Advanced technologies in the radiotherapy clinic: system fundamentals.

The radiotherapy treatment process is undergoing rapid development at every step from planning through delivery, and each step is increasingly automated and assisted by new imaging, positioning, contouring and treatment tools. Plan delivery and verification is now aided using an increasing range of image guidance technologies, and imaging at treatment now brings broad opportunities for dose guidance and adaptation for improving overall treatment quality. While these many tools bring exciting opportunities for exact, reliable and efficient targeting of radiation dose, a consistently high level of accuracy must be achieved at every step to achieve the desired results. This level of workmanship requires thorough understanding of the basic methods involved in each step, including the opportunities and limitations, by both the clinicians and the planning/delivery staff alike. These processes and their clinical implementation are discussed in depth throughout this volume. Here, we overview their integration and guiding background concepts, as well as a range of workday efficiencies for clinical practice.

Datamining approaches for modeling tumor control probability.

BACKGROUND: Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. MATERIAL AND METHODS: Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. RESULTS: Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). CONCLUSIONS: The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Obstacles and advances in intensity-modulated radiation therapy treatment planning.

In this paper, the current state of intensity-modulated radiation therapy (IMRT) treatment planning systems is reviewed, including some inefficiencies along with useful workarounds and potential advances. Common obstacles in IMRT treatment planning are discussed, including problems due to the lack of scatter tails in optimization dose calculations, unexpected hot spots appearing in uncontoured regions, and uncontrolled tradeoffs inherent in conventional systems. Workarounds that can be applied in current systems are reviewed, including the incorporation of an 'anchor zone' around the target volume (including a margin of separation), which typically induces adequate dose falloff around the target, and the use of pseudostructures to reduce conflicts among objective functions. We propose changing the planning problem statement so that different dosimetric or outcome goals are prioritized as part of the prescription ('prioritized prescription optimization'). Higher-priority goals are turned into constraints for iterations that consider lower-priority goals. This would control tradeoffs between dosimetric objectives. A plan review tool is proposed that specifically summarizes distances from a structure to hot or cold doses ('dose-distance plots'). An algorithm for including scatter in the optimization process is also discussed. Lastly, brief comments are made about the ongoing effort to use outcome models to rank or optimize treatment plans.

IMRT treatment planning based on prioritizing prescription goals.

Determining the 'best' optimization parameters in IMRT planning is typically a time-consuming trial-and-error process with no unambiguous termination point. Recently we and others proposed a goal-programming approach which better captures the desired prioritization of dosimetric goals. Here, individual prescription goals are addressed stepwise in their order of priority. In the first step, only the highest order goals are considered (target coverage and dose-limiting normal structures). In subsequent steps, the achievements of the previous steps are turned into hard constraints and lower priority goals are optimized, in turn, subject to higher priority constraints. So-called 'slip' factors were introduced to allow for slight, clinically acceptable violations of the constraints. Focusing on head and neck cases, we present several examples for this planning technique. The main advantages of the new optimization method are (i) its ability to generate plans that meet the clinical goals, as well as possible, without tuning any weighting factors or dose-volume constraints, and (ii) the ability to conveniently include more terms such as fluence map smoothness. Lower level goals can be optimized to the achievable limit without compromising higher order goals. The prioritized prescription-goal planning method allows for a more intuitive and human-time-efficient way of dealing with conflicting goals compared to the conventional trial-and-error method of varying weighting factors and dose-volume constraints.

Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters.

PURPOSE: To determine the clinical, dosimetric, and spatial parameters that correlate with radiation pneumonitis. METHODS AND MATERIALS: Patients treated with high-dose radiation for non-small-cell lung cancer with three-dimensional treatment planning were reviewed for clinical information and radiation pneumonitis (RP) events. Three-dimensional treatment plans for 219 eligible patients were recovered. Treatment plan information, including parameters defining tumor position and dose-volume parameters, was extracted from non-heterogeneity-corrected dose distributions. Correlation to RP events was assessed by Spearman's rank correlation coefficient (R). Mathematical models were generated that correlate with RP. RESULTS: Of 219 patients, 52 required treatment for RP (median interval, 142 days). Tumor location was the most highly correlated parameter on univariate analysis (R = 0.24). Multiple dose-volume parameters were correlated with RP. Models most frequently selected by bootstrap resampling included tumor position, maximum dose, and D35 (minimum dose to the 35% volume receiving the highest doses) (R = 0.28). The most frequently selected two- or three-parameter models outperformed commonly used metrics, including V20 (fractional volume of normal lung receiving >20 Gy) and mean lung dose (R = 0.18). CONCLUSIONS: Inferior tumor position was highly correlated with pneumonitis events within our population. Models that account for inferior tumor position and dosimetric information, including both high- and low-dose regions (D(35), International Commission on Radiation Units and Measurements maximum dose), risk-stratify patients more accurately than any single dosimetric or clinical parameter.