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.

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.

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.

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.

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: leaf‐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 (2×2,3×3,4×4,6×6 cm2) of both 6 MV and 18 MV photons were compared to those independently measured by the Imaging and Radiation Oncology Core (IROC), Houston, TX. 6 MV 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.

PACS number(s): 87.55.D, 87.56.nk

Pre-Chemoradiotherapy FDG PET/CT cannot Identify Residual Metabolically-Active Volumes within Individual Esophageal Tumors

Objective

To study whether subvolumes with a high pre-chemoradiotherapy (CRT) FDG uptake could identify residual metabolically-active volumes (MAVs) post-CRT within individual esophageal tumors. Accurate identification will allow simultaneous integrated boost to these subvolumes at higher risk to improve clinical outcomes.

Methods

Twenty patients with esophageal cancer were treated with CRT plus surgery and underwent FDG PET/CT scans before and after CRT. The two scans were rigidly registered. Seven MAVs pre-CRT and four MAVs post-CRT within a tumor were defined with various SUV thresholds. The similarity and proximity between the MAVs pre-CRT and post-CRT were quantified with three metrics: fraction of post-CRT MAV included in pre-CRT MAV, volume overlap and centroid distance.

Results

Eight patients had no residual MAV. Six patients had local residual MAV (SUV ≥2.5 post-CRT) within or adjoining the original MAV (SUV ≥2.5 pre-CRT). On average, less than 65% of any post-CRT MAVs was included in any pre-CRT MAVs, with a low volume overlap <45%, and large centroid distance >8.6 mm. In general, subvolumes with higher FDG-uptake pre-CRT or post-CRT had lower volume overlap and larger centroid distance. Six patients had new distant MAVs that were determined to be inflammation from radiation therapy.

Conclusions

Pre-CRT PET/CT cannot reliably identify the residual MAVs within individual esophageal tumors. Simultaneous integrated boost to subvolumes with high FDG uptake pre-CRT may not be feasible.

Evaluation of template matching for tumor motion management with cine-MR images in lung cancer patients

PURPOSE

Accurate determination of tumor position is crucial for successful application of motion compensated radiotherapy in lung cancer patients. This study tested the performance of an automated template matching algorithm in tracking the tumor position on cine-MR images by examining the tracking error and further comparing the tracking error to the interoperator variability of three human reviewers.

METHODS

Cine-MR images of 12 lung cancer patients were analyzed. Tumor positions were determined both automatically with template matching and manually by a radiation oncologist and two additional reviewers trained by the radiation oncologist. Performance of the automated template matching was compared against the ground truth established by the radiation oncologist. Additionally, the tracking error of template matching, defined as the difference in the tumor positions determined with template matching and the ground truth, was investigated and compared to the interoperator variability for all patients in the anterior-posterior (AP) and superior-inferior (SI) directions, respectively.

RESULTS

The median tracking error for ten out of the 12 patients studied in both the AP and SI directions was less than 1 pixel (= 1.95 mm). Furthermore, the median tracking error for seven patients in the AP direction and nine patients in the SI direction was less than half a pixel (= 0.975 mm). The median tracking error was positively correlated with the tumor motion magnitude in both the AP (R = 0.55, p = 0.06) and SI (R = 0.67, p = 0.02) directions. Also, a strong correlation was observed between tracking error and interoperator variability (y = 0.26 + 1.25x, R = 0.84, p < 0.001) with the latter larger.

CONCLUSIONS

Results from this study indicate that the performance of template matching is comparable with or better than that of manual tumor localization. This study serves as preliminary investigations towards developing online motion tracking techniques for hybrid MRI-Linac systems. Accuracy of template matching makes it a suitable candidate to replace the labor intensive manual tumor localization for obtaining the ground truth when testing other motion management techniques.

