The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC)

M. F. Jefferson; N. Pendleton; S. B. Lucas; M. A. Horan

The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC)

M. F. Jefferson, N. Pendleton, S. B. Lucas, M. A. Horan

Division of Neuroscience

Salford Royal NHS Foundation Trust

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

Abstract

I. INTRODUCTION No ns mall Cell Lung Cancer (NSCLC) accounts for approximately three quarters of all lung cancer his to logies. Surgical resection is the preferred treatment and approximately 50,000 operations are performed in the United States alone each year [1]. The over all prognosis for resected NSCLC is poor with fewer than a third of patients who undergo resection still alive 5 year s later [2].

Original language	English
Title of host publication	Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management
Editors	Raouf N.G. Naguib, Gajanan V. Sherbet
Publisher	CRC Press
Pages	39-54
Number of pages	16
ISBN (Electronic)	9781420036381
ISBN (Print)	0849396921, 9780849396922
Publication status	Published - 1 Jan 2001

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This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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abstract = "I. INTRODUCTION No ns mall Cell Lung Cancer (NSCLC) accounts for approximately three quarters of all lung cancer his to logies. Surgical resection is the preferred treatment and approximately 50,000 operations are performed in the United States alone each year [1]. The over all prognosis for resected NSCLC is poor with fewer than a third of patients who undergo resection still alive 5 year s later [2].",

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The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC). / Jefferson, M. F.; Pendleton, N.; Lucas, S. B. et al.
Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management. ed. / Raouf N.G. Naguib; Gajanan V. Sherbet. CRC Press, 2001. p. 39-54.

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC)

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AU - Pendleton, N.

AU - Lucas, S. B.

AU - Horan, M. A.

PY - 2001/1/1

Y1 - 2001/1/1

N2 - I. INTRODUCTION No ns mall Cell Lung Cancer (NSCLC) accounts for approximately three quarters of all lung cancer his to logies. Surgical resection is the preferred treatment and approximately 50,000 operations are performed in the United States alone each year [1]. The over all prognosis for resected NSCLC is poor with fewer than a third of patients who undergo resection still alive 5 year s later [2].

AB - I. INTRODUCTION No ns mall Cell Lung Cancer (NSCLC) accounts for approximately three quarters of all lung cancer his to logies. Surgical resection is the preferred treatment and approximately 50,000 operations are performed in the United States alone each year [1]. The over all prognosis for resected NSCLC is poor with fewer than a third of patients who undergo resection still alive 5 year s later [2].

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M3 - Chapter

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SN - 0849396921

SN - 9780849396922

SP - 39

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BT - Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management

A2 - Naguib, Raouf N.G.

A2 - Sherbet, Gajanan V.

PB - CRC Press

ER -

The use of a genetic algorithm neural network (GANN) for prognosis in surgically treated nonsmall cell lung cancer (NSCLC)

Abstract

UN SDGs

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