Assessment of Collimator Jaw Optimization in Reducing Normal Tissue Irradiation with Intensity Modulated Radiation Therapy

Purpose: To evaluate normal tissue dose reduction in step-and-shoot intensity-modulated radiation therapy (IMRT) on the Varian 2100 platform by tracking the multileaf collimator (MLC) apertures with the accelerator jaws. Methods: Clinical radiation treatment plans for 10 thoracic, 3 pediatric and 3 head and neck patients were converted to plans with the jaws tracking each segment’s MLC apertures. Each segment was then renormalized to account for the change in collimator scatter to obtain target coverage within 1% of that in the original plan. The new plans were compared to the original plans in a commercial radiation treatment planning system (TPS). Reduction in normal tissue dose was evaluated in the new plan by using the parameters V5, V10, and V20 in the cumulative dose-volume histogram for the following structures: total lung minus GTV (gross target volume), heart, esophagus, spinal cord, liver, parotids, and brainstem. In order to validate the accuracy of our beam model, MLC transmission measurements were made and compared to those predicted by the TPS. Results: The greatest change between the original plan and new plan occurred at lower dose levels. The reduction in V20 was never more than 6.3% and was typically less than 1% for all patients. The reduction in V5 was 16.7% maximum and was typically less than 3% for all patients. The variation in normal tissue dose reduction was not predictable, and we found no clear parameters that indicated which patients would benefit most from jaw tracking. Our TPS model of MLC transmission agreed with measurements with absolute transmission differences of less than 0.1 % and thus uncertainties in the model did not contribute significantly to the uncertainty in the dose determination. Conclusion: The amount of dose reduction achieved by collimating the jaws around each MLC aperture in step-and-shoot IMRT does not appear to be clinically significant.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Scanned Ion Beam Therapy for Thoracic Tumors

Although frequently cured of Hodgkin lymphoma, adolescents and young adults can develop radiation induced second cancers. These patients could potentially benefit from scanned ion radiotherapy yet likely would require motion mitigation strategies. In theory, four-dimensional (4D) optimization of ion beam fields for individual motion states of respiration can enable superior sparing of healthy tissue near moving targets, compared to other motion mitigation strategies. Furthermore, carbon-ion therapy can sometimes provide greater relative biological effectiveness (RBE) for cell sterilization in a target but nearly equivalent RBE in tissue upstream of the target, compared to proton therapy. Thus, we expected that for some patients with Hodgkin lymphoma, carbon-ion therapy would reduce the predicted risk of second cancer incidence in the breast compared with proton therapy. The purpose of this work was to determine whether 4D-optimized carbon-ion therapy would significantly reduce the predicted risk of radiation induced second cancers in the breast for female Hodgkin lymphoma patients while preserving tumor control compared with proton therapy. To achieve our goals, we first investigated whether 4D-optimized carbon beam tracking could reduce dose to volumes outside a moving target compared with 3D-optimized carbon beam tracking while preserving target dose coverage. To understand the reliability of scanned carbon beam tracking, we studied the robustness of dose distributions in thoracic targets to uncertainties in patient motion. Finally, we investigated whether using carbon-ion therapy instead of proton therapy would significantly reduce the predicted risk of second cancer in the breast for a sample of Hodgkin lymphoma patients. We found that 4D-optimized ion beam tracking therapy can reduce the maximum dose to critical structures near a moving target by as much as 53%, compared to 3D-optimized ion beam tracking therapy. We validated these findings experimentally using a scanned carbon ion synchrotron and a motion phantom. We found scanned carbon beam tracking to be sensitive to a number of motion uncertainties, most notably phase delays in tracking, systematic spatial errors, and interfractional motion changes. Our findings indicate that a lower risk of second cancer in the breast might be expected for some Hodgkin lymphoma patients using carbon-ion therapy instead of proton therapy. For our reference scenario, we found the ratio of risk to be 0.77 ± 0.35 for radiogenic breast cancer after carbon-ion therapy versus proton therapy. Our findings were dependent on the RBE values for tumor induction and the radiosensitivity of breast tissue, as well as the physical dose distribution.