NDLI: Application of artificial neural network and multiple linear regression models for predicting survival time of patients with non-small cell cancer using multiple prognostic factors including FDG-PET measurements

Content Provider	IEEE Xplore Digital Library
Author	Yonglin Pu Baad, M.J. Yulei Jiang Yisheng Chen
Copyright Year	2014
Description	Author affiliation: Novast Labs., Ltd., Nantong, China (Yisheng Chen) \|\| Dept. of Radiol., Univ. of Chicago, Chicago, IL, USA (Yonglin Pu; Baad, M.J.; Yulei Jiang)
Abstract	We hypothesize and demonstrate that artificial neural networks (ANN) can perform better than multiple linear regression models in overcoming the limitations of the current TNM staging system for predicting the overall survival time of patients with non-small cell lung cancer (NSCLC). Better prognostication of survival was achieved by including additional prognostic factors, such as FDG-PET measurements and other clinical and pathological prognostic factors. The use of an ANN resulted in a substantial improvement in correlation between actual and predicted months of survival in 328 patients with NSCLC. The ANN resulted in an increase in $R^{2},$ from 0.66 to 0.774, and a reduction in standard deviation, from 17.4 months to 14 months, when compared to multiple linear regressions. Furthermore, the cross-validation results of $R^{2}=0.608$ suggests that the ANN model was capable of predicting survival for patients who were not included in the database for building the ANN model.
Starting Page	225
Ending Page	230
File Size	2864941
Page Count	6
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889882
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Cancer Lungs Predictive models Positron emission tomography Tumors Surgery
Content Type	Text
Resource Type	Article

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