<i>A priori</i> prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods
Notice bibliographique
Résumé
// Laurentius O. Osapoetra 1 , 2 , 3 , 4 , Lakshmanan Sannachi 1 , 2 , 3 , 4 , Karina Quiaoit 1 , 2 , 3 , Archya Dasgupta 1 , 2 , 3 , Daniel DiCenzo 1 , 2 , 3 , Kashuf Fatima 1 , 2 , 3 , Frances Wright 5 , 6 , Robert Dinniwell 7 , 8 , 9 , Maureen Trudeau 10 , 11 , Sonal Gandhi 10 , 11 , William Tran 1 , 2 , 12 , Michael C. Kolios 13 , Wei Yang 14 and Gregory J. Czarnota 1 , 2 , 3 , 4 1 Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada 2 Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada 3 Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada 4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada 5 Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada 6 Department of Surgery, University of Toronto, Toronto, ON, Canada 7 Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada 8 Radiation Oncology, London Health Sciences Centre, London, ON, Canada 9 Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 10 Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada 11 Department of Medicine, University of Toronto, Toronto, ON, Canada 12 Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada 13 Department of Physics, Ryerson University, Toronto, ON, Canada 14 Department of Diagnostic Radiology, University of Texas, Houston, Texas, USA Correspondence to: Gregory J. Czarnota, email: gregory.czarnota@sunnybrook.ca Keywords: radiomics; breast cancer; texture-derivate; quantitative ultrasound; neoadjuvant chemotherapy Received: August 21, 2020     Accepted: December 29, 2020     Published: January 19, 2021 Copyright: © 2021 Osapoetra 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 Purpose: We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation. Materials and Methods: QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated. Results: A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy. Conclusions: A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.
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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 | 0,001 |
| 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 ».