The purpose of this work was to compare the risk of developing a second cancer after craniospinal irradiation using photon versus proton radiotherapy by means of simulation studies designed to account for the effects of neutron exposures. Craniospinal irradiation of a male phantom was calculated for passively-scattered and scanned-beam proton treatment units. Organ doses were estimated from treatment plans; for the proton treatments, the amount of stray radiation was calculated separately using the Monte Carlo method. The organ doses were converted to risk of cancer incidence using a standard formalism developed for radiation protection purposes. The total lifetime risk of second cancer due exclusively to stray radiation was 1.5% for the passively scattered treatment versus 0.8% for the scanned proton beam treatment. Taking into account the therapeutic and stray radiation fields, the risk of second cancer from intensity-modulated radiation therapy and conventional radiotherapy photon treatments were 7 and 12 times higher than the risk associated with scanned-beam proton therapy, respectively, and 6 and 11 times higher than with passively scattered proton therapy, respectively. Simulations revealed that both passively scattered and scanned-beam proton therapies confer significantly lower risks of second cancers than 6MV conventional and intensity-modulated photon therapies.

Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rank correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features' interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4-13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturerprotocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6-8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from ...

Purpose To assess the risk of a secondary malignant neoplasm (SMN) from proton therapy relative to intensity-modulated radiation therapy (IMRT) using X-rays, taking into account contributions from both primary and secondary sources of radiation, for prostate cancer. Methods and Materials A proton therapy plan and a 6-MV IMRT plan were constructed for 3 patients with early-stage adenocarcinoma of the prostate. Doses from the primary fields delivered to organs at risk of developing an SMN were determined from treatment plans. Secondary doses from the proton therapy and IMRT were determined from Monte Carlo simulations and available measured data, respectively. The risk of an SMN was estimated from primary and secondary doses on an organ-by-organ basis by use of risk models from the Committee on the Biological Effects of Ionizing Radiation. Results Proton therapy reduced the risk of an SMN by 26% to 39% compared with IMRT. The risk of an SMN for both modalities was greatest in the in-field organs. However, the risks from the in-field organs were considerably lower with the proton therapy plan than with the IMRT plan. This reduction was attributed to the substantial sparing of the rectum and bladder from exposure to the therapeutic beam by the proton therapy plan. Conclusions When considering exposure to primary and secondary radiation, proton therapy can reduce the risk of an SMN in prostate patients compared with contemporary IMRT.

Stray radiation exposures are of concern for patients receiving proton radiotherapy and vary strongly with several treatment factors. The purposes of this study were to conservatively estimate neutron exposures for a contemporary passive scattering proton therapy system and to understand how they vary with treatment factors. We studied the neutron dose equivalent per therapeutic absorbed dose (H/D) as a function of treatment factors including proton energy, location in the treatment room, treatment field size, spread-out Bragg peak (SOBP) width and snout position using both Monte Carlo simulations and analytical modeling. The H/D value at the isocenter for a 250 MeV medium field size option was estimated to be 20 mSv Gy(-1). H/D values generally increased with the energy or penetration range, fell off sharply with distance from the treatment unit, decreased modestly with the aperture size, increased with the SOBP width and decreased with the snout distance from the isocenter. The H/D values from Monte Carlo simulations agreed well with experimental results from the literature. The analytical model predicted H/D values within 28% of those obtained in simulations; this value is within typical neutron measurement uncertainties.

Contemporary treatment planning systems for proton radiotherapy typically use analytical pencil-beam algorithms - which require a comprehensive set of configuration data - to predict the absorbed dose distributions in the patient. In order to reduce the time required to prepare a new proton treatment planning system for clinical use, it was desirable to configure the planning system before measured beam data were available. However, it was not known if the Monte Carlo simulation method was a practical alternative to measuring beam profiles. The purpose of this study was to develop a model of a passively scattered proton therapy unit, to simulate the properties of the proton fields using the Monte Carlo technique and to configure an analytical treatment planning system using the simulated beam data. Additional simulations and treatment plans were calculated in order to validate the pencil-beam predictions against the Monte Carlo simulations using realistic treatment beams. Comparison of dose distributions in a water phantom revealed small dose difference and distances to agreement under the validation conditions. The total simulation time for generating the 768 beam configuration profiles was approximately 6 weeks using 30 nodes in a parallel processing cluster. The results of this study show that it is possible to configure and test a proton treatment planning system prior to the availability of measured proton beam data. The model presented here provided a means to reduce by several months the time required to prepare an analytical treatment planning system for patient treatments.