Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloHamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther.
Anno di pubblicazione2000
FormatoCartaceo
Autore/iG. Paoli, M. Muselli, R. Bellazzi, R. Corvò, D. Liberati, F. Foppiano
Affiliazioni autoriM. 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 .
Autori CNR e affiliazioni
  • DIEGO LIBERATI
  • MARCO MUSELLI
Lingua/e
  • inglese
AbstractThe 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.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da483
Pagine a486
Pagine totali-
RivistaMedical & biological engineering & computing
Attiva dal 1977
Editore: Peter Peregrinus. - Stevenage, Herts.
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0140-0118
Titolo chiave: Medical & biological engineering & computing
Titolo abbreviato: Med. biol. eng. comput.
Titolo alternativo: Medical and biological engineering and computing
Numero volume della rivista38
Fascicolo della rivista5
DOI10.1007/BF02345741
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-0034281914)
  • PubMed (Codice:11094802)
  • ISI Web of Science (WOS) (Codice:000090065000002)
Parole chiavehead and neck cancer, classification, Hamming Clustering, radiotherapy
Link (URL, URI)-
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR
    Progetti Europei-
    Allegati
    • Hamming Clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation ther.