A multi-parameterized artificial neural network for lung cancer risk prediction.
Authors
Hart GR, Roffman DA, Decker R, Deng J
Affiliations
- Department of Therapeutic Radiology, School of Medicine, Yale University, New Haven, Connecticut, United States of America.
Abstract
The objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate our ANN, with inputs: gender, age, BMI, diabetes, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. We identified 648 cancer and 488,418 non-cancer cases. For the training set the sensitivity was 79.8% (95% CI, 75.9%-83.6%), specificity was 79.9% (79.8%-80.1%), and AUC was 0.86 (0.85-0.88). For the validation set sensitivity was 75.3% (68.9%-81.6%), specificity was 80.6% (80.3%-80.8%), and AUC was 0.86 (0.84-0.89). Our results indicate that the use of an ANN based on personal health information gives high specificity and modest sensitivity for lung cancer detection, offering a cost-effective and non-invasive clinical tool for risk stratification.
Publication Details
PubMed ID
30356283
Digital Object Identifier
10.1371/journal.pone.0205264
Publication
PLoS One. 2018; Volume 13 (Issue 10): Pages e0205264