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Mathematical model simulation of low delta tumor growth (lambda = 0.2)

Purpose:Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology.Experimental Design:We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology.Results:In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth.Conclusions:At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.

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Mathematical model simulation of high delta tumor growth (lambda = 1.5)

Purpose Deformable image registration (DIR) of longitudinal liver cancer computed tomographic (CT) images can be challenging owing to anatomic changes caused by radiation therapy (RT) or disease progression. We propose a workflow for the DIR of longitudinal contrast-enhanced CT scans of liver cancer based on a biomechanical model of the liver driven by boundary conditions on the liver surface and centerline of an autosegmentation of the vasculature. Methods and Materials Pre- and post-RT CT scans acquired with a median gap of 112 (32-217) days for 28 patients who underwent RT for intrahepatic cholangiocarcinoma were retrospectively analyzed. For each patient, 5 corresponding anatomic landmarks in pre- and post-RT scans were identified in the liver by a clinical expert for evaluation of the accuracy of different DIR strategies. The first strategy corresponded to the use of a biomechanical model-based DIR method with boundary conditions specified on the liver surface (BM_DIR). The second strategy corresponded to the use of an expansion of BM_DIR consisting of the auto-segmentation of the liver vasculature to determine additional boundary conditions in the biomechanical model (BM_DIR_VBC). The 2 strategies were also compared with an intensity-based DIR strategy using a Demons algorithms. Results The group mean target registration errors were 12.4 ± 7.5, 7.7 ± 3.7 and 4.4 ± 2.5 mm, for the Demons, BM_DIR and BM_DIR_VBC, respectively. Conclusions In regard to the large and complex deformation observed in this study and the achieved accuracy of 4.4 mm, the proposed BM_DIR_VBC method might reveal itself as a valuable tool in future studies on the relationship between delivered dose and treatment outcome.

PURPOSE: Currently, radiologists use tumor-to-normal tissue contrast across multiphase computed tomography (MPCT) for lesion detection. Here, we developed a novel voxel-based enhancement pattern mapping (EPM) technique and investigated its ability to improve contrast-to-noise ratios (CNRs) in a phantom study and in patients with hepatobiliary cancers. METHODS: The EPM algorithm is based on the root mean square deviation between each voxel and a normal liver enhancement model using patient-specific (EPM-PA) or population data (EPM-PO). We created a phantom consisting of liver tissue and tumors with distinct enhancement signals under varying tumor sizes, motion, and noise. We also retrospectively evaluated 89 patients with hepatobiliary cancers who underwent active breath-hold MPCT between 2016 and 2017. MPCT phases were registered using a three-dimensional deformable image registration algorithm. For the patient study, CNRs of tumor to adjacent tissue across MPCT phases, EPM-PA and EPM-PO were measured and compared. RESULTS: EPM resulted in statistically significant CNR improvement (P < 0.05) for tumor sizes down to 3 mm, but the CNR improvement was significantly affected by tumor motion and image noise. Eighty-two of 89 hepatobiliary cases showed CNR improvement with EPM (PA or PO) over grayscale MPCT, by an average factor of 1.4, 1.6, and 1.5 for cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis, respectively (P < 0.05 for all). CONCLUSIONS: EPM increases CNR compared with grayscale MPCT for primary and secondary hepatobiliary cancers. This new visualization method derived from MPCT datasets may have applications for early cancer detection, radiomic characterization, tumor treatment response, and segmentation. IMPLICATIONS FOR PATIENT CARE: We developed a voxel-wise enhancement pattern mapping (EPM) technique to improve the contrast-to-noise ratio (CNR) of multiphase CT. The improvement in CNR was observed in datasets of patients with cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis. EPM has the potential to be clinically useful for cancers with regard to early detection, radiomic characterization, response, and segmentation.

Purpose To evaluate the effect of setup uncertainties including uncertainties between different breath holds (BH) and inter-fractional anatomical changes under CT-guided BH with intensity-modulated proton therapy (IMPT) in patients with liver cancer. Methods and materials This retrospective study considered 17 patients with liver tumors who underwent feedback-guided BH (FGBH) IMRT treatment with daily CT-on-rail imaging. Planning CT images were acquired at simulation using FGBH, and FGBH CT-on-rail images were also acquired prior to each treatment. Selective robust IMPT plans were generated using planning CT and re-calculated on each daily CT-on-rail image. Subsequently, the fractional doses were deformed and accumulated onto the planning CT according to the deformable image registration between daily and planning CTs. The doses to the target and organs at risk (OARs) were compared between IMRT, planned IMPT, and accumulated IMPT doses. Results For IMPT plans, the mean of D98% of CTV for all 17 patients was slightly reduced from the planned dose of 68.90 ± 1.61 Gy to 66.48 ± 1.67 Gy for the accumulated dose. The target coverage could be further improved by adjusting planning techniques. The dose–volume histograms of both planned and accumulated IMPT doses showed better sparing of OARs than that of the IMRT. Conclusions IMPT with FGBH and CT-on-rail guidance is a robust treatment approach for liver tumor cases.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology. Experimental Design: We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology. Results: In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth. Conclusions: At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.

Advances in technology have enabled the delivery of high doses of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) with low rates of toxicity. Although the role of radiation for pancreatic cancer continues to evolve, encouraging results with newer techniques indicate that radiation may benefit selected patient populations. Imaging has been central to the modern successes of radiation therapy for PDAC. Here, we review the role of diagnostic imaging, imaging-based planning, and image guidance in radiation oncology practice for PDAC.

Purpose: To review acute and late toxicities after chemoradiation for locally advanced pancreatic ductal adenocarcinoma in patients who were treated with escalated dose radiation (EDR). Methods and materials: Maximum Common Terminology Criteria for Adverse Events Version 4.0 acute toxicities (AT) during radiation and within 60 days after radiation were recorded for both acute gastrointestinal toxicity and overall toxicity (OT). Late toxicities were also recorded. EDR was generally delivered with daily image guidance and breath-hold techniques using intensity modulated radiation therapy (IMRT) planning. These were compared with patients who received standard dose radiation (SDR) delivered as 50.4 Gy in 28 fractions using 3-dimensional chemoradiation therapy planning. Results: A total of 59 of 154 patients (39%) received EDR with biologically equivalent doses >70 Gy. The most frequent schedules were 63 Gy in 28 fractions (19 of 154 patients), 67.5 Gy in 15 fractions (10 of 154 patients), and 70 Gy in 28 fractions (15 of 154 patients). No grade 4 or grade 5 OT or late toxicities were reported. Rates of grade 3 acute gastrointestinal toxicity were significantly lower in patients who received EDR compared with SDR (1% vs 14%; P < .001). Similarly, rates of grade 3 OT were also lower for EDR compared with SDR (4% vs 16%; P = .004). The proportion of patients who experienced no AT was higher in the EDR group than the SDR group (36% vs 15%; P = .001). For EDR patients treated with IMRT, a lower risk of AT was associated with a later treatment year (P = .007), nonpancreatic head tumor location (P = .01), breath-hold (P = .002), 4-dimensional computed tomography (P = .003), computed tomography on rails (P = .002), and lower stomach V40 (P = .03). With a median time of 12 months (range, 1-79 months) from the start of radiation therapy to the last known follow-up in the EDR group, 51 of 59 patients (86%) had no late toxicity. Six of 59 EDR patients (10%) had either strictures or gastrointestinal bleeding that required intervention. No significant predictors of late toxicity were identified. Conclusion: Overall acute and late toxicity rates were low with EDR using an IMRT technique with image guidance and respiratory gating.

