Controlling Beam Complexity in Intensity Modulated Radiation Therapy.

External beam intensity modulated radiation therapy (IMRT) is a technique in which the spatial intensity of radiation from each beam direction can be modulated to provide superior conformality of dose to a tumor volume while sparing important normal tissues. A fundamental and potentially limiting feature of IMRT is the highly complex fields that can be created through inverse plan optimization. Highly modulated treatments are a large departure from conventional radiotherapy methods, are difficult to deliver accurately and efficiently, and can result in an undesirable increase in leakage dose being delivered to the patient. Longer deliveries may also increase the chance for patient motion during treatment and could potentially reduce the probability of controlling some tumors. The large intensity fluctuations observed in IMRT beams are often a result of the degeneracy of the optimization problem, and the types of optimization method and cost function used. This work demonstrates that beam complexity is a result of these two issues, and is dependent on the placement of dose evaluation points in the target and normal tissues. This research shows that (i) optimizing surfaces instead of discrete beamlet intensities to represent the beam can reduce the degrees of freedom in IMRT and results in much smoother beams at the expense of a slight increase in normal tissues, (ii) maximum beamlet intensity restrictions are useful for improved delivery efficiency, but may restrict the optimizer at low limits, and (iii) modulation penalties can be incorporated into the cost function to promote plan smoothness without sacrificing plan quality. Penalizing the overall plan modulation is an effective way to reduce modulation, but it falsely penalizes the desirable beam modulation as well as the undesirable modulation. To address this problem, diffusion principles are used to develop a spatially adaptive smoothing method that only penalizes the unnecessary beam modulation and can be used without degrading plan quality. This method is customizable to a variety of treatment scenarios. The clinical impact of reducing beam complexity is significant, as it can result in an improvement in delivery accuracy and efficiency, quicker optimization times, and increased robustness to point sampling and geometric uncertainty.Ph.D.Nuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57648/2/mcoselmo_1.pd

Investigating ion recombination effects in a liquid‐filled ionization chamber array used for IMRT QA measurements

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134893/1/mp6822.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134893/2/mp6822_am.pd

Penalization of aperture complexity in inversely planned volumetric modulated arc therapy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134774/1/mp2566.pd

The impact of a high‐definition multileaf collimator for spine SBRT

PurposeAdvanced radiotherapy delivery systems designed for high‐dose, high‐precision treatments often come equipped with high‐definition multi‐leaf collimators (HD‐MLC) aimed at more finely shaping radiation dose to the target. In this work, we study the effect of a high definition MLC on spine stereotactic body radiation therapy (SBRT) treatment plan quality and plan deliverability.Methods and MaterialsSeventeen spine SBRT cases were planned with VMAT using a standard definition MLC (M120), HD‐MLC, and HD‐MLC with an added objective to reduce monitor units (MU). M120 plans were converted into plans deliverable on an HD‐MLC using in‐house software. Plan quality and plan deliverability as measured by portal dosimetry were compared among the three types of plans.ResultsOnly minor differences were noted in plan quality between the M120 and HD‐MLC plans. Plans generated with the HD‐MLC tended to have better spinal cord sparing (3% reduction in maximum cord dose). HD‐MLC plans on average had 12% more MU and 55% greater modulation complexity as defined by an in‐house metric. HD‐MLC plans also had significantly degraded deliverability. Of the VMAT arcs measured, 94% had lower gamma passing metrics when using the HD‐MLC.ConclusionModest improvements in plan quality were noted when switching from M120 to HD‐MLC at the expense of significantly less accurate deliverability in some cases.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139989/1/acm212197.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139989/2/acm212197_am.pd

Quantumâ inspired algorithm for radiotherapy planning optimization

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153600/1/mp13840.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153600/2/mp13840_am.pd

A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer

Background and purpose To study whether cytokine markers may improve predictive accuracy of radiation esophagitis (RE) in non-small cell lung cancer (NSCLC) patients. Materials and methods A total of 129 patients with stage I-III NSCLC treated with radiotherapy (RT) from prospective studies were included. Thirty inflammatory cytokines were measured in platelet-poor plasma samples. Logistic regression was performed to evaluate the risk factors of RE. Stepwise Akaike information criterion (AIC) and likelihood ratio test were used to assess model predictions. Results Forty-nine of 129 patients (38.0%) developed grade ≥2 RE. Univariate analysis showed that age, stage, concurrent chemotherapy, and eight dosimetric parameters were significantly associated with grade ≥2 RE (p < 0.05). IL-4, IL-5, IL-8, IL-13, IL-15, IL-1α, TGFα and eotaxin were also associated with grade ≥2 RE (p <0.1). Age, esophagus generalized equivalent uniform dose (EUD), and baseline IL-8 were independently associated grade ≥2 RE. The combination of these three factors had significantly higher predictive power than any single factor alone. Addition of IL-8 to toxicity model significantly improves RE predictive accuracy (p = 0.019). Conclusions Combining baseline level of IL-8, age and esophagus EUD may predict RE more accurately. Refinement of this model with larger sample sizes and validation from multicenter database are warranted

