Kenji Suzuki PhD, PRESENTER: Nothing to Disclose

Abstract: HTML Purpose: Low-dose helical CT (LDCT) is being used as a useful tool for lung cancer screening. It is very difficult, however, for radiologists to distinguish between benign and malignant nodules in LDCT. Our purpose in this study is to develop computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a novel pattern-classification technique based on a massive training artificial neural network (MTANN). Methods and Materials: We developed a multiple MTANN that consists of three expert MTANNs for distinction between malignant nodules and three groups of benign nodules. Each of the MTANNs was trained by using input CT images and the desired output images containing the distribution for the "likelihood of being a malignant nodule." The MTANNs were massively trained by using a large number of overlapping sub-regions obtained from the input image. Each MTANN was trained by using ten typical malignant nodules and ten benign nodules representing a specific type of benign nodule in each of three groups, i.e., each MTANN was applied for distinguishing malignant nodules from (1) small benign nodules with relatively high contrast, (2) benign nodules in the peripheral region, and (3) other types of benign nodules. The three MTANNs were combined by using an integration ANN such that these three groups of benign nodules can be distinguished from malignant nodules. Our database consisted of 72 primary lung cancers in 69 patients and 412 benign nodules in 337 patients, which were obtained from a lung cancer screening program on 7,847 screenees with LDCT for three years in Nagano, Japan. All cancers were confirmed surgically, and all benign nodules were diagnosed based on follow-up CT examinations. Another database consisted of 15 "missed" lung cancers that were not reported as lung cancers due to interpretation errors during the initial clinical reading. The performance of the multiple MTANN was evaluated by using receiver operating characteristic (ROC) analysis. Results: Our scheme achieved an Az (area under the ROC curve) value of 0.87 in the round robin test, whereas an average Az value of 0.70 was obtained by 16 radiologists in our observer study. Our scheme identified 97.2% (70/72) of malignant nodules, and 86.7% (13/15) of interpretation error cases correctly as malignant, whereas 49.3% (203/412) of benign nodules were identified correctly as benign. Conclusion: Our computerized scheme for distinction between benign and malignant nodules would be useful in assisting radiologists in diagnosis of lung nodules. (K.D. is a shareholder in R2 Technology, Inc., Sunnyvale, CA, and Deus Technologies, Inc., Rockville, MD.) Questions about this event email: suzuki@uchicago.edu

Suzuki PhD, K, Computerized Scheme for Distinction between Benign and Malignant Nodules in Thoracic Low-dose CT by Use of Massive Training Artificial Neural Network.  Radiological Society of North America 2003 Scientific Assembly and Annual Meeting, November 30 - December 5, 2003 ,Chicago IL. http://archive.rsna.org/2003/3100417.html