Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther. (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther. (Articolo in rivista) (literal)

Anno

• 2000-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1007/BF02345741 (literal)

Alternative label

• G. Paoli, M. Muselli, R. Bellazzi, R. Corvò, D. Liberati, F. Foppiano (2000)
  Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther.
  in Medical & biological engineering & computing
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• G. Paoli, M. Muselli, R. Bellazzi, R. Corvò, D. Liberati, F. Foppiano (literal)

Pagina inizio

• 483 (literal)

Pagina fine

• 486 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 38 (literal)

Rivista

• Medical & biological engineering & computing (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 5 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)
• PubMe (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• M. Muselli, D. Liberati: CNR-IEIIT, Italy; G. Paoli,R. Corvò, F. Foppiano: Istituto Nazionale per la Ricerca sul Cancro, Largo R. Benzi 10, 16132 Genova, Italy; R. Bellazzi: Dipartimento di informatica e Sistemistica, Universitá di Pavia, Pavia, Italy . (literal)

Titolo

• Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther. (literal)

Abstract

• The aim of the study is to demonstrate the usefulness of a new, non-linear classifier method, called Hamming clustering (HC), in selecting prognostic variables affecting overall survival in patients with head and neck cancer. In particular, the aim is to identify whether tumour proliferation parameters can be predictive factors of response in a set of 115 patients that receive either alternating chemo-radiotherapy or accelerated or conventional radiotherapy. HC is able to generate a set of understandable rules underlying the study objective; it can also select a subset of input variables that represent good prognostic factors. HC has been compared with other standard classifiers, providing better results in terms of classification accuracy. In particular, HC obtains the best accuracy of 74.8% (sensitivity of 51.1% and specificity of 91.2%) about survival. The rules found show that, besides the classical, well-known variables concerning the tumour dimension and the involved lymphonodes, some biological parameters, such as DNA ploidy, are also useful as predictive factors. The aim of the study is to demonstrate the usefulness of a new, non-linear classifier method, called Hamming clustering (HC), in selecting prognostic variables affecting overall survival in patients with head and neck cancer. In particular, the aim is to identify whether tumour proliferation parameters can be predictive factors of response in a set of 115 patients that receive either alternating chemo-radiotherapy or accelerated or conventional radiotherapy. HC is able to generate a set of understandable rules underlying the study objective; it can also select a subset of input variables that represent good prognostic factors. HC has been compared with other standard classifiers, providing better results in terms of classification accuracy. In particular, HC obtains the best accuracy of 74.8% (sensitivity of 51.1% and specificity of 91.2%) about survival. The rules found show that, besides the classical, well-known variables concerning the tumour dimension and the involved lymphonodes, some biological parameters, such as DNA ploidy, are also useful as predictive factors. (literal)

Prodotto di

• Machine Learning for Biological Data (INT.P02.002.001) (Modulo)
• Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni (IEIIT) (Istituto)

Autore CNR

• MARCO MUSELLI (Unità di personale interno)
• DIEGO LIBERATI (Persona)

Insieme di parole chiave

• Keywords of "Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther." (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni (IEIIT) (Istituto)
• Machine Learning for Biological Data (INT.P02.002.001) (Modulo)

Autore CNR di

• MARCO MUSELLI (Unità di personale interno)
• DIEGO LIBERATI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Medical & biological engineering & computing (Rivista)

Insieme di parole chiave di

• Keywords of "Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther." (Insieme di parole chiave)