Predicting outcomes in radiation oncology--multifactorial decision support systems

Abstract

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Figure 1

Schematic overview of methodological processes in clinical decision-support system development, describing model development, assessment of clinical usefulness and what ideally to publish. The coloured, parallel lines represent heterogeneous data, which have been split early for independent validation (but without internal cross-validation).

Figure 2

The importance of considering measured dose for outcome prediction for a patient with prostate cancer. a | Original planning CT scan that includes contours of the prostate (red), bladder (yellow), exterior wall of the rectum (blue) and seminal vesicles (green). b | Contoured CT scan after 16 fractions of radiotherapy. c | Reconstructed 3D dose after 16 fractions of radiotherapy. d | Calculated dose differences (expressed as a 3D Gamma Index) after 16 fractions of radiotherapy. e | Dose–volume histograms at fractions 1, 6, 11, 16, 21 and 26 (dashed lines) as well as pretreatment histograms (solid lines). Clear deviations are visible from the planned dose–volume histogram for the rectum and bladder.

Figure 3

Axial FDG–PET and CT images of two different patients with NSCLC. Tumour imaging biomarkers describing, for example, textural heterogeneity, FDG uptake and tumour size can be assessed noninvasively before, during and after radiotherapy and associated with treatment outcome. Abbreviations: FDG, ¹⁸F-fluorodeoxyglucose; NSCLC, non-small-cell lung cancer.

Figure 4

A published nomogram for local control in patients with cancer of the larynx treated with radiotherapy. Clinical and treatment variables are associated with local control status at follow-up durations of 2 and 5 years. The predictors are age of the patient (in years), haemoglobin level (in mmol/l), clinical tumour stage (T-stage), clinical nodal stage (N-stage), patient’s sex and equivalent dose (in Gy). A probability for local control can be calculated by drawing a vertical line from each predictor value to the score scale at the top—’points’. After manually summing up the scores, the ‘total points’ correspond to the probability of local control, which are estimated by drawing a vertical line from this value to the bottom scales to estimate local control.

Figure 5

Knowledge-driven health-care principles using a clinical decision-support system in conjunction with standard evidence and regulations to choose the optimal treatment. In learning from follow-up data, knowledge is fed back to improve the clinical decision-support system and adapt regulations.

Figure 6

A simplified schematic representation of systems biology applied to radiotherapy. a | On the basis of in-vitro, in-vivo and patient data, modules representing the three biological categories (gene expression, immunohistochemical data and mutation data) important for radiotherapy response can be created. b | For an individual patient, appropriate molecular data will be accumulated. c | Combining the individual patient data with the modules will provide knowledge on specific module alterations (such as a deletion [X], upregulation [red] or downregulation [blue]), which can be translated to information on relative radioresistance and the molecular ‘weak’ spots of the tumour. This information will subsequently indicate whether dose escalation is necessary and which targeted drug is most effective for the patient. Part b used with permission from the National Academy of Sciences © Duboisa, L. J. Proc. Natl Acad. Sci. USA 108, 14620–14625 (2011).