Use of plan quality degradation to evaluate tradeoffs in delivery efficiency and clinical plan metrics arising from IMRT optimizer and sequencer compromises

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135043/1/mp8118.pd

A Multi-Objective Bayesian Networks Approach for Joint Prediction of Tumor Local Control and Radiation Pneumonitis in Non-Small-Cell Lung Cancer (NSCLC) for Response-Adapted Radiotherapy

Purpose Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), which are likely to be intertwined. Here, we compare the performance of separate and joint outcomes predictions for response‐adapted personalized treatment planning. Methods A total of 118 NSCLC patients treated on prospective protocols with 32 cases of local progression and 20 cases of RP grade 2 or higher (RP2) were studied. Sixty‐eight patients with 297 features before and during radiotherapy were used for discovery and 50 patients were reserved for independent testing. A multiobjective Bayesian network (MO‐BN) approach was developed to identify important features for joint LC/RP2 prediction using extended Markov blankets as inputs to develop a BN predictive structure. Cross‐validation (CV) was used to guide the MO‐BN structure learning. Area under the free‐response receiver operating characteristic (AU‐FROC) curve was used to evaluate joint prediction performance. Results Important features including single nucleotide polymorphisms (SNPs), micro RNAs, pretreatment cytokines, pretreatment PET radiomics together with lung and tumor gEUDs were selected and their biophysical inter‐relationships with radiation outcomes (LC and RP2) were identified in a pretreatment MO‐BN. The joint LC/RP2 prediction yielded an AU‐FROC of 0.80 (95% CI: 0.70–0.86) upon internal CV. This improved to 0.85 (0.75–0.91) with additional two SNPs, changes in one cytokine and two radiomics PET image features through the course of radiotherapy in a during‐treatment MO‐BN. This MO‐BN model outperformed combined single‐objective Bayesian networks (SO‐BNs) during‐treatment [0.78 (0.67–0.84)]. AU‐FROC values in the evaluation of the MO‐BN and individual SO‐BNs on the testing dataset were 0.77 and 0.68 for pretreatment, and 0.79 and 0.71 for during‐treatment, respectively. Conclusions MO‐BNs can reveal possible biophysical cross‐talks between competing radiotherapy clinical endpoints. The prediction is improved by providing additional during‐treatment information. The developed MO‐BNs can be an important component of decision support systems for personalized response‐adapted radiotherapy

Thoracic radiation-induced pleural effusion and risk factors in patients with lung cancer

The risk factors and potential practice implications of radiation-induced pleural effusion (RIPE) are undefined. This study examined lung cancer patients treated with thoracic radiation therapy (TRT) having follow-up computed tomography (CT) or 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT. Increased volumes of pleural effusion after TRT without evidence of tumor progression was considered RIPE. Parameters of lung dose-volume histogram including percent volumes irradiated with 5-55 Gy (V5-V55) and mean lung dose (MLD) were analyzed by receiver operating characteristic analysis. Clinical and treatment-related risk factors were detected by univariate and multivariate analyses. 175 out of 806 patients receiving TRT with post-treatment imaging were included. 51 patients (24.9%) developed RIPE; 40 had symptomatic RIPE including chest pain (47.1%), cough (23.5%) and dyspnea (35.3%). Female (OR = 0.380, 95% CI: 0.156–0.926, p = 0.033) and Caucasian race (OR = 3.519, 95% CI: 1.327–9.336, p = 0.011) were significantly associated with lower risk of RIPE. Stage and concurrent chemotherapy had borderline significance (OR = 1.665, p = 0.069 and OR = 2.580, p = 0.080, respectively) for RIPE. Patients with RIPE had significantly higher whole lung V5-V40, V50 and MLD. V5 remained as a significant predictive factor for RIPE and symptomatic RIPE (p = 0.007 and 0.022) after adjusting for race, gender and histology. To include, the incidence of RIPE is notable. Whole lung V5 appeared to be the most significant independent risk factor for symptomatic RIPE

Principal component analysis identifies patterns of cytokine expression in non-small cell lung cancer patients undergoing definitive radiation therapy

Radiation treatment (RT) stimulates the release of many immunohumoral factors, complicating the identification of clinically significant cytokine expression patterns. This study used principal component analysis (PCA) to analyze cytokines in non-small cell lung cancer (NSCLC) patients undergoing RT and explore differences in changes after hypofractionated stereotactic body radiation therapy (SBRT) and conventionally fractionated RT (CFRT) without or with chemotherapy