Yuki Sasaki1,2, Yohan Kondo3, Tadashi Aoki4, Naoya Koizumi5, Toshiro Ozaki5, Hiroshi Seki5. 1. Division of Central Radiology, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata, 951-8566, Japan. ysasakiaca@gmail.com. 2. Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, Niigata, Japan. ysasakiaca@gmail.com. 3. Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, Niigata, Japan. 4. Department of Thoracic Surgery, Niigata Cancer Center Hospital, Niigata, Japan. 5. Department of Radiology, Niigata Cancer Center Hospital, Niigata, Japan.
Abstract
PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN). METHODS: This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence. RESULTS: The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86. CONCLUSION: We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.
PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN). METHODS: This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence. RESULTS: The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86. CONCLUSION: We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.
Authors: Fred R Hirsch; Giorgio V Scagliotti; James L Mulshine; Regina Kwon; Walter J Curran; Yi-Long Wu; Luis Paz-Ares Journal: Lancet Date: 2016-08-27 Impact factor: 79.321
Authors: Peter Goldstraw; Kari Chansky; John Crowley; Ramon Rami-Porta; Hisao Asamura; Wilfried E E Eberhardt; Andrew G Nicholson; Patti Groome; Alan Mitchell; Vanessa Bolejack Journal: J Thorac Oncol Date: 2016-01 Impact factor: 15.609