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L. Fu
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P3.13 - Radiology/Staging/Screening (ID 729)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 10/18/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P3.13-022 - 3D CNNs for Recognition of Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma (ID 9799)
09:30 - 09:30 | Author(s): L. Fu
- Abstract
Background:
In this study, we built three 3-dimensional convolutional neural networks (CNN) for recognition of epidermal growth factor receptor (EGFR) mutation status in Chinese patients with lung adenocarcinomas based on non-enhanced computed tomography (CT) images.
Method:
From October 2008 to December 2015, 405 patients with lung adenocarcinomas were included in this retrospective study. Their pathological phenotypes and EGFR mutation status were gained from surgical resections. Their CT images used in this study were taken before any invasive operation. Tumors with a diameter smaller than 8 mm or have ground glass component were excluded. Region of interest that includes tumors were segmented manually by clinicians and preprocessed to have uniform size and grey-level range before applied to CNNs. The three CNNs have 4 convolutional and 1 full connection layers between input and output layers. The inputs size of three CNNs are 21×21×21, 31×31×31, and 41×41×41, respectively. The outputs of the CNN are the probabilities of mutant and wild status. The CNN classifier’s performance was then validated using an independent set and evaluated using area under curve (AUC) values of the receiver operating characteristic.
Result:
405 patients diagnosed with lung adenocarcinoma staging I to IV were included in this study (195 male, 210 female; 61 smokers, 344 non-smokers). The patients received surgery based treatment and their tumor stage was based on pathological reports. EGFR mutations (mainly 19del and 21L858R) were found in 198/320(61.9%) and 56/85(65.9%) patients in training and validation sets, respectively. The CNN showed an AUC of 0.767 (95% confidence interval: 0.668-0.866, p<0.001) in the validation set. The sensitivity and specificity are 62.5% and 89.7% at best diagnostic decision point. These results were highest among published results of only using images to recognize EGFR.
Conclusion:
The CNN showed potential ability to recognize EGFR mutation status in patients with lung adenocarcinomas and could be improved in the future works to help make clinical decisions.