Purpose: Proton therapy is increasingly used to treat pediatric brain tumors. However, the response of both tumors and healthy tissues to proton therapy is currently under investigation. One way of assessing this response is magnetic resonance (MR) diffusion tensor imaging (DTI), which can measure molecular mobility at the cellular level, quantified by the apparent diffusion coefficient (ADC). In addition, DTI may reveal axonal fiber directional information in white matter, quantified by fractional anisotropy (FA). Here we report use of DTI to assess tumor and unexposed healthy brain tissue responses in a child who received proton therapy for juvenile pilocytic astrocytoma. Materials and Methods: A 10-year-old boy with recurrent juvenile pilocytic astrocytoma of the left thalamus received proton therapy to a dose of 50.4Gy (RBE) in 28 fractions. Functional magnetic resonance imaging was used to select beam angles for treatment planning. Over the course of the 7-year follow-up period, magnetic resonance imaging including DTI was done to assess response. The MR images were registered to the treatment-planning computed tomography scan, and the gross tumor volume (GTV) was mapped onto the MR images at each follow-up. The GTV contour was then mirrored to the right side of brain through the midline to represent unexposed healthy brain tissue. Results: Proton therapy delivered the full prescribed dose to the target while completely sparing the contralateral brain. The MR ADC images obtained before and after proton therapy showed that enhancement corresponding to the GTV had nearly disappeared by 25 months. The ADC and FA measurements confirmed that contralateral healthy brain tissue was not affected, and the GTV reverted to clinically normal ADC and FA values. Conclusion: Use of DTI allowed quantitative evaluation of tumor and healthy brain tissue responses to proton therapy.

BACKGROUND AND PURPOSE: This study documents the utilization and efficacy of proton beam therapy (PBT) in western patients with localized unresectable hepatocellular carcinoma (HCC). METHODS AND METHODS: Forty-six patients with HCC, Child-Pugh class of A or B, no prior radiotherapy history, and ECOG performance status 0-2 received PBT at our institution from 2007 to 2016. Radiographic control within the PBT field (local control, LC) and overall survival (OS) were calculated from the start of PBT. RESULTS: Most (83%) patients had Child-Pugh class A. Median tumor size was 6 cm (range, 1.5-21.0 cm); 22% of patients had multiple tumors and 28% had tumor vascular thrombosis. Twenty-five (54%) patients received prior treatment. Median biologically effective dose (BED) was 97.7 GyE (range, 33.6-144 GyE) administered in 15 fractions. Actuarial 2-year LC and OS rates were 81% and 62%; median OS was 30.7 months. Out-of-field intrahepatic failure was the most common site of disease progression. Patients receiving BED ≥90 GyE had a significantly better OS than those receiving BED

The clinical advantage of intensity modulated proton therapy (IMPT) may be diminished by range and patient setup uncertainties. We evaluated the effectiveness of robust optimization that incorporates uncertainties into the treatment planning optimization algorithm for treatment of base of skull cancers.We compared 2 IMPT planning methods for 10 patients with base of skull chordomas and chondrosarcomas: (1) conventional optimization, in which uncertainties are dealt with by creating a planning target volume (PTV); and (2) robust optimization, in which uncertainties are dealt with by optimizing individual spot weights without a PTV. We calculated root-mean-square deviation doses (RMSDs) for every voxel to generate RMSD volume histograms (RVHs). The area under the RVH curve was used for relative comparison of the 2 methods' plan robustness. Potential benefits of robust planning, in terms of target dose coverage and homogeneity and sparing of organs at risk (OARs) were evaluated using established clinical metrics. Then the plan evaluation metrics were averaged and compared with 2-sided paired t tests. The impact of tumor volume on the effectiveness of robust optimization was also analyzed.Relative to conventionally optimized plans, robustly optimized plans were less sensitive for both targets and OARs. In the nominal scenario, robust and conventional optimization resulted in similar D95% doses (D95% clinical target volume [CTV]: 63.3 and 64.8 Gy relative biologic effectiveness [RBE]), P.01]) and D5%-D95% (D5%-D95% CTV: 8.0 and 7.1 Gy[RBE], [P.01); irradiation of OARs was less with robust optimization (brainstem V60: 0.076 vs 0.26 cm(3) [P.01], left temporal lobe V70: 0.22 vs 0.41 cm(3), [P = .068], right temporal lobe V70: 0.016 vs 0.11 cm(3), [P = .096], left cochlea Dmean: 28.1 vs 30.1 Gy[RBE], [P = .023], right cochlea Dmean: 23.7 vs 25.2 Gy[RBE], [P = .059]). Results in the worst-case scenario were analogous.Robust optimization is effective for creating clinically feasible IMPT plans for tumors of the base of skull.

Purpose To quantify and compare the effects of respiratory motion on paired passively scattered proton therapy (PSPT) and intensity modulated photon therapy (IMRT) plans; and to establish the relationship between the magnitude of tumor motion and the respiratory-induced dose difference for both modalities. Methods and Materials In a randomized clinical trial comparing PSPT and IMRT, radiation therapy plans have been designed according to common planning protocols. Four-dimensional (4D) dose was computed for PSPT and IMRT plans for a patient cohort with respiratory motion ranging from 3 to 17 mm. Image registration and dose accumulation were performed using grayscale-based deformable image registration algorithms. The dose–volume histogram (DVH) differences (4D-3D [3D = 3-dimensional]) were compared for PSPT and IMRT. Changes in 4D-3D dose were correlated to the magnitude of tumor respiratory motion. Results The average 4D-3D dose to 95% of the internal target volume was close to zero, with 19 of 20 patients within 1% of prescribed dose for both modalities. The mean 4D-3D between the 2 modalities was not statistically significant ( P Conclusions With our current margin formalisms, target coverage was maintained in the presence of respiratory motion up to 17 mm for both PSPT and IMRT. Only 2 of 11 4D-3D indices (lung V5 and spinal cord maximum) were statistically distinguishable between PSPT and IMRT, contrary to the notion that proton therapy will be more susceptible to respiratory motion. Because of the lack of strong correlations with 4D-3D dose differences in PSPT and IMRT, the extent of tumor motion was not an adequate predictor of potential dosimetric error caused by breathing motion.

