Junfeng Xiong1,2, Xiaoyang Li3, Lin Lu2, Schwartz H Lawrence2, Xiaolong Fu3, Jun Zhao1, Binsheng Zhao2. 1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China. 2. Department of Radiology, Columbia University Medical Center, NY 10032 USA. 3. Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030 China.
Abstract
OBJECTIVE: To compare CNN models implemented using different strategies in the CT assessment of EGFR mutation status in patients with lung adenocarcinoma. METHODS: 1,010 consecutive lung adenocarcinoma patients with known EGFR mutation status were randomly divided into a training set (n=810) and a testing set (n=200). CNN models were constructed based on ResNet-101 architecture but implemented using different strategies: dimension filters (2D/3D), input sizes (small/middle/large and their fusion), slicing methods (transverse plane only and arbitrary multi-view planes), and training approaches (from scratch and fine-tuning a pre-trained CNN). The performance of the CNN models was compared using AUC. RESULTS: The fusion approach yielded consistently better performance than other input sizes, although the effect often did not reach statistical significance. Multi-view slicing was significantly superior to the transverse method when fine-tuning a pre-trained 2D CNN but not a CNN trained from scratch. The 3D CNN was significantly better than the 2D transverse plane method but only marginally better than the multi-view slicing method when trained from scratch. The highest performance (AUC=0.838) was achieved for the fine-tuned 2D CNN model when built using the fusion input size and multi-view slicing method. CONCLUSION: The assessment of EGFR mutation status in patients is more accurate when CNN models use more spatial information and are fine-tuned by transfer learning. Our finding about implementation strategy of a CNN model could be a guidance to other medical 3D images applications. Compared with other published studies which used medical images to identify EGFR mutation status, our CNN model achieved the best performance in a biggest patient cohort.
OBJECTIVE: To compare CNN models implemented using different strategies in the CT assessment of EGFR mutation status in patients with lung adenocarcinoma. METHODS: 1,010 consecutive lung adenocarcinoma patients with known EGFR mutation status were randomly divided into a training set (n=810) and a testing set (n=200). CNN models were constructed based on ResNet-101 architecture but implemented using different strategies: dimension filters (2D/3D), input sizes (small/middle/large and their fusion), slicing methods (transverse plane only and arbitrary multi-view planes), and training approaches (from scratch and fine-tuning a pre-trained CNN). The performance of the CNN models was compared using AUC. RESULTS: The fusion approach yielded consistently better performance than other input sizes, although the effect often did not reach statistical significance. Multi-view slicing was significantly superior to the transverse method when fine-tuning a pre-trained 2D CNN but not a CNN trained from scratch. The 3D CNN was significantly better than the 2D transverse plane method but only marginally better than the multi-view slicing method when trained from scratch. The highest performance (AUC=0.838) was achieved for the fine-tuned 2D CNN model when built using the fusion input size and multi-view slicing method. CONCLUSION: The assessment of EGFR mutation status in patients is more accurate when CNN models use more spatial information and are fine-tuned by transfer learning. Our finding about implementation strategy of a CNN model could be a guidance to other medical 3D images applications. Compared with other published studies which used medical images to identify EGFR mutation status, our CNN model achieved the best performance in a biggest patient cohort.
Authors: Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun Journal: Nature Date: 2017-01-25 Impact factor: 49.962