Liposomes: Clinical Applications and Potential for Image-Guided Drug Delivery.

Liposomes have been extensively studied and are used in the treatment of several diseases. Liposomes improve the therapeutic efficacy by enhancing drug absorption while avoiding or minimizing rapid degradation and side effects, prolonging the biological half-life and reducing toxicity. The unique feature of liposomes is that they are biocompatible and biodegradable lipids, and are inert and non-immunogenic. Liposomes can compartmentalize and solubilize both hydrophilic and hydrophobic materials. All these properties of liposomes and their flexibility for surface modification to add targeting moieties make liposomes more attractive candidates for use as drug delivery vehicles. There are many novel liposomal formulations that are in various stages of development, to enhance therapeutic effectiveness of new and established drugs that are in preclinical and clinical trials. Recent developments in multimodality imaging to better diagnose disease and monitor treatments embarked on using liposomes as diagnostic tool. Conjugating liposomes with different labeling probes enables precise localization of these liposomal formulations using various modalities such as PET, SPECT, and MRI. In this review, we will briefly review the clinical applications of liposomal formulation and their potential imaging properties.

Feasibility of CBCT-based dose with a patient-specific stepwise HU-to-density curve to determine time of replanning.

PURPOSE: (a) To investigate the accuracy of cone-beam computed tomography (CBCT)-derived dose distributions relative to fanbeam-based simulation CT-derived dose distributions; and (b) to study the feasibility of CBCT dosimetry for guiding the appropriateness of replanning. METHODS AND MATERIALS: Image data corresponding to 40 patients (10 head and neck [HN], 10 lung, 10 pancreas, 10 pelvis) who underwent radiation therapy were randomly selected. Each patient had both intensity-modulated radiation therapy and volumetric-modulated arc therapy plans; these 80 plans were subsequently recomputed on the CBCT images using a patient-specific stepwise curve (Hounsfield units-to-density). Planning target volumes (PTVs; D98%, D95%, D2%), mean dose, and V95% were compared between simulation-CT-derived treatment plans and CBCT-based plans. Gamma analyses were performed using criterion of 3%/3 mm for three dose zones (>90%, 70%~90%, and 30%~70% of maximum dose). CBCT-derived doses were then used to evaluate the appropriateness of replanning decisions in 12 additional HN patients whose plans were previously revised during radiation therapy because of anatomic changes; replanning in these cases was guided by the conventional observed source-to-skin-distance change-derived approach. RESULTS: For all disease sites, the difference in PTV mean dose was 0.1% ± 1.1%, D2% was 0.7% ± 0.1%, D95% was 0.2% ± 1.1%, D98% was 0.2% ± 1.0%, and V95% was 0.3% ± 0.8%; For 3D dose comparison, 99.0% ± 1.9%, 97.6% ± 4.4%, and 95.3% ± 6.0% of points passed the 3%/3 mm criterion of gamma analysis in high-, medium-, and low-dose zones, respectively. The CBCT images achieved comparable dose distributions. In the 12 previously replanned 12 HN patients, CBCT-based dose predicted well changes in PTV D2% (Pearson linear correlation coefficient = 0.93; P < 0.001). If 3% of change is used as the replanning criteria, 7/12 patients could avoid replanning. CONCLUSIONS: CBCT-based dose calculations produced accuracy comparable to that of simulation CT. CBCT-based dosimetry can guide the decision to replan during the course of treatment.

Colloidal Properties of Nanoerythrosomes Derived from Bovine Red Blood Cells.