Purpose To evaluate a method for quantifying the effect of setup errors and range uncertainties on dose distribution and dose–volume histogram using statistical parameters; and to assess existing planning practice in selected treatment sites under setup and range uncertainties. Methods and Materials Twenty passively scattered proton lung cancer plans, 10 prostate, and 1 brain cancer scanning-beam proton plan(s) were analyzed. To account for the dose under uncertainties, we performed a comprehensive simulation in which the dose was recalculated 600 times per given plan under the influence of random and systematic setup errors and proton range errors. On the basis of simulation results, we determined the probability of dose variations and calculated the expected values and standard deviations of dose–volume histograms. The uncertainties in dose were spatially visualized on the planning CT as a probability map of failure to target coverage or overdose of critical structures. Results The expected value of target coverage under the uncertainties was consistently lower than that of the nominal value determined from the clinical target volume coverage without setup error or range uncertainty, with a mean difference of −1.1% (−0.9% for breath-hold), −0.3%, and −2.2% for lung, prostate, and a brain cases, respectively. The organs with most sensitive dose under uncertainties were esophagus and spinal cord for lung, rectum for prostate, and brain stem for brain cancer. Conclusions A clinically feasible robustness plan analysis tool based on direct dose calculation and statistical simulation has been developed. Both the expectation value and standard deviation are useful to evaluate the impact of uncertainties. The existing proton beam planning method used in this institution seems to be adequate in terms of target coverage. However, structures that are small in volume or located near the target area showed greater sensitivity to uncertainties.

The purpose of this study was to analyze factors affecting proton stopping-power-ratio (SPR) estimations and range uncertainties in proton therapy planning using the standard stoichiometric calibration. The SPR uncertainties were grouped into five categories according to their origins and then estimated based on previously published reports or measurements. For the first time, the impact of tissue composition variations on SPR estimation was assessed and the uncertainty estimates of each category were determined for low-density (lung), soft, and high-density (bone) tissues. A composite, 95th percentile water-equivalent-thickness uncertainty was calculated from multiple beam directions in 15 patients with various types of cancer undergoing proton therapy. The SPR uncertainties (1σ) were quite different (ranging from 1.6% to 5.0%) in different tissue groups, although the final combined uncertainty (95th percentile) for different treatment sites was fairly consistent at 3.0-3.4%, primarily because soft tissue is the dominant tissue type in the human body. The dominant contributing factor for uncertainties in soft tissues was the degeneracy of Hounsfield numbers in the presence of tissue composition variations. To reduce the overall uncertainties in SPR estimation, the use of dual-energy computed tomography is suggested. The values recommended in this study based on typical treatment sites and a small group of patients roughly agree with the commonly referenced value (3.5%) used for margin design. By using tissue-specific range uncertainties, one could estimate the beam-specific range margin by accounting for different types and amounts of tissues along a beam, which may allow for customization of range uncertainty for each beam direction.

For robust plan optimization and evaluation purposes, one needs a computationally efficient way to calculate dose distributions and dose-volume histograms (DVHs) under various changes in the variables associated with beam delivery and images. In this study, we report an approximate method for rapid calculation of dose when setup errors and anatomical changes occur during proton therapy. This fast dose approximation method calculates new dose distributions under various circumstances based on the prior knowledge of dose distribution from a reference setting. In order to validate the method, we calculated and compared the dose distributions from our approximation method to the dose distributions calculated from a clinically commissioned treatment planning system (TPS) which was used as the ground truth. The overall accuracy of the proposed method was tested against varying degrees of setup error and anatomical deformation for selected patient cases. The setup error was simulated by rigid shifts of the patient; while the anatomical deformation was introduced using weekly acquired repeat CT data sets. We evaluated the agreement between the dose approximation method and full dose recalculation using a 3D gamma index and the root-mean-square (RMS) and maximum deviation of the cumulative dose volume histograms (cDVHs). The average passing rate of 3D gamma analysis under 3% dose and 3mm distance-to-agreement criteria were 96% and 89% for setup errors and severe anatomy changes, respectively. The average of RMS and maximum deviation of the cDVHs under the setup error was 0.5% and 1.5%, respectively for all structures considered. Similarly, the average of RMS and maximum deviations under the weekly anatomical change were 0.6% and 2.7%, respectively. Our results show that the fast dose approximation method was able to account for the density variation of the patient due to the setup and anatomical changes with acceptable accuracy while significantly improving the computation time.

Respiratory motion is traditionally assessed using tumor motion magnitude. In proton therapy, respiratory motion causes density variations along the beam path that result in uncertainties of proton range. This work has investigated the use of water‐equivalent thickness (WET) to quantitatively assess the effects of respiratory motion on calculated dose in passively scattered proton therapy (PSPT). A cohort of 29 locally advanced non‐small cell lung cancer patients treated with 87 PSPT treatment fields were selected for analysis. The variation in WET (ΔWET) along each field was calculated between exhale and inhale phases of the simulation four‐dimensional computed tomography. The change in calculated dose (ΔDose) between full‐inhale and full‐exhale phase was quantified for each field using dose differences, 3D gamma analysis, and differential area under the curve (ΔAUC) analysis. Pearson correlation coefficients were calculated between ΔDose and ΔWET. Three PSPT plans were redesigned using field angles to minimize variations in ΔWET and compared to the original plans. The median ΔWET over 87 treatment fields ranged from 1‐9 mm, while the ΔWET 95th percentile value ranged up to 42 mm. The ΔWET was significantly correlated (p

Purpose Computed tomography (CT) artifacts can severely degrade dose calculation accuracy in proton therapy. Prompted by the recently increased popularity of magnetic resonance imaging (MRI) in the radiation therapy clinic, we developed an MRI-based CT artifact correction method for improving the accuracy of proton range calculations. Methods and materials The proposed method replaces corrupted CT data by mapping CT Hounsfield units (HU number) from a nearby artifact-free slice, using a coregistered MRI. MRI and CT volumetric images were registered with use of 3-dimensional (3D) deformable image registration (DIR). The registration was fine-tuned on a slice-by-slice basis by using 2D DIR. Based on the intensity of paired MRI pixel values and HU from an artifact-free slice, we performed a comprehensive analysis to predict the correct HU for the corrupted region. For a proof-of-concept validation, metal artifacts were simulated on a reference data set. Proton range was calculated using reference, artifactual, and corrected images to quantify the reduction in proton range error. The correction method was applied to 4 unique clinical cases. Results The correction method resulted in substantial artifact reduction, both quantitatively and qualitatively. On respective simulated brain and head and neck CT images, the mean error was reduced from 495 and 370 HU to 108 and 92 HU after correction. Correspondingly, the absolute mean proton range errors of 2.4 cm and 1.7 cm were reduced to less than 2 mm in both cases. Conclusions Our MRI-based CT artifact correction method can improve CT image quality and proton range calculation accuracy for patients with severe CT artifacts.

