Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate.

2.50
Hdl Handle:
http://hdl.handle.net/10541/77922
Title:
Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate.
Authors:
Gulliford, Sarah L; Webb, Steve; Rowbottom, Carl G; Corne, David W; Dearnaley, David P
Abstract:
BACKGROUND AND PURPOSE: This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated. PATIENTS AND METHODS: A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data. RESULTS: It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome. CONCLUSION: ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes.
Affiliation:
Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Trust, Sutton, Surrey SM2 5PT, UK.
Citation:
Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. 2004, 71 (1):3-12 Radiother Oncol
Journal:
Radiotherapy and Oncology
Issue Date:
Apr-2004
URI:
http://hdl.handle.net/10541/77922
DOI:
10.1016/j.radonc.2003.03.001
PubMed ID:
15066290
Type:
Article
Language:
en
ISSN:
0167-8140
Appears in Collections:
All Christie Publications

Full metadata record

DC FieldValue Language
dc.contributor.authorGulliford, Sarah L-
dc.contributor.authorWebb, Steve-
dc.contributor.authorRowbottom, Carl G-
dc.contributor.authorCorne, David W-
dc.contributor.authorDearnaley, David P-
dc.date.accessioned2009-08-19T15:58:17Z-
dc.date.available2009-08-19T15:58:17Z-
dc.date.issued2004-04-
dc.identifier.citationUse of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. 2004, 71 (1):3-12 Radiother Oncolen
dc.identifier.issn0167-8140-
dc.identifier.pmid15066290-
dc.identifier.doi10.1016/j.radonc.2003.03.001-
dc.identifier.urihttp://hdl.handle.net/10541/77922-
dc.description.abstractBACKGROUND AND PURPOSE: This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated. PATIENTS AND METHODS: A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data. RESULTS: It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome. CONCLUSION: ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes.en
dc.language.isoenen
dc.subjectProstatic Canceren
dc.subject.meshGastrointestinal Hemorrhage-
dc.subject.meshHumans-
dc.subject.meshMale-
dc.subject.meshNeural Networks (Computer)-
dc.subject.meshProstate-Specific Antigen-
dc.subject.meshProstatic Neoplasms-
dc.subject.meshRadiotherapy, Conformal-
dc.subject.meshRectal Diseases-
dc.subject.meshRectum-
dc.subject.meshTreatment Outcome-
dc.subject.meshUrinary Bladder-
dc.subject.meshUrination Disorders-
dc.titleUse of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate.en
dc.typeArticleen
dc.contributor.departmentJoint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Trust, Sutton, Surrey SM2 5PT, UK.en
dc.identifier.journalRadiotherapy and Oncologyen

Related articles on PubMed

All Items in Christie are protected by copyright, with all rights reserved, unless otherwise indicated.