Liposomes are nanoscale containers that are typically synthesized from lipids using a high-shear process such as extrusion or sonication. While liposomes are extensively used in drug delivery, they do suffer from certain problems including limited colloidal stability and short circulation times in the body. As an alternative to liposomes, we explore a class of container structures derived from erythrocytes (red blood cells). The procedure involves emptying the inner contents of these cells (specifically hemoglobin) and resuspending the empty structures in buffer, followed by sonication. The resulting structures are termed nanoerythrosomes (NERs), i.e., they are membrane-covered nanoscale containers, much like liposomes. Cryo-transmission electron microscopy (cryo-TEM) and small-angle neutron scattering (SANS) are employed for the first time to study these NERs. The results reveal that the NERs are discrete spheres (∼110 nm diameter) with a unilamellar membrane of thickness ∼4.5 nm. Remarkably, the biconcave disc-like shape of erythrocytes is also exhibited by the NERs under hypertonic conditions. Moreover, unlike typical liposomes, NERs show excellent colloidal stability in both buffer as well as in serum at room temperature, and are also able to withstand freeze-thaw cycling. We have explored the potential for using NERs as colloidal vehicles for targeted delivery. Much like conventional liposomes, NER membranes can be decorated with fluorescent or other markers, solutes can be encapsulated in the cores of the NERs, and NERs can be targeted to specifically bind to mammalian cells. Our study shows that NERs are a promising and versatile class of nanostructures. NERs that are harvested from a patient's own blood and reconfigured for nanomedicine can potentially offer several benefits including biocompatibility, minimization of immune response, and extended circulation time in the body.

Optimizing the MLC model parameters for IMRT in the RayStation treatment planning system.

Unlike other commercial treatment planning systems (TPS) which model the rounded leaf end differently (such as the MLC dosimetric leaf gap (DLG) or rounded leaf-tip radius), the RayStation TPS (RaySearch Laboratories, Stockholm, Sweden) models transmission through the rounded leaf end of the MLC with a step function, in which the radiation transmission through the leaf end is the square root of the average MLC transmission factor. We report on the optimization of MLC model parameters for the RayStation planning system. This (TPS) models the rounded leaf end of the MLC with the following parameters: eaf-tip offset, leaf-tip width, average transmission factor, and tongue and groove. We optimized the MLC model parameters for IMRT in the RayStation v. 4.0 planning system and for a Varian C-series linac with a 120-leaf Millennium MLC, and validated the model using measured data. The leaf-tip offset is the geometric offset due to the rounded leaf-end design and resulting divergence of the light/radiation field. The offset value is a function of the leaf-tip position, and tabulated data are available from the vendor. The leaf-tip width was iteratively evaluated by comparing computed and measured transverse dose profiles of MLC defined fields at dmax in water. In-water profile comparisons were also used to verify the MLC leaf position (leaf-tip offset). The average transmission factor and leaf tongue-and-groove width were derived iteratively by maximizing the agreement between measurements and RayStation TPS calculations for five clinical IMRT QA plans. Plan verifications were performed by comparing MapCHECK2 measurements and Monte Carlo calculations. The MLC model was validated using five test IMRT cases from the AAPM Task Group 119 report. Absolute gamma analyses (3 mm/3% and 2 mm/2%) were applied. In addition, computed output factors for MLC-defined small fields (2 × 2, 3 × 3, 4 × 4, 6× 6cm2) of both 6 MV and 18 MV photons were compared to those independently measured by the Imaging and Radiation Oncology Core (IROC), Houston, TX. 6MV and 18 MV models were both determined to have the same MLC parameters: leaf-tip offset = 0.3 cm, 2.5% transmission, and leaf tongue-and-groove width = 0.05 cm. IMRT QA analysis for five test cases in TG-119 resulted in a 100% passing rate with 3 mm/3% gamma analysis for 6 MV, and > 97.5% for 18 MV. The passing rate was > 94.6% for 6 MV and > 90.9% for 18 MV when the 2 mm/2% gamma analysis criteria was applied. These results compared favorably with those published in AAPM Task Group 119. The reported MLC model parameters serve as a reference for other users.

Online monitoring and error detection of real-time tumor displacement prediction accuracy using control limits on respiratory surrogate statistics.