Robust optimization for IMPT takes setup and range uncertainties into account during plan optimization. However, anatomical changes were not prospectively included. The purpose of this study was to examine robustness and dose variation due to setup uncertainty and anatomical change in IMPT of lung cancer.Plans were generated with multi-field optimization based on planning target volume (MFO-PTV) and worst-case robust optimization (MFO-RO) on simulation computed tomography scans (CT0) for nine patients. Robustness was evaluated on the CT0 by computing the standard deviation of DVH (SD-DVH). Dose variations calculated on weekly CTs were compared with SD-DVH. Equivalent uniform dose (EUD) change from the original plan on weekly dose was also calculated for both plans.SD-DVH and dose variation on weekly CTs were both significantly lower in the MFO-RO plans than in the MFO-PTV plans for targets, lungs, and the esophagus (p0.05). When comparing EUD for ITV between weekly and planned dose distributions, three patients and 28% of repeated CTs for MFO-RO plans, and six patients and 44% of repeated CTs for MFO-PTV plans, respectively, showed an EUD change of5%.RO in IMPT reduces the dose variation due to setup uncertainty and anatomy changes during treatment compared with PTV-based planning. However, dose variation could still be substantial; repeated imaging and adaptive planning as needed are highly recommended for IMPT of lung tumors.

Purpose We compared conventionally optimized intensity modulated proton therapy (IMPT) treatment plans against worst-case scenario optimized treatment plans for lung cancer. The comparison of the 2 IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient setup, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods and materials For each of the 9 lung cancer cases, 2 treatment plans were created that accounted for treatment uncertainties in 2 different ways. The first used the conventional method: delivery of prescribed dose to the planning target volume that is geometrically expanded from the internal target volume (ITV). The second used a worst-case scenario optimization scheme that addressed setup and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of changes in patient anatomy attributable to respiratory motion were investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the 2 groups were compared with 2-sided paired Student t tests. Results Without respiratory motion considered, we affirmed that worst-case scenario optimization is superior to planning target volume–based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, worst-case scenario optimization still achieved more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality (D 95% ITV, 96.6% vs 96.1% [ P = .26]; D 5% − D 95% ITV, 10.0% vs 12.3% [ P = .082]; D 1% spinal cord, 31.8% vs 36.5% [ P = .035]). Conclusions Worst-case scenario optimization led to superior solutions for lung IMPT. Despite the fact that worst-case scenario optimization did not explicitly account for respiratory motion, it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.

Intensity-modulated proton therapy (IMPT) is highly sensitive to uncertainties in beam range and patient setup. Conventionally, these uncertainties are dealt using geometrically expanded planning target volume (PTV). In this paper, the authors evaluated a robust optimization method that deals with the uncertainties directly during the spot weight optimization to ensure clinical target volume (CTV) coverage without using PTV. The authors compared the two methods for a population of head and neck (HN) cancer patients.Two sets of IMPT plans were generated for 14 HN cases, one being PTV-based conventionally optimized and the other CTV-based robustly optimized. For the PTV-based conventionally optimized plans, the uncertainties are accounted for by expanding CTV to PTV via margins and delivering the prescribed dose to PTV. For the CTV-based robustly optimized plans, spot weight optimization was guided to reduce the discrepancy in doses under extreme setup and range uncertainties directly, while delivering the prescribed dose to CTV rather than PTV. For each of these plans, the authors calculated dose distributions under various uncertainty settings. The root-mean-square dose (RMSD) for each voxel was computed and the area under the RMSD-volume histogram curves (AUC) was used to relatively compare plan robustness. Data derived from the dose volume histogram in the worst-case and nominal doses were used to evaluate the plan optimality. Then the plan evaluation metrics were averaged over the 14 cases and were compared with two-sided paired t tests.CTV-based robust optimization led to more robust (i.e., smaller AUCs) plans for both targets and organs. Under the worst-case scenario and the nominal scenario, CTV-based robustly optimized plans showed better target coverage (i.e., greater D95%), improved dose homogeneity (i.e., smaller D5% - D95%), and lower or equivalent dose to organs at risk.CTV-based robust optimization provided significantly more robust dose distributions to targets and organs than PTV-based conventional optimization in HN using IMPT. Eliminating the use of PTV and planning directly based on CTV provided better or equivalent normal tissue sparing.

Proton dose distributions can potentially be altered by anatomical changes in the beam path despite perfect target alignment using traditional image guidance methods. In this simulation study, the authors explored the use of dosimetric factors instead of only anatomy to set up patients for proton therapy using in-room volumetric computed tomographic (CT) images.To simulate patient anatomy in a free-breathing treatment condition, weekly time-averaged four-dimensional CT data near the end of treatment for 15 lung cancer patients were used in this study for a dose-based isocenter shift method to correct dosimetric deviations without replanning. The isocenter shift was obtained using the traditional anatomy-based image guidance method as the starting position. Subsequent isocenter shifts were established based on dosimetric criteria using a fast dose approximation method. For each isocenter shift, doses were calculated every 2 mm up to ± 8 mm in each direction. The optimal dose alignment was obtained by imposing a target coverage constraint that at least 99% of the target would receive at least 95% of the prescribed dose and by minimizing the mean dose to the ipsilateral lung.The authors found that 7 of 15 plans did not meet the target coverage constraint when using only the anatomy-based alignment. After the authors applied dose-based alignment, all met the target coverage constraint. For all but one case in which the target dose was met using both anatomy-based and dose-based alignment, the latter method was able to improve normal tissue sparing.The authors demonstrated that a dose-based adjustment to the isocenter can improve target coverage and/or reduce dose to nearby normal tissue.

