Abstract
Objective
To predict the local recurrence of giant cell bone tumors (GCTB) on MR features and the clinical characteristics after curettage using a deep convolutional neural network (CNN).
Methods
MR images were collected from 56 patients with histopathologically confirmed GCTB after curettage who were followed up for 5.8 years (range, 2.0 to 9.5 years). The inception v3 CNN architecture was fine-tuned by two categories of the MR datasets (recurrent and non-recurrent GCTB) obtained through data augmentation and was validated using fourfold cross-validation to evaluate its generalization ability. Twenty-eight cases (50%) were chosen as the training dataset for the CNN and four radiologists, while the remaining 28 cases (50%) were used as the test dataset. A binary logistic regression model was established to predict recurrent GCTB by combining the CNN prediction and patient features (age and tumor location). Accuracy and sensitivity were used to evaluate the prediction performance.
Results
When comparing the CNN, CNN regression, and radiologists, the accuracies of the CNN and CNN regression models were 75.5% (95% CI 55.1 to 89.3%) and 78.6% (59.0 to 91.7%), respectively, which were higher than the 64.3% (44.1 to 81.4%) accuracy of the radiologists. The sensitivities were 85.7% (42.1 to 99.6%) and 87.5% (47.3 to 99.7%), respectively, which were higher than the 58.3% (27.7 to 84.8%) sensitivity of the radiologists (p < 0.05).
Conclusion
The CNN has the potential to predict recurrent GCTB after curettage. A binary regression model combined with patient characteristics improves its prediction accuracy.
Key Points
• Convolutional neural network (CNN) can be trained successfully on a limited number of pre-surgery MR images, by fine-tuning a pre-trained CNN architecture.
• CNN has an accuracy of 75.5% to predict post-surgery recurrence of giant cell tumors of bone, which surpasses the 64.3% accuracy of human observation.
• A binary logistic regression model combining CNN prediction rate, patient age, and tumor location improves the accuracy to predict post-surgery recurrence of giant cell bone tumors to 78.6%.
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Abbreviations
- CNN:
-
Convolutional neural network
- GCTB:
-
Giant cell tumor of bone
- PMMA:
-
Polymethylmethacrylate
- RMSProp:
-
Root mean square prop
- t-SNE:
-
t-Distributed stochastic neighbor embedding
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Acknowledgements
We thank Dr. Xiaochun Yuan, Dr. Qin Chen, Dr. Gehong Yao, and Dr. Lin Zhang in Shanghai General Hospital for analyzing the images.
Funding
This study was sponsored by the National Natural Science Foundation of China (project no. 81471662), Ministry of Science and Technology of China (2016YFE0103000), Science and Technology Commission of Shanghai Municipality (16411968500 and 16410722300), Shanghai Jiao Tong University (ZH2018ZDB10), Shanghai Jiao Tong University School of Medicine - Gaofeng Clinical Medicine Grant Support (20181814), and Clinical Research Innovation Plan of Shanghai General Hospital (CTCCR-2018B04).
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The scientific guarantor of this publication is Prof. Xueqian Xie, MD PhD, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the co-authors, Dr. Jiapan Guo, PhD, from the University Medical Center Groningen, The Netherlands, kindly provided statistical advice and IT supports for this manuscript.
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Written informed consent was waived by the Institutional Review Board.
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• Retrospective
• Diagnostic study
• Performed at one institution
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He, Y., Guo, J., Ding, X. et al. Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images. Eur Radiol 29, 5441–5451 (2019). https://doi.org/10.1007/s00330-019-06082-2
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DOI: https://doi.org/10.1007/s00330-019-06082-2