Title :
Forecasting the performance status of head and neck cancer patient treatment by an interval arithmetic pruned perceptron
Author :
Drago, Gian Paolo ; Setti, Ernesto ; Licitra, Lisa ; Liberati, Diego
Author_Institution :
Ist. per i Circuiti Elettronici, CNR, Genoa, Italy
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.
Keywords :
cancer; learning (artificial intelligence); patient treatment; perceptrons; radiation therapy; Kamofsky performance status; artificial neural network; chemotherapy; combined chemo-radiation treatment; cumulative toxicity; head and neck cancer; interval arithmetic pruned perceptron; k-fold cross-validation; locoregional control; patient treatment performance status; predictive factors; radiotherapy; training process; Arithmetic; Artificial neural networks; Cancer; Diseases; Medical treatment; Metastasis; Neck; Neural networks; Oncological surgery; Testing; Antineoplastic Agents; Clinical Trials, Phase II as Topic; Head and Neck Neoplasms; Humans; Karnofsky Performance Status; Models, Statistical; Neural Networks (Computer); Predictive Value of Tests; Radiotherapy, Adjuvant; Risk Factors; Survival Analysis; Treatment Outcome;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2002.800788