimized using the validation set, and final evaluation was conducted on an independent test set. The area under the receiver operating characteristic curve (AUC) was used as the primary evaluation metric. RESULTS: Among the 6886 thyroid nodules included in this study, 4433 were benign and 2453 were malignant. DLAM−CFF—a DL model that integrates intranodular calcification features and nodule imaging features—exhibited excellent performance in differentiating benign from malignant thyroid nodules within the independent test cohort. Its sensitivity, specificity, and accuracy were 0.863, 0.864, and 0.864, respectively, with an AUC of 0.925. DLAM−CFF was compared with DLCFF (a DL model relying solely on nodule features) and traditional DL models, including Xception, InceptionResNetV2, DenseNet121, and ResNet50. The results indicated that the AUC values of these comparative models were 0.832, 0.805, 0.821, 0.813, and 0.798, respectively—all of which were significantly lower than those of DLAM−CFF (P < 0.05). CONCLUSIONS: The “calcification”-enhanced DL model proposed in this study not only enables the automatic detection of thyroid nodules and their intranodular calcifications in ultrasound images but also demonstrates excellent diagnostic performance in predicting the benignity or malignancy of thyroid nodules by integrating the overall features of nodules with calcification features.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".