A “calcification”-enhanced deep learning approach for precise differentiation of thyroid nodules
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle