OBJECTIVE: The aim of this study was to predict the number and trends of cancer cases for radiotherapy up to the year 2015 in Manipal Teaching Hospital, Pokhara, Nepal. METHODS: A retrospective study was carried out on data retrieved from the radiotherapy treatment records of patients treated at Manipal Teaching Hospital between 28 September 2000 and 31 December 2008. Different statistical programmes were used for statistical modelling and prediction. Using curve-fitting methods, Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, Exponential, and Growth models were tested. RESULTS: Including constant term, none of the models were best fitted. However, excluding the constant term, the cubic model was best fitted; R2=0. 95, p=0.001 for total cancer cases, R2=0. 94, p=0.001 for female cancer cases and R2=0. 95, p=0.001 for male cancer cases. The cancer cases estimated using cubic model showed a steady increase in the total frequency of cancers (including male and female cancer cases) following the year 2010. The three most common cancers reported were head and neck 24.2% (CI 21.6 - 27.0), lung 20.9% (CI 18.4 -23.6), cervix 15.9% (CI 13.7-18.3) respectively. CONCLUSION: The cancer cases in need of radiotherapy will increase in future years. The curve fitting method could be an effective exploratory modelling technique for predicting cancer frequency and trends over the years.
OBJECTIVE: The aim of this study was to predict the number and trends of cancer cases for radiotherapy up to the year 2015 in Manipal Teaching Hospital, Pokhara, Nepal. METHODS: A retrospective study was carried out on data retrieved from the radiotherapy treatment records of patients treated at Manipal Teaching Hospital between 28 September 2000 and 31 December 2008. Different statistical programmes were used for statistical modelling and prediction. Using curve-fitting methods, Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, Exponential, and Growth models were tested. RESULTS: Including constant term, none of the models were best fitted. However, excluding the constant term, the cubic model was best fitted; R2=0. 95, p=0.001 for total cancer cases, R2=0. 94, p=0.001 for female cancer cases and R2=0. 95, p=0.001 for male cancer cases. The cancer cases estimated using cubic model showed a steady increase in the total frequency of cancers (including male and female cancer cases) following the year 2010. The three most common cancers reported were head and neck 24.2% (CI 21.6 - 27.0), lung 20.9% (CI 18.4 -23.6), cervix 15.9% (CI 13.7-18.3) respectively. CONCLUSION: The cancer cases in need of radiotherapy will increase in future years. The curve fitting method could be an effective exploratory modelling technique for predicting cancer frequency and trends over the years.