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Deep Learning for Classification of Bone Lesions on Routine MRI.
Feyisope R Eweje1, Bingting Bao2, Jing Wu2
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, 19104, USA.
Ebiomedicine
|June 7, 2021
Summary
A new deep learning model can differentiate benign and malignant bone lesions using MRI scans and patient data, performing comparably to expert radiologists. This AI tool could improve diagnostic accuracy and reduce unnecessary procedures.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Distinguishing benign from malignant bone lesions is challenging for radiologists due to overlapping imaging features.
- Routine magnetic resonance imaging (MRI) and patient demographics are key data sources for bone lesion analysis.
Purpose of the Study:
- To develop a deep learning algorithm for differentiating benign and malignant bone lesions.
- To assess the algorithm's performance against human expert classification.
Main Methods:
- A dataset of 1,060 bone lesions from five institutions was used for model development, internal validation, and external testing.
- EfficientNet-B0 architecture was employed for image-based models, combined with a logistic regression model using patient age, sex, and lesion location.
- A voting ensemble model was created and its performance compared to radiology experts.
Main Results:
- The deep learning ensemble model achieved an accuracy of 0.76, sensitivity of 0.79, and specificity of 0.75, with a ROC AUC of 0.82.
- Performance was comparable to expert radiologists (accuracy 0.73, sensitivity 0.81, specificity 0.66).
- External testing demonstrated a ROC AUC of 0.79 for the model.
Conclusions:
- Deep learning models can effectively distinguish between benign and malignant bone lesions, matching expert performance.
- These AI-driven tools have the potential to enhance diagnostic accuracy and streamline patient referrals, reducing unnecessary biopsies and specialized center visits.
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