PURPOSE: To evaluate Hotelling's T(2) statistic and the input variable squared prediction error (Q((X))) for detecting large respiratory surrogate-based tumor displacement prediction errors without directly measuring the tumor's position. METHODS: Tumor and external marker positions from a database of 188 Cyberknife Synchrony™ lung, liver, and pancreas treatment fractions were analyzed. The first ten measurements of tumor position in each fraction were used to create fraction-specific models of tumor displacement using external surrogates as input; the models were used to predict tumor position from subsequent external marker measurements. A partial least squares (PLS) model with four scores was developed for each fraction to determine T(2) and Q((X)) confidence limits based on the first ten measurements in a fraction. The T(2) and Q((X)) statistics were then calculated for every set of external marker measurements. Correlations between model error and both T(2) and Q((X)) were determined. Receiver operating characteristic analysis was applied to evaluate sensitivities and specificities of T(2), Q((X)), and T(2)∪Q((X)) for predicting real-time tumor localization errors >3 mm over a range of T(2) and Q((X)) confidence limits. RESULTS: Sensitivity and specificity of detecting errors >3 mm varied with confidence limit selection. At 95% sensitivity, T(2)∪Q((X)) specificity was 15%, 2% higher than either T(2) or Q((X)) alone. The mean time to alarm for T(2)∪Q((X)) at 95% sensitivity was 5.3 min but varied with a standard deviation of 8.2 min. Results did not differ significantly by tumor site. CONCLUSIONS: The results of this study establish the feasibility of respiratory surrogate-based online monitoring of real-time respiration-induced tumor motion model accuracy for lung, liver, and pancreas tumors. The T(2) and Q((X)) statistics were able to indicate whether inferential model errors exceeded 3 mm with high sensitivity. Modest improvements in specificity were achieved by combining T(2) and Q((X)) results.

Deformable planning CT to cone-beam CT image registration in head-and-neck cancer.

PURPOSE: The purpose of this work was to implement and validate a deformable CT to cone-beam computed tomography (CBCT) image registration method in head-and-neck cancer to eventually facilitate automatic target delineation on CBCT. METHODS: Twelve head-and-neck cancer patients underwent a planning CT and weekly CBCT during the 5-7 week treatment period. The 12 planning CT images (moving images) of these patients were registered to their weekly CBCT images (fixed images) via the symmetric force Demons algorithm and using a multiresolution scheme. Histogram matching was used to compensate for the intensity difference between the two types of images. Using nine known anatomic points as registration targets, the accuracy of the registration was evaluated using the target registration error (TRE). In addition, region-of-interest (ROI) contours drawn on the planning CT were morphed to the CBCT images and the volume overlap index (VOI) between registered contours and manually delineated contours was evaluated. RESULTS: The mean TRE value of the nine target points was less than 3.0 mm, the slice thickness of the planning CT. Of the 369 target points evaluated for registration accuracy, the average TRE value was 2.6 +/- 0.6 mm. The mean TRE for bony tissue targets was 2.4 +/- 0.2 mm, while the mean TRE for soft tissue targets was 2.8 +/- 0.2 mm. The average VOI between the registered and manually delineated ROI contours was 76.2 +/- 4.6%, which is consistent with that reported in previous studies. CONCLUSIONS: The authors have implemented and validated a deformable image registration method to register planning CT images to weekly CBCT images in head-and-neck cancer cases. The accuracy of the TRE values suggests that they can be used as a promising tool for automatic target delineation on CBCT.

Optimal beam arrangement for stereotactic body radiation therapy delivery in lung tumors.

PURPOSE: To compare the different beam arrangement and delivery techniques for stereotactic body radiation therapy (SBRT) of lung lesions using the criteria of Radiation Therapy Oncology Group (RTOG) 0236 protocol. MATERIAL AND METHODS: Thirty-seven medically inoperable lung cancers were evaluated with various planning techniques including multiple coplanar multiple static beams, multiple non-coplanar static beams and arc delivery. Twelve plans were evaluated for each case, including five plans using coplanar fixed beams, six plans using non-coplanar fixed beams and one plan using arc therapy. These plans were compared using the target prescription isodose coverage, high and low dose volumes, and critical organ dose-volume limits. RESULTS: The prescription isodose coverage, high dose evaluation criteria and dose to critical organs were similar among treatment delivery techniques. However, there were differences in low dose criteria, especially in the ratio of the volume of 50% isodose of the prescription dose to the volume of planning treatment volume (R(50%)). The R(50%) in plans using non-coplanar static beams was lower than other plans in 30 of 37 cases (81%). CONCLUSION: Based on the dosimetric criteria outlined in RTOG 0236, the treatment technique using non-coplanar static beams showed the most preferable results for SBRT of lung lesions.