Purpose To report a method for explicitly designing a planning target volume (PTV) for treatment planning and evaluation in heterogeneous media for passively scattered proton therapy and scanning beam proton therapy using single-field optimization (SFO). Methods and Materials A beam-specific PTV (bsPTV) for proton beams was derived by ray-tracing and shifting ray lines to account for tissue misalignment in the presence of setup error or organ motion. Range uncertainties resulting from inaccuracies in computed tomography–based range estimation were calculated for proximal and distal surfaces of the target in the beam direction. The bsPTV was then constructed based on local heterogeneity. The bsPTV thus can be used directly as a planning target as if it were in photon therapy. To test the robustness of the bsPTV, we generated a single-field proton plan in a virtual phantom. Intentional setup and range errors were introduced. Dose coverage to the clinical target volume (CTV) under various simulation conditions was compared between plans designed based on the bsPTV and a conventional PTV. Results The simulated treatment using the bsPTV design performed significantly better than the plan using the conventional PTV in maintaining dose coverage to the CTV. With conventional PTV plans, the minimum coverage to the CTV dropped from 99% to 67% in the presence of setup error, internal motion, and range uncertainty. However, plans using the bsPTV showed minimal drop of target coverage from 99% to 94%. Conclusions The conventional geometry-based PTV concept used in photon therapy does not work well for proton therapy. We investigated and validated a beam-specific PTV method for designing and evaluating proton plans.

Purpose: Diffusion tensor imaging (DTI) can measure molecular mobility at the cellular level, quantified by the apparent diffusion coefficient (ADC). DTI may also reveal axonal fiber directional information in the white matter, quantified by the fractional anisotropy (FA). Juvenile pilocytic astrocytoma (JPA) is a rare brain tumor that occurs in children and young adults. Proton therapy (PT) is increasingly used in the treatment of pediatric brain tumors including JPA. However, the response of both tumors and normal tissues to PT is currently under investigation. We report tumor and normal brain tissue responses for a pediatric case of JPA treated with PT assessed using DTI. Methods: A ten year old male with JPA of the left thalamus received passive scattered PT to a dose of 50.4 Gy (RBE) in 28 fractions. Post PT, the patient has been followed up in seven years. At each follow up, MRI imaging including DTI was performed to assess response. MR images were registered to the treatment planning CT and the GTV mapped onto each MRI. The GTV contour was then mirrored to the right side of brain through the patient’s middle line to represent normal brain tissue. ADC and FA were measured within the ROIs. Results: Proton therapy can completely spare contra lateral brain while the target volume received full prescribed dose. From a series of MRI ADC images before and after PT at different follow ups, the enhancement corresponding to GTV had nearly disappeared more than 2 years after PT. Both ADC and FA demonstrate that contralateral normal brain tissue were not affect by PT and the tumor volume reverted to normal ADC and FA values. Conclusion: DTI allowed quantitative evaluation of tumor and normal brain tissue responses to PT. Further study in a larger cohort is warranted.

Purpose: We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case robustly optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods: For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional Method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from the internal target volume (ITV). The second employed the worst-case robust optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests. Results: Without respiratory motion considered, we affirmed that worst-case robust optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, robust optimization still leads to more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% - D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)]. Conclusion: Worst-case robust optimization led to superior solutions for lung IMPT. Despite of the fact that robust optimization did not explicitly account for respiratory motion it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.

Purpose: Acoustic neuroma is treated using highly conformal, framed-based radiosurgery. The gross target volume (GTV) is located near the radiosensitive brainstem. Currently, plan evaluation methods that consider GTV dose conformity and dose to organs at risk (OAR) do not account for setup uncertainty, thereby potentially giving a false sense of target coverage and OAR sparing. More realistic plan evaluations should consider delivery uncertainties. In this work, we introduce a new plan evaluation method based on probability and investigate the effect of translational and rotational setup errors. Methods: Clinically approved 18 acoustic neuroma treatment plans were studied retrospectively. For each treatment plan, 200 new dose distributions were re-calculated while introducing both translational (in all cardinal directions) and rotational errors (in all 3 orthogonal rotation angles). The magnitude of errors for each simulation was randomly generated from the Gaussian distribution with one standard deviation of 0.5 mm and 0.5 degree respectively. From the simulated dose distributions, an approximated probability density function of dose objective for both GTV and brainstem were calculated. The probability density function was then used to compute the probability of delivering prescribed dose to 90% of GTV for each patient. For brainstem, the probability of limiting maximum dose to 11Gy was calculated. These probabilistic metrics were then analyzed with respect to GTV volume, conformity index, and min and max dose to the GTV and brainstem. Results: Across all cases, the average probability of delivering prescribed dose to the 90% of GTV volume was 85±14%. Four patients showed > 50% risk of exceeding 11Gy limit to the brainstem. Conclusion: In highly conformal, single fraction radiosurgery, a small setup error can be critical. We found that the probabilistic assessment of plan objectives can be used to quantitatively assess true target coverage better than the conventional plan assessment metrics.

Purpose: Severe CT artifacts can impair our ability to accurately calculate proton range thereby resulting in a clinically unacceptable treatment plan. In this work, we investigated a novel CT artifact correction method based on a coregistered MRI and investigated its ability to estimate CT HU and proton range in the presence of severe CT artifacts. Methods: The proposed method corrects corrupted CT data using a coregistered MRI to guide the mapping of CT values from a nearby artifact-free region. First patient MRI and CT images were registered using 3D deformable image registration software based on B-spline and mutual information. The CT slice with severe artifacts was selected as well as a nearby slice free of artifacts (e.g. 1cm away from the artifact). The two sets of paired MRI and CT images at different slice locations were further registered by applying 2D deformable image registration. Based on the artifact free paired MRI and CT images, a comprehensive geospatial analysis was performed to predict the correct CT HU of the CT image with severe artifact. For a proof of concept, a known artifact was introduced that changed the ground truth CT HU value up to 30% and up to 5cm error in proton range. The ability of the proposed method to recover the ground truth was quantified using a selected head and neck case. Results: A significant improvement in image quality was observed visually. Our proof of concept study showed that 90% of area that had 30% errors in CT HU was corrected to 3% of its ground truth value. Furthermore, the maximum proton range error up to 5cm was reduced to 4mm error. Conclusion: MRI based CT artifact correction method can improve CT image quality and proton range calculation for patients with severe CT artifacts.

