Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization
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Notice bibliographique
Résumé
Purpose: The distribution of a radiomic feature can differ between two institutions due to, for example, different image acquisition parameters, imaging systems, and contouring (i.e., tumor delineation) variations between clinicians. We aimed to develop effective statistical methods to successfully apply a radiomics-based predictive model to an external dataset. Theory: Two common feature normalization methods, rescaling and standardization, were evaluated for suitability in reducing feature variability between institutions. Standardization was chosen as the preferred approach, since rescaling was more sensitive to statistical outliers, and potentially reduced the discrimination power of a feature. It was also demonstrated why a dataset needs to be balanced between positive and negative outcomes before standardization is applied to it. Methods: In this paper, the novelty and power of the developed method for improved application of radiomics models on external datasets is tied to finding the normalization transformations separately for each independent set. The clinical effectiveness of the normalization method was shown using magnetic resonance images of primary uterine adenocarcinoma. Feature selection was done using 94 samples (Institution X), and feature testing was done using 63 samples (Institution Y). The outcomes studied were lymphovascular space invasion and cancer staging. Logistic regression was used to obtain the prediction accuracy of a feature. Promising radiomic features were defined as those with AUC > 0.75 in the training set. Results: When comparing the prediction accuracy, F-score, and Matthews correlation coefficient (MCC) of promising radiomic features in the testing set with and without standardization, there was an improvement due to standardization. For cancer stage prediction, average accuracy for all promising features rose from 0.64 to 0.72, average F-score from 0.48 to 0.71, and average MCC from 0.34 to 0.44 (p <; 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> ). Furthermore, when applying standardization, the ratio of sensitivity to specificity was close to unity in the testing set, comparable to the ratio in the training set. Without standardization, this ratio deviated significantly from unity in the testing set. Conclusions: Applying feature standardization separately for each independent set using imbalance adjustments was shown to improve the predictive ability of radiomic models when applied to a dataset from an external institution.
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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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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