Abstract
Purpose
The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that smaller than 0.5 cm, the early detection of PM is still a difficult problem. This study is aiming at constructing a deep convolution neural network classifier based on meta-learning to predict PM.
Method
Peritoneal metastases are delineated on enhanced CT. The model is trained based on meta-learning, and features are extracted using multi-modal deep Convolutional Neural Network(CNN) with enhanced CT to classify PM. Besides, we evaluate the performance on the test dataset, and compare it with other PM prediction algorithm.
Results
The training datasets are consisted of 9574 images from 43 patients with PM and 67 patients without PM. The testing datasets are consisted of 1834 images from 21 testing patients. To increase the accuracy of the prediction, we combine the multi-modal inputs of plain scan phase, portal venous phase and arterial phase to build a meta-learning-based multi-modal PM predictor. The classifier shows an accuracy of 87.5% with Area Under Curve(AUC) of 0.877, sensitivity of 73.4%, specificity of 95.2% on the testing datasets. The performance is superior to routine PM classify based on logistic regression (AUC: 0.795), a deep learning method named ResNet3D (AUC: 0.827), and a domain generalization (DG) method named MADDG (AUC: 0.834).
Conclusions
we proposed a novel training strategy based on meta-learning to improve the model’s robustness to “unseen” samples. The experiments shows that our meta-learning-based multi-modal PM predicting classifier obtain more competitive results in synchronous PM prediction compared to existing algorithms and the model’s improvements of generalization ability even with limited data.
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Availability of data and materials
The datasets generated and/or analysed during the current study are not publicly available due to data privacy but are available from the corresponding author on reasonable request.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were perfomed by bin li, luan ye, lingxiang ruan, the first draft of the manuscript was written by hangyu zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Zhang, H., Zhu, X., Li, B. et al. Development and validation of a meta-learning-based multi-modal deep learning algorithm for detection of peritoneal metastasis. Int J CARS 17, 1845–1853 (2022). https://doi.org/10.1007/s11548-022-02698-w
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DOI: https://doi.org/10.1007/s11548-022-02698-w