A systematic quality assurance framework for the upgrade of radiation oncology information systems.

In spite of its importance, no systematic and comprehensive quality assurance (QA) program for radiation oncology information systems (ROIS) to verify clinical and treatment data integrity and mitigate against data errors/corruption and/or data loss risks is available. Based on data organization, format and purpose, data in ROISs falls into five different categories: (1) the ROIS relational database and associated files; (2) the ROIS DICOM data stream; (3) treatment machine beam data and machine configuration data; (4) electronic medical record (EMR) documents; and (5) user-generated clinical and treatment reports from the ROIS. For each data category, this framework proposes a corresponding data QA strategy to very data integrity. This approach verified every bit of data in the ROIS, including billions of data records in the ROIS SQL database, tens of millions of ROIS database-associated files, tens of thousands of DICOM data files for a group of selected patients, almost half a million EMR documents, and tens of thousands of machine configuration files and beam data files. The framework has been validated through intentional modifications with test patient data. Despite the 'big data' nature of ROIS, the multiprocess and multithread nature of our QA tools enabled the whole ROIS data QA process to be completed within hours without clinical interruptions. The QA framework suggested in this study proved to be robust, efficient and comprehensive without labor-intensive manual checks and has been implemented for our routine ROIS QA and ROIS upgrades.

Investigation of motion sickness and inertial stability on a moving couch for intra-fraction motion compensation.

BACKGROUND. Respiration-induced tumor motion compensation using a treatment couch requires moving the patient at non-trivial speeds. The purpose of this work was to investigate motion sickness and stability of the patient's external surface due to a moving couch with respiration-comparable velocities and accelerations. MATERIAL AND METHODS. A couch was designed to move with a peak-peak displacement of 5 cm and 1 cm in the S-I and A-P directions, respectively, and a period of 3.6 s. Fifty patients completed a 16-question motion sickness assessment questionnaire (MSAQ) prior to, during, and after the study. Seven optical reflectors affixed to the abdomen of each patient were monitored by infrared cameras. The relationship between reflector positions under stationary and moving conditions was evaluated to assess the stability of the patient's external surface. RESULTS AND DISCUSSION. Among the 4800 responses, 95% were 1 (no discomfort) of 9, and there were no scores of 6 or higher. Mild discomfort (scores of 4-5) was similar during couch motion and before couch motion (p = 0.39). Mild discomfort was less common after couch motion (p = 0.039) than before or during couch movement. There was a near 1:1 correspondence between marker-pair regression coefficients and phase offset values during couch-stationary and couch-moving conditions. Our results show that patients do not suffer motion sickness or external surface instability on a moving couch.

The Feasibility of Integrating Resting-State fMRI Networks into Radiotherapy Treatment Planning.

