ArticleAsian Pacific journal of cancer prevention : APJCP2026
Time-Series Forecasting of Radiotherapy Utilization in Older Adults in Southern Thailand's Largest Quaternary Hospital: A Retrospective Study.
Article in Asian Pacific journal of cancer prevention : APJCP, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to forecast the radiotherapy demand among geriatric patients in Southern Thailand's largest quaternary hospital by 2030 using an Autoregressive Integrated Moving Average (ARIMA) model.
methodsThis retrospective analysis was conducted using data from January 2004 and December 2022 and comprised patients aged ≥65 years who received radiotherapy. Monthly time-series data were analyzed in two phases. First, descriptive statistics were used to summarize patient demographics, cancer types, and treatment intent over time. Time-series decomposition and automatic machine learning were used to explore these patterns. Stationarity was assessed using the augmented Dickey-Fuller test. The model parameters were selected based on autocorrelation and partial autocorrelation plots and refined through optimization. Model selection was performed based on the Akaike Information Criterion, and forecasting accuracy was measured using the Mean Absolute Percentage Error (MAPE). Residual diagnostics included the Ljung-Box and Jarque-Bera tests as well as the assessment of heteroskedasticity.
resultsOf the 39,653 patients who underwent radiotherapy, 10,717 (27%) were aged ≥65 years (mean age 71.8; 60% male). The most common cancers were head and neck, lung, colorectal, and breast. Most patients received curative treatment, with increasing trends in radiotherapy utilization, particularly for lung, colorectal, breast, and prostate cancers. The optimal model, ARIMA(3,1,0)(0,0,1,4), incorporating exogenous variables related to the older adult population in Southern Thailand, achieved a MAPE of 0.17 and successfully passed all residual diagnostics. By 2030, the model forecasted approximately 74.7 new monthly cases of geriatric radiotherapy, with a 95% confidence interval of 53.8-95.7.
conclusionThe demand for radiotherapy among older adults is projected to increase, underscoring the need for capacity planning. Future studies should explore sophisticated prediction techniques and include more clinical variables to enhance the accuracy of forecasts and aid thorough oncology planning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.