Response assessment in locally advanced head and neck cancer based on RECIST and volume measurements using cone beam CT images

The purpose of this work was to find potential trends in RECIST measurements and volume regressions obtained from weekly cone-beam computed tomography images and to evaluate their relationship to clinical outcomes in locally advanced head and neck cancer. We examined thirty head and neck cancer patients who underwent a pre-treatment planning CT and weekly cone-beam computed tomography (CBCT) during the 5-7 week treatment period. The gross tumor volume (GTV) and lymph nodes were manually contoured on the treatment planning CT. The regions of interest enclosed by delineated contours were converted to binary masks and warped to weekly CBCT images using the 3D deformation field obtained by deformable image registration. The RECIST diameters and volumes were measured from these warped masks. Different predictor variables based on these measurements were calculated and correlated with clinical outcomes, based on a clinical exam and a PET imaging study. We found that there was substantial regression of the gross tumor volume over the treatment course (average gross tumor volume regression of 25%). Among the gross tumor volume predicators, it was found that the early regression of gross tumor volume showed a marginal statistical significance (p = 0.045) with complete response and non-complete response treatment outcomes. RECIST diameter measurements during treatment varied very little and did not correlate with clinical outcomes. We concluded that regression of the gross tumor volume obtained from weekly CBCT images is a promising predictor of clinical outcomes for head and neck patients. A larger sample is needed to confirm its statistical significance.

Predicting pathologic tumor response to chemoradiotherapy with histogram distances characterizing longitudinal changes in 18F-FDG uptake patterns

PURPOSE

A family of fluorine-18 ((18)F)-fluorodeoxyglucose ((18)F-FDG) positron-emission tomography (PET) features based on histogram distances is proposed for predicting pathologic tumor response to neoadjuvant chemoradiotherapy (CRT). These features describe the longitudinal change of FDG uptake distribution within a tumor.

METHODS

Twenty patients with esophageal cancer treated with CRT plus surgery were included in this study. All patients underwent PET/CT scans before (pre-) and after (post-) CRT. The two scans were first rigidly registered, and the original tumor sites were then manually delineated on the pre-PET/CT by an experienced nuclear medicine physician. Two histograms representing the FDG uptake distribution were extracted from the pre- and the registered post-PET images, respectively, both within the delineated tumor. Distances between the two histograms quantify longitudinal changes in FDG uptake distribution resulting from CRT, and thus are potential predictors of tumor response. A total of 19 histogram distances were examined and compared to both traditional PET response measures and Haralick texture features. Receiver operating characteristic analyses and Mann-Whitney U test were performed to assess their predictive ability.

RESULTS

Among all tested histogram distances, seven bin-to-bin and seven crossbin distances outperformed traditional PET response measures using maximum standardized uptake value (AUC = 0.70) or total lesion glycolysis (AUC = 0.80). The seven bin-to-bin distances were: L(2) distance (AUC = 0.84), χ(2) distance (AUC = 0.83), intersection distance (AUC = 0.82), cosine distance (AUC = 0.83), squared Euclidean distance (AUC = 0.83), L(1) distance (AUC = 0.82), and Jeffrey distance (AUC = 0.82). The seven crossbin distances were: quadratic-chi distance (AUC = 0.89), earth mover distance (AUC = 0.86), fast earth mover distance (AUC = 0.86), diffusion distance (AUC = 0.88), Kolmogorov-Smirnov distance (AUC = 0.88), quadratic form distance (AUC = 0.87), and match distance (AUC = 0.84). These crossbin histogram distance features showed slightly higher prediction accuracy than texture features on post-PET images.

CONCLUSIONS

The results suggest that longitudinal patterns in (18)F-FDG uptake characterized using histogram distances provide useful information for predicting the pathologic response of esophageal cancer to CRT.

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion

PURPOSE

To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods.

METHODS

Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥ 3 mm), and always (approximately once per minute).

RESULTS

Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively.

CONCLUSIONS

The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization.

Dosimetric comparison of patient setup strategies in stereotactic body radiation therapy for lung cancer

PURPOSE

In this work, the authors retrospectively compared the accumulated dose over the treatment course for stereotactic body radiation therapy (SBRT) of lung cancer for three patient setup strategies.