BACKGROUND: Functional magnetic resonance imaging (fMRI) presents the ability to selectively protect functionally significant regions of the brain when primary brain tumors are treated with radiation therapy. Previous research has focused on task-based fMRI of language and sensory networks; however, there has been limited investigation on the inclusion of resting-state fMRI into the design of radiation treatment plans. METHODS AND MATERIALS: In this pilot study of 9 patients with primary brain tumors, functional data from the default mode network (DMN), a network supporting cognitive functioning, was obtained from resting-state fMRI and retrospectively incorporated into the design of radiation treatment plans. We compared the dosimetry of these fMRI DMN avoidance treatment plans with standard of care treatment plans to demonstrate feasibility. In addition, we used normal tissue complication probability models to estimate the relative benefit of fMRI DMN avoidance treatment plans over standard of care treatment plans in potentially reducing memory loss, a surrogate for cognitive function. RESULTS: On average, we achieved 20% (P = 0.002) and 12% (P = 0.002) reductions in the mean and maximum doses, respectively, to the DMN without compromising the dose coverage to the planning tumor volume or the dose-volume constraints to organs at risk. Normal tissue complication probability models revealed that when the fMRI DMN was considered during radiation treatment planning, the probability of developing memory loss was lowered by more than 20%. CONCLUSION: In this pilot study, we demonstrated the feasibility of including rs-MRI data into the design of radiation treatment plans to spare cognitively relevant brain regions during radiation therapy. These results lay the groundwork for future clinical trials that incorporate such treatment planning methods to investigate the long-term behavioral impact of this reduction in dose to the cognitive areas and their neural networks that support cognitive performance.

A nested partitions framework for beam angle optimization in intensity-modulated radiation therapy.

Coupling beam angle optimization with dose optimization in intensity-modulated radiation therapy (IMRT) increases the size and complexity of an already large-scale combinatorial optimization problem. We have developed a novel algorithm, nested partitions (NP), that is capable of finding suitable beam angle sets by guiding the dose optimization process. NP is a metaheuristic that is flexible enough to guide the search of a heuristic or deterministic dose optimization algorithm. The NP method adaptively samples from the entire feasible region, or search space, and coordinates the sampling effort with a systematic partitioning of the feasible region at successive iterations, concentrating the search in promising subsets. We used a 'warm-start' approach by initiating NP with beam angle samples derived from an integer programming (IP) model. In this study, we describe our implementation of the NP framework with a commercial optimization algorithm. We compared the NP framework with equi-spaced beam angle selection, the IP method, greedy heuristic and random sampling heuristic methods. The results of the NP approach were evaluated using two clinical cases (head and neck and whole pelvis) involving the primary tumor and nodal volumes. Our results show that NP produces better quality solutions than the alternative considered methods.

A Treatment Planning Method for Better Management of Radiation-Induced Oral Mucositis in Locally Advanced Head and Neck Cancer.

PURPOSE/AIM: To describe a two-phase intensity-modulated radiation therapy (IMRT) treatment planning approach, that is, promising for reduction of oral mucositis risk in locally advanced head-and-neck cancer. MATERIALS AND METHODS: Ten locally advanced head-and-neck cancer patients who underwent RT were retrospectively collected. Conventional IMRT and volumetric-modulated arc therapy (VMAT) plans were generated for these patients following clinical protocol. Following the first phase of generating conventional IMRT plans, our approach utilized data from Monte Carlo-based kernel superposition dose calculations corresponding to beam apertures (generated from the conventional IMRT plans) and used an exact mathematical programming-based optimization approach applying linear programming (LP) to dose optimization in the second phase. RESULTS: Compared with conventional IMRT and VMAT treatment plans, our novel method achieved better preservation of oral cavity (16%-29% lower mean dose, P < 0.01), parotid glands (6%-17% lower mean dose, P < 0.04), and spinal cord (3-11 Gy lower maximum dose, P < 0.03) and lower doses to nonorgan-at-risk/nontarget normal tissues, with the same or better target coverage. CONCLUSIONS: Our LP-based method can be practically implemented in routine clinical use with a goal of limiting radiation-induced oral mucositis for head-and-neck cancer patients.

A multiplan treatment-planning framework: a paradigm shift for intensity-modulated radiotherapy.

