DocumentCode :
2571434
Title :
Pre- and post-operative predictions of recurrence in patients with cancer of the oesophago-gastric junction using radial basis function artificial neural networks
Author :
Naguib, R.N.G. ; Wayman, J. ; Bennett, M.K. ; Raimes, S.A. ; Griffin, S.M.
Author_Institution :
Dept. of Electr. & Electron. Eng., Newcastle upon Tyne Univ., UK
Volume :
6
fYear :
1998
fDate :
29 Oct-1 Nov 1998
Firstpage :
3335
Abstract :
Pre-operative data from 103 patients undergoing potentially curative resection of adenocarcinoma of the oesophago-gastric junction were collected prospectively and analysed by a radial basis function artificial neural network system. A separate neural structure was designed for use with selected additional post-operative parameters. Output variables for both systems were recurrence at 12, 18 and 24 months. Prediction specificities, using pre-operative data alone, were 60%, 62% and 60%, respectively. Inclusion of post-operative parameters improved the specificity to 72.7% at 12 months, 66% at 18 months and 62.2% at 24 months, while the sensitivity of prediction at 18 months reached 90% and at two years 93%. There was a strong correlation between the predictive value of pre- and post-operative findings in individual patients at each time period (correlation coefficient=0.6333, p<0.0001; 0.4873, p=0.0083; 0.266, p=0.0025). This study demonstrates that artificial neural networks are able to reliably predict, even with limited clinical pre-operative information, patients who are destined to fail when treated by surgery alone. This approach may have a role in assisting clinicians to achieve more appropriate selection of patients for surgery and neo-adjuvant therapy
Keywords :
biological organs; cancer; forecasting theory; medical computing; radial basis function networks; sensitivity analysis; statistical analysis; surgery; adenocarcinoma; cancer recurrence prediction; correlation coefficient; neo-adjuvant therapy; neural structure; oesophageal neoplasms; oesophago-gastric junction; patient selection; post-operative parameters; potentially curative resection; pre-operative data; prediction sensitivity; prediction specificity; radial basis function artificial neural networks; statistical analysis; stomach neoplasms; surgical treatment; Artificial neural networks; Cancer; Intelligent networks; Lymph nodes; Medical treatment; Neoplasms; Oncological surgery; Pathology; Stomach; Tumors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE
Conference_Location :
Hong Kong
ISSN :
1094-687X
Print_ISBN :
0-7803-5164-9
Type :
conf
DOI :
10.1109/IEMBS.1998.746214
Filename :
746214
Link To Document :
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