Artificial neural networks for diagnosis and survival prediction in colon cancer

By F. Ahmed. Molecular Cancer, 2005

531 citations recorded by the source corpus.

ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cd9a7b9a316f8ce7872f88f31985148b1c67e5f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Guillaume Lemaitre
2. Robert Marti
3. Fabrice Mériaudeau
4. Prashanth Rawla

In Oncology

1. Robert Langer
2. Prashanth Rawla

In Radiology, Nuclear Medicine and Imaging

1. Robert Marti
2. Paul Michael Walker
3. Fabrice Mériaudeau
4. Jordi Freixenet
5. Prashanth Rawla

Citations in the corpus

• Epidemiology of Prostate Cancer (2019, background): “This metabolic switch is known to occur early in prostate tumorigenesis [334].”
• Artificial neural networks in medical diagnosis (2013, background): “However, the most commonly used is back propagation (Zupan and Gasteiger 1999; Ahmed 2005).”
• Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review (2015, background): “64 ppm) [88] Lower concentration [83,84,86] Higher concentration [83,84,86] þ [85]”
• Application of Micro- and Nano-Electromechanical Devices to Drug Delivery (2006, background): “ANNs have a number of properties and capabilities that are important for critical, computationintensive applications: parallel processing, fault tolerance, self-organization, generalization ability, and continuous adaptivity (20,21).”