PURPOSE: To describe a multiplan intensity-modulated radiotherapy (IMRT) planning framework, and to describe a decision support system (DSS) for ranking multiple plans and modeling the planning surface. METHODS AND MATERIALS: One hundred twenty-five plans were generated sequentially for a head-and-neck case and a pelvic case by varying the dose-volume constraints on each of the organs at risk (OARs). A DSS was used to rank plans according to dose-volume histogram (DVH) values, as well as equivalent uniform dose (EUD) values. Two methods for ranking treatment plans were evaluated: composite criteria and pre-emptive selection. The planning surface determined by the results was modeled using quadratic functions. RESULTS: The DSS provided an easy-to-use interface for the comparison of multiple plan features. Plan ranking resulted in the identification of one to three "optimal" plans. The planning surface models had good predictive capability with respect to both DVH values and EUD values and generally, errors of <6%. Models generated by minimizing the maximum relative error had significantly lower relative errors than models obtained by minimizing the sum of squared errors. Using the quadratic model, plan properties for one OAR were determined as a function of the other OAR constraint settings. The modeled plan surface can then be used to understand the interdependence of competing planning objectives. CONCLUSION: The DSS can be used to aid the planner in the selection of the most desirable plan. The collection of quadratic models constructed from the plan data to predict DVH and EUD values generally showed excellent agreement with the actual plan values.

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation.

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.

Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy.

Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the feedback control system. Our results show that, with the tumor motion unknown but inferred by skin-based markers through the PLS model, the predictive feedback control system was able to effectively compensate intra-fraction motion.

The use of gated and 4D CT imaging in planning for stereotactic body radiation therapy.

The localization of treatment targets is of utmost importance for patients receiving stereotactic body radiation therapy (SBRT), where the dose per fraction is large. While both setup or respiration-induced motion components affect the localization of the treatment volume, the purpose of this work is to describe our management of the intrafraction localization uncertainty induced by normal respiration. At our institution, we have implemented gated computed tomography (CT) acquisition with an active breathing control system (ABC), and 4-dimensional (4D) CT using a skin-based marker and retrospective respiration phase-based image sorting. During gated simulation, 3D CT images were acquired corresponding to end-inhalation and end-exhalation. For 4D CT imaging, 3D CT images were acquired corresponding to 8 phases of the respiratory cycle. In addition to gated or 4D CT images, we acquired a conventional free-breathing CT (FB). For both gated and 4D CT images, the target contours were registered to the FB scan in the planning system. These contours were then combined in the FB image set to form the internal target volume (ITV). Dynamic conformal arc treatment plans were generated for the ITV using the FB scan and the gated or 4D scans with an additional 7-mm margin for patient setup uncertainty. We have described our results for a pancreas and a lung tumor case. Plans were normalized so that the PTV received 95% of the prescription dose. The dose distribution for all the critical structures in the pancreas and lung tumor cases resulted in increased sparing when the ITV was defined using gated or 4D CT images than when the FB scan was used. Our results show that patient-specific target definition using gated or 4D CT scans lead to improved normal tissue sparing.

Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET.

Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF = 9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne's adaptive thresholding method (DSI = 0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.

Randomized Phase II Study of Preoperative Chemoradiotherapy ± Panitumumab Followed by Consolidation Chemotherapy in Potentially Operable Locally Advanced (Stage IIIa, N2+) Non-Small Cell Lung Cancer: NRG Oncology RTOG 0839.

