Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound
Notice bibliographique
Résumé
// Archya Dasgupta 1 , 2 , 3 , Divya Bhardwaj 3 , Daniel DiCenzo 3 , Kashuf Fatima 3 , Laurentius Oscar Osapoetra 3 , Karina Quiaoit 3 , Murtuza Saifuddin 3 , Stephen Brade 3 , Maureen Trudeau 4 , 5 , Sonal Gandhi 4 , 5 , Andrea Eisen 4 , 5 , Frances Wright 6 , 7 , Nicole Look-Hong 6 , 7 , Ali Sadeghi-Naini 1 , 3 , 8 , 9 , Belinda Curpen 10 , 11 , Michael C. Kolios 12 , Lakshmanan Sannachi 3 and Gregory J. Czarnota 1 , 2 , 3 , 8 1 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada 2 Department of Radiation Oncology, University of Toronto, Toronto, Canada 3 Physical Sciences, Sunnybrook Research Institute, Toronto, Canada 4 Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada 5 Department of Medicine, University of Toronto, Toronto, Canada 6 Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada 7 Department of Surgery, University of Toronto, Toronto, Canada 8 Department of Medical Biophysics, University of Toronto, Toronto, Canada 9 Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada 10 Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada 11 Department of Medical Imaging, University of Toronto, Toronto, Canada 12 Department of Physics, Ryerson University, Toronto, Canada Correspondence to: Gregory J. Czarnota, email: gregory.czarnota@sunnybrook.ca Keywords: radiomics; breast cancer; quantitative ultrasound; recurrence; machine learning Received: August 23, 2021     Accepted: November 10, 2021     Published: December 07, 2021 Copyright: © 2021 Dasgupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Background: The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC). Materials and Methods: A prospective study was conducted in patients with LABC ( n = 83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation. Results: With a median follow up of 69 months (range 7–118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% ( p = 0.003), and the predicted 5-year overall survival was 85% and 74% ( p = 0.083), respectively. Conclusions: A QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.
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Comment cette classification a été obtenuedéplier
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 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».