Purpose: To determine the ability to adapt discrete spot-scanning proton therapy (SSPT) plans based on geometric changes of anatomy to minimize normal tissue dose and maintain target coverage. Methods: We developed and tested a range-correction algorithm to compensate for anatomy changes in SSPT with correction factors for target lateral size changes and energy scaling. This algorithm adjusts the energy of each spot from the original optimized treatment plan to match the new daily anatomy based on water equivalent path-length. To correct for the lateral target size changes, the peripheral spots were scaled based on phantom studies with variable target size. For energy change corrections, alternative cumulative scaling factor lookup tables were generated based on calculated central-axis and integral depth dose calculations for different energies. These various adaptive algorithms were performed on 7 lung cancer patients that were previously treated with proton therapy and who required at least one adaptive intervention. Single-field optimized SSPT plans were generated for these patients with clinical beam angles. Dose-volume histogram metrics were obtained for these patients for both the non-adaptive and the different adaptive plans applied to the last available weekly CT scan. Results: The doses to normal tissue were largely reduced for the spinal cord (Dmax), total lung (V20Gy), and contralateral lung (V20Gy) for all different methods of adaptive planning. With both corrections applied, the average changes for these metrics were −6.2Gy, −2.7%, and −4.9%, respectively. The same method generated unacceptably high target hot spots with average target V110% increase of 12.3%. Conclusion: Adaptive methods based on direct adjustments to proton range can reduce normal tissue doses under large anatomical changes but are insufficient in achieving clinically acceptable target doses and generate unacceptably sizeable hot spots. Adaptive planning methods for proton therapy will likely necessitate some aspect of reoptimization for acceptable clinical utility.

Purpose: This work investigated a novel planning strategy of selecting radiotherapy beam angles that minimizes the change in water equivalent thickness (dWET) during respiration in order to reduce the effects of respiratory motion in passively scattered proton therapy (PSPT). Methods: In a clinical trial treating locally-advanced lung cancer with proton therapy, 2–4 co-planar beams were previously selected by dosimetrists in the design of physician-approved PSPT treatment plans. The authors identified a cohort of patients in which respiratory motion affected the planned PSPT dose delivery. For this cohort, this work analyzed dWET during respiration over a 360 degree arc of potential treatment angles around the patient: the dWET was defined as the difference in WET between the full-exhale (T50) and full-inhale (T0) phases of the simulation 4DCT. New PSPT plans were redesigned by selecting new beam angles that demonstrated significant reduction in the value of dWET. Between the T50 and T0 phases, the root-mean-square deviation of dose and the change in dose-volume histogram curves (dAUC) for anatomical structures were calculated to compare the original to dWET reduction plans. Results: To date, three plans were retrospectively redesigned based on dWET analysis. In the dWET reduction plan, the root mean square dose (T50-T0) was reduced by 15–35% and the DVH dAUC values were reduced by more than 60%.The PSPT plans redesigned by selecting appropriate field angles to minimize dWET demonstrated less dosimetric variation due to respiration. Conclusion: We have introduced the use of a new metric to quantify respiratory motion in proton therapy: dWET. The use of dWET allows us to minimize the impact of respiratory motion of the entire anatomy in the beam path. This work is a proof of principle that dWET could suggest field angles in proton therapy that are more robust to the effects of respiratory motion.

Purpose: To examine the supposition of the static dose cloud and adequacy of the planning target volume (PTV) dose distribution as the worst-case representation of clinical target volume (CTV) dose distribution for photon therapy in head and neck (H&N) plans. Methods: Five diverse H&N plans clinically delivered at our institution were selected. Isocenter for each plan was shifted positively and negatively in the three cardinal directions by a displacement equal to the PTV expansion on the CTV (3 mm) for a total of six shifted plans per original plan. The perturbed plan dose was recalculated in Eclipse (AAA v11.0.30) using the same, fixed fluence map as the original plan. The dose distributions for all plans were exported from the treatment planning system to determine the worst-case CTV dose distributions for each nominal plan. Two worst-case distributions, cold and hot, were defined by selecting the minimum or maximum dose per voxel from all the perturbed plans. The resulting dose volume histograms (DVH) were examined to evaluate the worst-case CTV and nominal PTV dose distributions. Results: Inspection demonstrates that the CTV DVH in the nominal dose distribution is indeed bounded by the CTV DVHs in the worst-case dose distributions. Furthermore, comparison of the D95% for the worst-case (cold) CTV and nominal PTV distributions by Pearson's chi-square test shows excellent agreement for all plans. Conclusion: The assumption that the nominal dose distribution for PTV represents the worst-case dose distribution for CTV appears valid for the five plans under examination. Although the worst-case dose distributions are unphysical since the dose per voxel is chosen independently, the cold worst-case distribution serves as a lower bound for the worst-case possible CTV coverage. Minor discrepancies between the nominal PTV dose distribution and worst-case CTV dose distribution are expected since the dose cloud is not strictly static. This research was supported by the NCI through grant K25CA168984, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, and by the Fraternal Order of Eagles Cancer Research Fund, the Career Development Award Program at Mayo Clinic.

Purpose: The objective of this study was to quantify respiratory motion-induced dose uncertainty at the planning stage for step-and-shoot intensity-modulated radiation therapy (IMRT) using an analytical technique.Methods: Ten patients with stage II/III lung cancer who had undergone a planning four-dimensional (4D) computed tomographic scan and step-and-shoot IMRT planning were selected with a mix of motion and tumor size for this retrospective study. A step-and-shoot IMRT plan was generated for each patient. The maximum and minimum doses with respiratory motion were calculated for each plan, and the mean deviation from the 4D dose was calculated, taking delivery time, fractionation, and patient breathing cycle into consideration.Results: For all patients evaluated in this study, the mean deviation from the 4D dose in the planning target volume (PTV) was

Purpose: The purpose of this study is two‐fold: 1) to study proton dose variation due to weekly patient setup and anatomy change; and 2) to evaluate the effectiveness of robustness optimization and robustness evaluation techniques. Methods: For a given patient, single‐field optimized (SFO), multi‐field optimized (MFO) and multi‐field optimized with robustness constrains (MFO‐Robust) plans were generated on a simulation CT (CT0). The robustness of the plans was evaluated on CT0 with a statistical technique to compute standard deviation of the dose‐volume histogram (SD‐DVH) of the target volumes and critical structures for 600 combinations of setup and range uncertainties. The original plans were recalculated on subsequent weekly CTs (CTn), and the dose distributions on weekly CTs were deformed onto CT0 using the Velocity (Velocity Medical Solutions) software. The DVHs of weekly doses, as well as the accumulated dose were compared on CT0 with the anticipated SD‐DVH variations predicted by the statistical plan robustness evaluation system. Results: The mean standard deviation of weekly doses for voxels within the GTV is

