The integration of chemotherapy and radiotherapy for the treatment of advanced head and neck cancer is still a matter of clinical investigation. An important limitation is that the concomitant administration of chemotherapy and radiotherapy still induces severe toxicity. In this paper, a simple artificial neural network is used to predict, on the basis of biological and clinical data, if the cumulative toxicity of the combined chemo-radiation treatment itself would be tolerated. The resulting method, tested on clinical data from a phase II trial, proved to be able to forecast which patients will tolerate a combined chemo-radiotherapeutic approach. This result should open a new perspective in the clinical approach, by supplying a potential predictive indicator for toxicity.

Forecasting the performance status of head and neck cancer patient treatment by an interval arithmetic pruned perceptron

D Liberati
2002

Abstract

The integration of chemotherapy and radiotherapy for the treatment of advanced head and neck cancer is still a matter of clinical investigation. An important limitation is that the concomitant administration of chemotherapy and radiotherapy still induces severe toxicity. In this paper, a simple artificial neural network is used to predict, on the basis of biological and clinical data, if the cumulative toxicity of the combined chemo-radiation treatment itself would be tolerated. The resulting method, tested on clinical data from a phase II trial, proved to be able to forecast which patients will tolerate a combined chemo-radiotherapeutic approach. This result should open a new perspective in the clinical approach, by supplying a potential predictive indicator for toxicity.
2002
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
49
782
787
interval arithmetic
learning
neural networks
perceptrons
predictive factors
Pubblicazione sulla rivista che da decenni e' tra le piu' autorevoli in campo interanzionale nel settore interdisciplinare dell'Ingegneria dell'informazione applicata alla biomedicina, nota a chiunque sia attivo nel settore. Il fattore di impatto nel 2002 era pari a 1.665
4
info:eu-repo/semantics/article
262
P Drago, G; Licitra, L; Setti, E; Liberati, D
01 Contributo su Rivista::01.01 Articolo in rivista
none
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/49162
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