Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Description
2.1.1. BRATS-2017 Dataset
2.1.2. The Cancer Genome Atlas Breast Invasive Carcinoma Dataset
2.2. Data Preprocessing
2.3. Multimodal Three-Dimensional DenseNet
2.3.1. Three-Dimensional Deep Learning Framework
2.3.2. DenseNet
2.3.3. Multi-Channel Technique
2.4. Implementation Details
2.4.1. Data Augmentation
2.4.2. Parameter Initialization
2.4.3. Transfer Learning
2.4.4. Training Tricks
2.5. Evaluation Criteria and Measurement
3. Results
3.1. Isocitrate Dehydrogenase Genotype Prediction Experiment
3.2. Comparing Single-Modality and Multi-Modality Model Experiment
3.3. Evaluating the Generalizability of M3D-DenseNet
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Clinical Features | Value |
---|---|
No. of patients | 167 |
Age, mean ± SD | 52.4 ± 15.5 |
<30 | 18 (10.8%) |
30–60 | 90 (53.9%) |
60–80 | 55 (32.9%) |
≥80 | 3 (1.8%) |
Sex | |
Male | 90 (54.2%) |
Female | 74 (45.8%) |
Tumor Grade | |
Low-grade (grade II, III) | 65 (38.9%) |
High-grade (grade IV) | 102 (61.1%) |
IDH genotype | |
Mutant | 53 (31.7%) |
Wild-type | 114 (68.3%) |
Layers | Output Size | M3D-DenseNet-121 | M3D-DenseNet-161 | M3D-DenseNet-169 | M3D-DenseNet-201 |
---|---|---|---|---|---|
Convolution | conv, stride 2 | ||||
Pooling | max pool, stride 2 | ||||
3D Dense Block (1) * | |||||
Transition Layer (1) * | conv | ||||
average pool, stride 2 | |||||
3D Dense Block (2) * | |||||
Transition Layer (2) * | conv | ||||
average pool, stride 2 | |||||
3D Dense Block (3) * | |||||
Transition Layer (3) * | conv | ||||
average pool, stride 2 | |||||
3D Dense Block (4) * | |||||
Classification Layer | global average pool | ||||
2D fully-connected, softmax |
Training Dataset | Validation Dataset | |||||||
---|---|---|---|---|---|---|---|---|
ACC | SN | SP | AUC | ACC | SN | SP | AUC | |
MNet-121 | 88.9% | 92.6% | 87.2% | 97.1% | 84.6% | 78.5% | 88.0% | 85.7% |
MNet-161 | 91.3% | 82.9% | 95.3% | 97.5% | 82.1% | 57.1% | 96.0% | 85.0% |
MNet-169 | 85.0% | 85.3% | 84.9% | 94.2% | 82.1% | 64.3% | 92.0% | 82.8% |
MNet-201 | 87.4% | 63.4% | 98.8% | 94.6% | 76.9% | 42.8% | 96.0% | 85.7% |
Training Dataset | Validation Dataset | |||||||
---|---|---|---|---|---|---|---|---|
ACC | SN | SP | AUC | ACC | SN | SP | AUC | |
SNet-T1 | 67.7% | 56.6% | 92.4% | 67.6% | 64.1% | 43.2% | 80.2% | 47.4% |
SNet-T2 | 77.9% | 82.9% | 75.6% | 87.5% | 74.4% | 78.6% | 72.0% | 81.6% |
SNet-T1Gd | 74.8% | 65.9% | 79.0% | 81.0% | 74.3% | 50.0% | 88.0% | 74.6% |
SNet-FLAIR | 76.3% | 31.7% | 97.7% | 81.0% | 71.8% | 35.7% | 92.0% | 72.6% |
MNet-121 | 88.9% | 92.6% | 87.2% | 97.1% | 84.6% | 78.5% | 88.0% | 85.7% |
Training Dataset | Validation Dataset | |||||||
---|---|---|---|---|---|---|---|---|
ACC | SN | SP | AUC | ACC | SN | SP | AUC | |
MNet-121 | 75.2% | 96.3% | 42.3% | 88.4% | 80.0% | 100% | 46.1% | 84.3% |
MNet-161 | 75.9% | 62.9% | 96.2% | 94.3% | 77.1% | 68.2% | 92.3% | 87.9% |
MNet-169 | 91.7% | 93.8% | 88.5% | 96.9% | 85.7% | 86.4% | 84.6% | 91.1% |
MNet-201 | 90.2% | 88.9% | 92.3% | 95.3% | 91.4% | 92.3% | 92.3% | 94.8% |
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Share and Cite
Liang, S.; Zhang, R.; Liang, D.; Song, T.; Ai, T.; Xia, C.; Xia, L.; Wang, Y. Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas. Genes 2018, 9, 382. https://doi.org/10.3390/genes9080382
Liang S, Zhang R, Liang D, Song T, Ai T, Xia C, Xia L, Wang Y. Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas. Genes. 2018; 9(8):382. https://doi.org/10.3390/genes9080382
Chicago/Turabian StyleLiang, Sen, Rongguo Zhang, Dayang Liang, Tianci Song, Tao Ai, Chen Xia, Liming Xia, and Yan Wang. 2018. "Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas" Genes 9, no. 8: 382. https://doi.org/10.3390/genes9080382
APA StyleLiang, S., Zhang, R., Liang, D., Song, T., Ai, T., Xia, C., Xia, L., & Wang, Y. (2018). Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas. Genes, 9(8), 382. https://doi.org/10.3390/genes9080382