Purpose: To assess the significance of the treatment plan complexity on the sensitivity of multifield optimized intensity modulated proton therapy (IMPT) plans to inter‐fraction anatomical changes. Methods: Eight lung cancer patients were planned with IMPT to meet clinical criteria. These plans were retrospectively analyzed for this study. A complexity score based on the in‐field dose heterogeneity within the target weighted by the number of regions of dose homogeneity was devised to evaluate the complexity of IMPT plans. The complexity score was calculated for each of the treatment plans and the change in target coverage over the course of treatment was assessed for each patient based on weekly CT imaging. The change in proton range to the distal edge of the clinical target volume was used as a quantitative metric for anatomical change. Greater than 5% drop to the target D99% was chosen as an action level to signal a clinically significant drop in target coverage that would require adaptive replanning. Results: For plans with a low complexity score, target coverage was well preserved over the course of treatment for even a large change in proton range to the target distal edge. Plans with high complexity scores, however, were sensitive to changes in anatomy, and even a small WET change resulted in a clinically significant drop in target coverage. Plans with complexity score below 1 preserved target coverage in 16/18 cases, while plans with complexity score above 1 preserved target coverage in only 3/25 cases. Conclusion: IMPT plan with highly modulated field‐to‐field dose distribution can suffer clinically significant target coverage changes due to anatomical change. The complexity score function devised in this work can be a good predictor of robustness to anatomical change and could potentially be a part of routine plan check.

Purpose: To compare the effects of respiratory motion by calculating four‐dimensional (4D) dose accumulation on proton and photon radiotherapy plans for a stage III lung cancer cohort.Methods and Materials: This study used passively scattered proton therapy (PSPT) and photon intensity modulated radiotherapy (IMRT) plans designed to meet the same criteria for an ongoing clinical trial. For 20 patients, 4D dose evaluation was calculated on both the PSPT and IMRT plans by accumulating individual phase dose using an intensity‐based in‐house deformable image registration algorithm. The resulting 4D dose accumulation method was compared against current standard of three dimensional (3D) dose as calculated on the average CT. Results: Both proton and photon plans demonstrated dosimetric differences when accounting for respiratory motion using 4D dose calculation methods. Reported 4D‐3D values are mean plus one standard deviation. 4D‐3D difference in PTV 95% dose coverage was >2% for 5 IMRT (0.5±1.1Gy) and 4 PSPT plans (0.5±0.8Gy). The structural doses most affected by respiratory motion were normal structures distal to the tumor, such as esophagus V65 (IMRT: −0.5±1.2Gy, PSPT: −0.4±1.4), mean heart (IMRT: − 0.4±0.6 Gy, PSPT: −0.3±0.9 Gy) and spinal cord maximum (IMRT: 0.0±0.2Gy, PSPT: 1.5±2.9Gy). No correlations were found between tumor motion and the change in 4D dose difference for any lung, heart, or spine dose‐volume index. Conclusions: The use of 4D dose evaluation techniques at time of treatment planning revealed small but significant respiratory motion induced deviations from the 3D planned dose for both photon and proton therapy. In both modalities, differences between 3D and 4D dose distributions are patient dependent; no population trends were found correlating dose differences to the extent of tumor motion. The implementation of 4D dose calculation methods for proton and photon lung cancer radiotherapy plans may help identify patients for whom respiratory motion alters the planned dose distribution. This project is supported by grant P01CA021239 from the National Cancer Institute.

Purpose: Proton therapy is more sensitive to both setup and beam range uncertainties than the conventional photon therapy due to the finite range of protons. For the purpose of evaluation of plan robustness in the face of such uncertainties, we developed a method to quantify and visualize the uncertainties in proton plans using comprehensive statistical analysis. The application of the proposed method is shown for selected patients in various clinical sites. Methods: Clinically approved 20 lung, 10 prostate, and a brain cases were retrospectively analyzed. For a selected volume of interest, probability density function (PDF) describing variation in point dose value for every tissue element under setup and range uncertainties was computed based on over 600 dose calculations per plan. The expected dose‐volume histogram (E‐DVH) was calculated by weighting the probability of each tissue element receiving a specified dose level. Similarly, standard deviation of the E‐DVH was calculated (SD‐DVH). Furthermore for each volume element, their probability of receiving greater dose than the dose constraint was visualized as color‐wash, mapped onto the patients CT images for spatial visualization and assessment. Results: The E‐DVH value of target coverage under the uncertainties was consistently lower than that of the nominal value, with a mean difference of−1.1% (−0.9% for breath‐hold), −0.3%, and −2.2% for lung, prostate, and a brain cases respectively. The organs that were most sensitive to the uncertainties were esophagus and spinal cord for lung, rectum for prostate, and brain stem for brain cancer. Conclusion: A clinically feasibly plan robustness analysis tool based on statistical simulation has been developed for proton therapy plans. The passively scattered beam lung plans and scanning beam prostate plans appears to be robust in terms of target coverage. However, structures that are small in volume or located near the target area showed greater sensitivity to uncertainties. This project is supported by grant P01CA021239 from the National Cancer Institute.

Purpose: The objective of this study was to quantify respiratory motion induced dose uncertainty at the planning stage for step and shoot intensity modulated radiation therapy (IMRT) using a novel analytical technique. Methods: Ten patients with stage II/III lung cancer who had undergone a planning 4DCT scan and step and shoot IMRT planning were selected with a mix of motion and tumor size for this study. The maximum and minimum doses with respiratory motion were calculated for each plan, and the mean deviation from the 4D dose was calculated, taking delivery time, fractionation, and patient breathing cycle into consideration. Results: For all patients evaluated in this study, the mean difference between the total delivered dose and the 4D dose in the planning target volume (PTV) was

Purpose: To compare plan robustness, dose variations at field junctions, and overall dose conformity to target between passive scattering and discrete spot scanning (DSS) proton craniospinal irradiation. Methods: A DSS treatment plan was generated for three craniospinal CT image sets. A forward planning approach was used to generate treatment plans for the cranium and lower spine, which exhibited dose tapering of about 10 Gy/cm towards the thoracic spine to facilitate the low‐gradient field junctions. In‐house software accomplished optimization of discrete spot weights for the thoracic‐level field, which was then imported into the Varian Eclipse clinical treatment planning system for dose calculation. Planning was guided using clinical targets defined for craniospinal irradiation with passively scattered proton beams. Robustness analysis was performed by varying the position of each beam isocenter +/−3 mm along each cardinal axis, simulating setup errors, as well as adjusting the CT number to relative stopping power curve to mimic +/−3.5% range variation, and then analyzing the resulting dose distribution. Dose profiles along the craniospinal axis were used to evaluate intrafraction dose variation at field junctions. Dose volume histograms (DVH) were generated for each robustness element, and then combined to create composite DVH illustrating a range of possible delivered dose. Results: Results of robustness analysis indicate that field isocenter shifts along the craniospinal axis can Result in dose intrafraction variations at the junction in excess of 25% for passive scattering proton plans. This value is markedly reduced for the same shifts applied to DSS plans using a tapered‐dose field matching technique. Conclusion: This work demonstrates the potential for improved robustness of proton craniospinal irradiations using a DSS delivery method, as well as significant decreases in clinic expenses. The use of apertures to define the sagittal plane field edge for DSS delivery improves the dose to target.