INTRODUCTION: Multimodality therapy has curative potential in locally advanced NSCLC. Mediastinal nodal sterilization (MNS) after induction chemoradiotherapy (CRT) can serve as an intermediate marker for efficacy. NRG Oncology Radiation Therapy Oncology Group (RTOG) 0229 demonstrated the feasibility and efficacy of combining full-dose radiation (61.2 Gy) with chemotherapy followed by resection and chemotherapy. On the basis of that experience and evidence that EGFR antibodies are radiosensitizing, we explored adding panitumumab to CRT followed by resection and consolidation chemotherapy in locally advanced NSCLC with a primary end point of MNS. METHODS: Patients with resectable locally advanced NSCLC were eligible if deemed suitable for trimodality therapy before treatment. Surgeons were required to demonstrate expertise after CRT and adhere to specific management guidelines. Concurrent CRT consisted of weekly carboplatin (area under the curve = 2.0), paclitaxel (50 mg/m2), and 60 Gy of radiation therapy delivered in 30 fractions. There was a 2:1 randomization in favor of panitumumab at 2.5 mg/kg weekly for 6 weeks. The mediastinum was pathologically reassessed before or at the time of resection. Consolidation chemotherapy was weekly carboplatin (area under the curve = 6) and paclitaxel, 200 mg/m2 every 21 days for two courses. The study was designed to detect an improvement in MNS from 52% to 72%. With use of a 0.15 one-sided type 1 error and 80% power, 97 patients were needed. RESULTS: The study was opened in November 2010 and closed in August 2015 by the Data Monitoring Committee after 71 patients had been accrued for futility and excessive toxicity in the experimental arm. A total of 60 patients were eligible: 19 patients (86%) who received CRT and 29 (76%) who received CRT plus panitumumab and underwent an operation. With regard to postoperative toxicity, there were three grade 4 adverse events (13.6%) and no grade 5 adverse events (0%) among those who received CRT versus six grade 4 (15.8%) and four grade 5 adverse events (10.5%) among those who received CRT plus panitumumab. The MNS rates were 68.2% (95% confidence interval: 45.1-86.1) and 50.0% (95% confidence interval: 33.4-66.6) for CRT and CRT plus panitumumab, respectively (p = 0.95). CONCLUSION: The addition of panitumumab to CRT did not improve MNS. There was an unexpectedly high mortality rate in the panitumumab arm, although the relationship to panitumumab is unclear. The control arm had outcomes similar to those in NRG Oncology RTOG 0229.

An analysis of the treatment couch and control system dynamics for respiration-induced motion compensation.

Sophisticated methods for real-time motion compensation include using the linear accelerator, MLC, or treatment couch. To design such a couch, the required couch and control system dynamics need to be investigated. We used an existing treatment couch known as the Hexapod to gain insight into couch dynamics and an internal model controller to simulate feedback control of respiration-induced motion. The couch dynamics, described using time constants and dead times, were investigated using step inputs. The resulting data were modeled as first and second order systems with dead time. The couch was determined to have a linear response for step inputs < or = 1 cm. Motion data from 12 patients were obtained using a skin marker placed on the abdomen of the patient and the marker data were assumed to be an exact surrogate of tumor motion. The feedback system was modeled with the couch as a second-ordersystem and the controller as a first order system. The time constants of the couch and controller and the dead times were varied starting with parameters obtained from the Hexapod couch and the performance of the feedback system was evaluated. The resulting residual motion under feedback control was generally <0.3 cm when a fast enough couch was simulated.

A difference-matrix metaheuristic for intensity map segmentation in step-and-shoot IMRT delivery.

At an intermediate stage of radiation treatment planning for IMRT, most commercial treatment planning systems for IMRT generate intensity maps that describe the grid of beamlet intensities for each beam angle. Intensity map segmentation of the matrix of individual beamlet intensities into a set of MLC apertures and corresponding intensities is then required in order to produce an actual radiation delivery plan for clinical use. Mathematically, this is a very difficult combinatorial optimization problem, especially when mechanical limitations of the MLC lead to many constraints on aperture shape, and setup times for apertures make the number of apertures an important factor in overall treatment time. We have developed, implemented and tested on clinical cases a metaheuristic (that is, a method that provides a framework to guide the repeated application of another heuristic) that efficiently generates very high-quality (low aperture number) segmentations. Our computational results demonstrate that the number of beam apertures and monitor units in the treatment plans resulting from our approach is significantly smaller than the corresponding values for treatment plans generated by the heuristics embedded in a widely use commercial system. We also contrast the excellent results of our fast and robust metaheuristic with results from an 'exact' method, branch-and-cut, which attempts to construct optimal solutions, but, within clinically acceptable time limits, generally fails to produce good solutions, especially for intensity maps with more than five intensity levels. Finally, we show that in no instance is there a clinically significant change of quality associated with our more efficient plans.