BackgroundAdaptive radiation therapy (ART) “flags,” such as change in external body contour or relative weight loss, are widely used to identify which head and neck cancer (HNC) patients may benefit from replanned treatment. Despite the popularity of ART, few published quantitative approaches verify the accuracy of replan candidate identification, especially with regards to the simple flagging approaches that are considered current standard of practice. We propose a quantitative evaluation framework, demonstrated through the assessment of a single institution's clinical ART flag: change in body contour exceeding 1.5 cm.MethodsGround truth replan criteria were established by surveying HNC radiation oncologists. Patient‐specific dose deviations were approximated by using weekly acquired CBCT images to deform copies of the CT simulation, yielding during treatment “synthetic CTs.” The original plan reapplied to the synthetic CTs estimated interfractional dose deposition and truth table analysis compared ground truth flagging with the clinical ART metric. This process was demonstrated by assessing flagged fractions for 15 HNC patients whose body contour changed by >1.5 cm at some point in their treatment.ResultsSurvey results indicated that geometric shifts of high‐dose volumes relative to image‐guided radiation therapy alignment of bony anatomy were of most interest to HNC physicians. This evaluation framework successfully identified a fundamental discrepancy between the “truth” criteria and the body contour flagging protocol selected to identify changes in central axis dose. The body contour flag had poor sensitivity to survey‐derived major violation criteria (0%–28%). The sensitivity of a random sample for comparable violation/flagging frequencies was 27%.ConclusionsThese results indicate that centers should establish ground truth replan criteria to assess current standard of practice ART protocols. In addition, more effective replan flags may be tested and identified according to the proposed framework. Such improvements in ART flagging may contribute to better clinical resource allocation and patient outcome.
PurposeTo determine which head and neck adaptive radiotherapy (ART) correction objectives are feasible and to derive efficient ART patient selection guidelines.MethodsWe considered various head and neck ART objectives including independent consideration of dose-sparing of the brainstem/spinal cord, parotid glands, and pharyngeal constrictor, as well as prediction of patient weight loss. Two-hundred head and neck cancer patients were used for model development and an additional 50 for model validation. Patient chart data, pre-treatment images, treatment plans, on-unit patient measurements, and combinations thereof were assessed as potential predictors of each objective. A stepwise approach identified combinations of predictors maximizing the Youden index of random forest (RF) models. A heuristic translated RF results into simple patient selection guidelines which were further refined to balance predictive capability and practical resource costs. Generalizability of the RF models and simplified guidelines to new data was tested using the validation set.ResultsTop performing RF models used various categories of predictors, however, final simplified patient selection guidelines only required pre-treatment information for ART predictions, indicating the potential for significant ART process streamlining. The simplified guidelines for each objective predicted which patients would experience increases in dose to: brainstem/spinal cord with sensitivity = 1.0, specificity = 0.66; parotid glands with sensitivity = 0.82, specificity = 0.70; and pharyngeal constrictor with sensitivity = 0.84, specificity = 0.68. Weight loss could be predicted with sensitivity = 0.60 and specificity = 0.55. Furthermore, depending on the ART objective, 28%-58% of patients required replan assessment, less than for previous studies, indicating a step towards more effective patient selection.ConclusionsThe above ART objectives appear to be practically achievable, with patients selected for ART according to simple clinical patient selection guidelines. Explicit ART guidelines are rare in the literature, and our guidelines may aid in balancing the potential clinical gains of ART with high associated resource costs, formalizing ART trials, and ensuring the reproducibility of clinical successes.
A very important issue in contemporary inverse treatment radiotherapy planning is the specification of proper dose-volume constraints limiting the treatment planning algorithm from delivering high doses to the normal tissue surrounding the tumor. Recently we have proposed a method called reverse mapping of normal tissue complication probabilities (NTCP) onto dose-volume histogram (DVH) space, which allows the calculation of appropriate biologically based dose-volume constraints to be used in the inverse treatment planning. The method of reverse mapping requires random sampling from the functional space of all monotonically decreasing functions in the unit square. We develop, in this paper, a random function generator for the purpose of the reverse mapping. Since the proposed generator is based on the theory of random walk, it is therefore designated in this work, as a random walk DVH generator. It is theoretically determined that the distribution of the number of monotonically decreasing functions passing through a point in the dose volume histogram space follows the hypergeometric distribution. The proposed random walk DVH generator thus simulates, in a random fashion, trajectories of monotonically decreasing functions (finite series) that are situated in the unit square [0, 1] X [1,0] using the hypergeometric distribution. The DVH generator is an important tool in the study of reverse NTCP mapping for the calculation of biologically based dose-volume constraints for inverse treatment planning.
Purpose. To investigate the capacity of two phenomenological expressions to describe the population tumor response in case of a heterogeneous irradiation of the tumor. The generalization of the individual tumor control probability (TCP) models to include the case of a heterogeneous irradiation is a trivial problem. However, an analytical solution that results in a closed form population TCP formula for the heterogeneous case is, unfortunately, a very complex mathematical problem. Therefore we applied a numerical approach to the problem. to describe the population tumor response in case of heterogeneous irradiation is investigated through their fitting to the psuedo-experimental data sets. Results and conclusions. While both expressions produce statistically acceptable fits to the pseudo-experimental data within 2% TCP error band, the use of the second expression is preferable since it produces considerably better fits to the data sets.almost half a century ago Munro and Gilbert [1] introduced the notion of tumor control probability (TCP) as the probability of zero surviving clonogenic tumor cells. The importance of the introduction of the TCP notion is well demonstrated by the immense number of papers, which provide theoretical model derivations, discussions, and clinical/animal experimental data analysis with the purpose of TCP model testing and model parameter estimation. although the main emphasis was initially placed on describing the response of a homogeneously irradiated tumor, the dose heterogeniety was included in the TCP model in the theoretical work of Fisher [2]. Years later, when accurate knowledge of the dose distribution was made possible by technological development, it was reintroduced by a number of authors [3][4][5]. The estimation 1294 P. Stavrev et al. of the TCP of an individual tumor in case of heterogeneous irradiation is easily done. The tumor is speculatively divided into subvolumes (tumorlets), each presumed to be homogeneously irradiated and the TCP of each tumorlet is calculated. The product of the TCPs of the tumorlets gives the TCP of the tumor in this case. This follows directly from the definition of probability of independent events [2]. Indeed, we assume that the irradiation of one tumorlet does not affect the neighboring ones.The next step in the development of TCP modeling was the inclusion of the inter-individual variation of the TCP model parameter values. It reflects the fact that clinical data on tumor response represent the response of a population of patients [5][6][7][8][9] which is the average of the responses of the individuals constituting the population [5,10,11], based on the distribution of the model parameter values among the population.Clinical data represent the response of a population of patients and therefore, a population TCP model should be applied to fit these data. This sugestion was made by Brenner [8] as well, after fitting an individual TCP model to clinical data and obtaining biologically unrealistic values of the model parameters. Webb ...
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