Purpose: Intensity-modulated proton therapy (IMPT) is highly sensitive to uncertainties in beam range and patient setup. Conventionally, these uncertainties are dealt using geometrically expanded planning target volume (PTV). In this paper, the authors evaluated a robust optimization method that deals with the uncertainties directly during the spot weight optimization to ensure clinical target volume (CTV) coverage without using PTV. The authors compared the two methods for a population of head and neck (H&N) cancer patients. Methods: Two sets of IMPT plans were generated for 14 H&N cases, one being PTV-based conventionally optimized and the other CTV-based robustly optimized. For the PTV-based conventionally optimized plans, the uncertainties are accounted for by expanding CTV to PTV via margins and delivering the prescribed dose to PTV. For the CTV-based robustly optimized plans, spot weight optimization was guided to reduce the discrepancy in doses under extreme setup and range uncertainties directly, while delivering the prescribed dose to CTV rather than PTV. For each of these plans, the authors calculated dose distributions under various uncertainty settings. The root-mean-square dose (RMSD) for each voxel was computed and the area under the RMSD-volume histogram curves (AUC) was used to relatively compare plan robustness. Data derived from the dose volume histogram in the worst-case and nominal doses were used to evaluate the plan optimality. Then the plan evaluation metrics were averaged over the 14 cases and were compared with two-sided paired t tests. Results: CTV-based robust optimization led to more robust (i.e., smaller AUCs) plans for both targets and organs. Under the worst-case scenario and the nominal scenario, CTV-based robustly optimized plans showed better target coverage (i.e., greater D95%), improved dose homogeneity (i.e., smaller D5% − D95%), and lower or equivalent dose to organs at risk. Conclusions: CTV-based robust optimization provided significantly more robust dose distributions to targets and organs than PTV-based conventional optimization in H&N using IMPT. Eliminating the use of PTV and planning directly based on CTV provided better or equivalent normal tissue sparing.

Purpose: Unlike photon therapy, proton beams are affected by range uncertainties caused by variations of anatomy in the beam path. Appropriate target margins must be used during treatment planning to account for range uncertainties. Currently, there are no clinical data to quantify the magnitude of interfractional anatomical uncertainties for different beam directions. The purpose of this study was to develop a ray‐tracing based method to determine margins to account for anatomical variations. The method was applied for nodal boost treatments in the pelvic region. Methods:CT scans obtained weekly during chemoradiation for intact cervical cancer were utilized for this study. Common and external iliac nodal contours were transferred from planning to weekly images using rigid alignment. A ray‐tracing method was used to create 2D water equivalent thickness matrices for both distal and proximal surfaces of targets in the beam direction. Positive and negative differences between weekly and planning matrices were used to determine required planning margins in the distal and proximal directions, respectively. We required 90% of the treatment fractions to be covered for population margin definition. Results: For common iliac nodes, the angles with the least and most anatomical variations for the distal margin were at 90‐degrees (LtLat) and 45‐degrees (LAO), which required 0.5 and 1.6cm margins, respectively. For proximal margins, the least and most anatomical variations were at 180‐degrees (PA) and 315‐degrees (RAO) with 1.1 and 2.9cm margins, respectively. For external iliac nodes, PA and lateral beams required 0.8 and 0.4cm distal margins and 1.0 and 1.1cm proximal margins, respectively. Conclusions: We have developed a fast margin determination method using historical clinical data. We also demonstrated that inter‐fractional anatomical variations are asymmetric and dependent on beam angle. Beams passing through the bowel region where ranges are impacted by gas content and weight loss have much greater anatomical variations.

Purpose: Women with intact cervical cancer with involved pelvic lymph nodes require additional boost radiation therapy to involved lymph nodes following pelvic irradiation. These boosts are particularly challenging to plan due to the need to avoid central structures, which are treated to organ tolerance with intracavitary brachytherapy. A single posterior (PA) proton beam may be an ideal treatment technique for nodal boost, which can spare the rectum, bladder and small bowel. However, appropriate margins to account for setup error and range uncertainty are unknown in this setting. The purpose of this study was to determine the margins required to account for interfractional anatomical variations and setup uncertainties during intensity modulated protontreatment (IMPT). Methods: Five patients treated with definitive radiation therapy for cervical cancer were used retrospectively for this study. All patients received 4–5 weekly CTs during treatment. External iliac nodes were contoured by a radiationoncologist on each planning image. Rigid alignments using bony structures near the external iliac nodes are used. A single IMPT PA‐beam was simulated. Ray‐ trace methods were used to determine the water equivalent thickness (WET) of each ray parallel to the beam path. The resulting 2D‐WET matrixes were then mapped back to the planning CTimage and converted back to physical depth to determine the actual margin required to cover the target. Results: Distal margins are represented by a positive number while the opposite is true for proximal margins. The average margin mean for all patients is ‐ 0.7mm (Range −2 to 5mm). The average variation for all patients is 6mm (Range 2 to 12mm). Maximum distal and proximal margins needed to cover all fractions were 8 and 9mm, respectively Conclusions: We determined that a ∼9mm margin would be sufficient for nodal boost IMPT, which produced excellent dosimetric results, compared to corresponding photon plans. Supported in part by NCI P01 CA021239

Despite recent developments in computer-aided design in architecture, both in terms of form generation techniques and performance-based design tools, there still appears to be polarization between the ‘visual’ and the ‘technical’ elements of design. Two causes of this are discussed: long-standing tradition within the discipline and perception of design as primarily a visual exercise. Structural layout optimization is a technique which enables automatic identification of optimal arrangements of structural elements in frames. As the technique appears to have the potential to help reduce the polarization between the visual and the technical elements of design, it can be considered as an ‘integrative’ form generation tool. Applications of the technique are considered via three design examples, demonstrating both its potential and areas where refinement is required before it is suitable for application in practice.