Usefulness of an Artificial Neural Network for Differentiating Benign from Malignant Pulmonary Nodules on High-Resolution CT
Evaluation with Receiver Operating Characteristic Analysis
Yuichi Matsuki1,
Katsumi Nakamura1,
Hideyuki Watanabe1,
Takatoshi Aoki1,
Hajime Nakata1,
Shigehiko Katsuragawa2 and
Kunio Doi3
1
Department of Radiology, University of Occupational and Environmental Health
School of Medicine, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Japan
807-8555.
2
Nippon Bunri University General Research Center, Nippon Bunri University,
Ichiki 1727, Oita-shi, Japan 870-0397.
3
Kurt Rossmann Laboratories for Radiologic Image Research, Department of
Radiology, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL
60637.
View larger version (94K):
[in a new window]
|
Fig. 1. —54-year-old woman with lung cancer. High-resolution CT scan
shows nodule with spiculation in whole margin and obvious pleural
indentation.
|
|
View larger version (97K):
[in a new window]
|
Fig. 2. —58-year-old man with organizing pneumonia. High-resolution CT
scan shows nodule without spiculation or obvious pleural indentation.
|
|
View larger version (23K):
[in a new window]
|
Fig. 3. —Bar chart shows distribution of artificial neural network
output that indicates likelihood of malignancy for malignant and benign
nodules. Note that number of cases indicated equal to or more than 80% of
likelihood of malignancy is 70. Sixty-seven cases (96%) were malignant, and
only three cases (4%) were benign. Black bar = malignant, striped bar =
benign.
|
|
View larger version (15K):
[in a new window]
|
Fig. 4. —Graph shows receiver operating characteristic curve of
artificial neural network for differentiating benign from malignant nodules.
Note that Az value of artificial neural network was 0.951,
indicating high performance.
|
|
View larger version (15K):
[in a new window]
|
Fig. 5. —Graph shows average receiver operating characteristic curves
for differentiating benign from malignant nodules without and with artificial
neural network (ANN) output by attending radiologists. Note that observer
performance with ANN output was significantly improved. Solid line = with ANN
(Az = 0.985), dashed line = without ANN
(Az = 0.933).
|
|
View larger version (16K):
[in a new window]
|
Fig. 6. —Graph shows average receiver operating characteristic curves
for differentiating benign from malignant nodules without and with artificial
neural network (ANN) output by radiology fellows. Note that observer
performance with ANN output was significantly improved. Solid line = with ANN,
(Az = 0.932), dashed line = without ANN
(Az = 0.821).
|
|
View larger version (16K):
[in a new window]
|
Fig. 7. —Graph shows average receiver operating characteristic curves
for differentiating benign from malignant nodules without and with artificial
neural network (ANN) output by radiology residents. Note that observer
performance with ANN output was significantly improved. Solid line = with ANN
(Az = 0.961), dashed line = without ANN
(Az = 0.759).
|
|
View larger version (17K):
[in a new window]
|
Fig. 8. —Graph shows comparison of average receiver operating
characteristic (ROC) curves for all observers without and with artificial
neural network (ANN) output and ROC curves for ANN output alone. Note that
observer performance with ANN output was significantly higher than that
without ANN or than that with ANN alone. Solid line = with ANN
(Az = 0.959), dotted line = ANN alone
(Az = 0.951), dashed line = without ANN
(Az = 0.831).
|
|
View larger version (16K):
[in a new window]
|
Fig. 9. —Histogram shows number of cases affected (>30) due to
artificial neural network output on benign nodules. Note that number of cases
affected beneficially was significantly higher than number of cases affected
detrimentally for benign nodules. Observers A—D are attending
radiologists, observers E—H are radiology fellows, and observers
I—K and L are radiology residents.
|
|
View larger version (17K):
[in a new window]
|
Fig. 10. —Histogram shows number of cases affected (>30) due to
artificial neural network output on malignant nodules. Note that number of
cases affected beneficially was significantly higher than number of cases
affected detrimentally for malignant nodules. Observers A—D are
attending radiologists, observers E—H are radiology fellows, and
observers I—L are radiology residents.
|
|
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg 
Reddit 
Technorati What's this?
Copyright © 2002 by the American Roentgen Ray Society.
