Deep Learning for Classification of Bone Lesions on Routine MRI.
EBioMedicine
; 68: 103402, 2021 Jun.
Article
en En
| MEDLINE
| ID: mdl-34098339
ABSTRACT
BACKGROUND:
Radiologists have difficulty distinguishing benign from malignant bone lesions because these lesions may have similar imaging appearances. The purpose of this study was to develop a deep learning algorithm that can differentiate benign and malignant bone lesions using routine magnetic resonance imaging (MRI) and patient demographics.METHODS:
1,060 histologically confirmed bone lesions with T1- and T2-weighted pre-operative MRI were retrospectively identified and included, with lesions from 4 institutions used for model development and internal validation, and data from a fifth institution used for external validation. Image-based models were generated using the EfficientNet-B0 architecture and a logistic regression model was trained using patient age, sex, and lesion location. A voting ensemble was created as the final model. The performance of the model was compared to classification performance by radiology experts.FINDINGS:
The cohort had a mean age of 30±23 years and was 58.3% male, with 582 benign lesions and 478 malignant. Compared to a contrived expert committee result, the ensemble deep learning model achieved (ensemble vs. experts) similar accuracy (0·76 vs. 0·73, p=0·7), sensitivity (0·79 vs. 0·81, p=1·0) and specificity (0·75 vs. 0·66, p=0·48), with a ROC AUC of 0·82. On external testing, the model achieved ROC AUC of 0·79.INTERPRETATION:
Deep learning can be used to distinguish benign and malignant bone lesions on par with experts. These findings could aid in the development of computer-aided diagnostic tools to reduce unnecessary referrals to specialized centers from community clinics and limit unnecessary biopsies.FUNDING:
This work was funded by a Radiological Society of North America Research Medical Student Grant (#RMS2013) and supported by the Amazon Web Services Diagnostic Development Initiative.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Óseas
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Imagen por Resonancia Magnética
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Interpretación de Imagen Radiográfica Asistida por Computador
Tipo de estudio:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
Límite:
Adolescent
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Adult
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Child
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
EBioMedicine
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos