The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

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Purpose/Objective(s): Treatment choices in adaptive radiotherapy are often based on subjective experiences and heuristic rules that lack an effective strategy to dynamically optimize long-term clinical outcomes. Therefore, we are investigating reinforcement learning (RL) approaches that can provide a robust framework to interactively adapt treatment regimens to individual patient's characteristics over time. In this context, RL approaches would balance trade-offs between exploring varying dose fractionation options and exploiting knowledge synthesized from patient's time-dependent covariates (states) history to maximize accumulative reward (TCP) and minimize long-term regret (NTCP). Materials/Methods: We studied an institutional cohort of 145 hepatocellular carcinoma SBRT patients with 45 on non-adaptive and 100 on adaptive protocols. Adaptation was based on liver function, assessed with Indocyanine green retention, by delivering a split-course of 3 + 2 fractions with a month break in between. Median prescribed tumor dose was 40 Gy delivered in 3 or 5 fractions with doses converted into EQD2 using the LQL model. Plasma biomarkers were analyzed before and during treatment. NTCP was assessed as a 1-grade change in ALBI score. The RL radiotherapy environment was modeled as a 2-stage Markov decision process of baseline and one month into treatment states. States are represented by the patient's clinical, dosimetric, and biological covariates history. Two typical decision-making scenarios at stage-2 were considered for testing RL feasibility retrospectively: (1) choosing to adapt with a splitcourse or not, and (2) determining to deliver an additional 2 fractions after the initial course. The reward/regret was defined by the complication-free tumor control (P+ Z TCP Â (1-NTCP)). Q-learning with a simple regression model of state-action mapping was used for optimizing strategy selection by solving the corresponding Bellman equation. The performance was evaluated using an adjusted R-squared (aR 2 ) to correct for over-fitting pitfalls. Results: Using states of clinical and dosimetric (tumor size, tumor dose, mean liver dose) covariates, Q-learning at one month (stage-2) selected split-course adaptation as an optimal action with a model fit of aR 2 Z 0.65 (P < 0.001). Percentage change in TGF-b1 concentration was the only biological variable to correlate with outcomes (ALBI score, P Z 0.03); its addition improved the fit to aR 2 Z 0.74. In case of 3 versus 5 fractions determination, the delivery of 2 extra fractions was found to be a better action with an aR 2 Z 0.66 (P < 0.001). The inclusion of TGF-b1 improved the fit to aR 2 Z 0.74. Conclusion:Our results demonstrate that RL approaches provide a promising framework for sequential clinical decision making in adaptive radiotherapy. Moreover, biological metrics seem to improve the goodness of fit. We are exploring advanced Q-learning with nonlinear models that may accelerate clinical adoption of RL for optimal decision-selection in adaptive radiotherapy.

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