METHODS

Ten patients who underwent lung SBRT were selected for this study. At each fraction, patients were immobilized using a vacuum cushion and were CT scanned. Treatment plans were performed on the simulation CT. The planning target volume (PTV) was created by adding a 5-mm uniform margin to the internal target volume derived from the 4DCT. All plans were normalized such that 99% of the PTV received 60 Gy. The plan parameters were copied onto the daily CT images for dose recalculation under three setup scenarios: skin marker, bony structure, and soft tissue based alignments. The accumulated dose was calculated by summing the dose at each fraction along the trajectory of a voxel over the treatment course through deformable image registration of each CT with the planning CT. The accumulated doses were analyzed for the comparison of setup accuracy.

RESULTS

The tumor volume receiving 60 Gy was 91.7 ± 17.9%, 74.1 ± 39.1%, and 99.6 ± 1.3% for setup using skin marks, bony structures, and soft tissue, respectively. The isodose line covering 100% of the GTV was 55.5 ± 7.1, 42.1 ± 16.0, and 64.3 ± 7.1 Gy, respectively. The corresponding average biologically effective dose of the tumor was 237.3 ± 29.4, 207.4 ± 61.2, and 258.3 ± 17.7 Gy, respectively. The differences in lung biologically effective dose, mean dose, and V20 between the setup scenarios were insignificant.

CONCLUSIONS

The authors' results suggest that skin marks and bony structure are insufficient for aligning patients in lung SBRT. Soft tissue based alignment is needed to match the prescribed dose delivered to the tumors.

Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics

PURPOSE

To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer.

METHODS AND MATERIALS

This study included 20 patients who underwent trimodality therapy (CRT+surgery) and underwent 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]max, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC.

RESULTS

When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)-results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model.

CONCLUSIONS

The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.

Beam controlled arc therapy--a delivery concept for stationary targets

Volumetric modulated arc therapy (VMAT) presupposes that it is beneficial to deliver radiation from all beam angles as the gantry rotates, requiring the multi-leaf collimator to maintain continuity in shape from one angle to another. In turn, radiation from undesirable beam angles could compromise the dose distribution. In this work, we challenge the notion that the radiation beam must be held on as the gantry rotates around the patient. We propose a new approach for delivering intensity-modulated arc therapy, beam-controlled arc therapy (BCAT), during which the radiation beam is controlled on or off and the dose rate is modulated while the gantry rotates around the patient. We employ linear-programming-based dose optimization to each aperture weight, resulting in some zero weight apertures. During delivery, the radiation beam is held off at control points with zero weights as the MLC shape transits to the next non-zero weight shape. This was tested on ten head and neck cases. Plan quality and delivery efficiency were compared with VMAT. Improvements of up to 17% (p-value 0.001) and 57% (p-value 0.018) in organ-at-risk sparing and target dose uniformity, respectively, were achieved. Compared to the fixed number of apertures used in single-arc and double-arc VMAT, the BCAT used 109 and 175 apertures on average, respectively. The difference in total MUs for VMAT and BCAT plans was less than 4%. Plan quality improvement was confirmed after delivery with γ analysis resulting in over 99% agreement, or 4 in 1099 points that failed.

Dosimetric and geometric evaluation of a novel stereotactic radiotherapy device for breast cancer: the GammaPod™

PURPOSE

A dedicated stereotactic gamma irradiation device, the GammaPod™ from Xcision Medical Systems, was developed specifically to treat small breast cancers. This study presents the first evaluation of dosimetric and geometric characteristics from the initial prototype installed at University of Maryland Radiation Oncology Department.

METHODS

The GammaPod™ stereotactic radiotherapy device is an assembly of a hemi-spherical source carrier containing 36 (60)Co sources, a tungsten collimator, a dynamically controlled patient support table, and the breast immobilization system which also functions as a stereotactic frame. The source carrier contains the sources in six columns spaced longitudinally at 60° intervals and it rotates together with the variable-size collimator to form 36 noncoplanar, concentric arcs focused at the isocenter. The patient support table enables motion in three dimensions to position the patient tumor at the focal point of the irradiation. The table moves continuously in three cardinal dimensions during treatment to provide dynamic shaping of the dose distribution. The breast is immobilized using a breast cup applying a small negative pressure, where the immobilization cup is embedded with fiducials also functioning as the stereotactic frame for the breast. Geometric and dosimetric evaluations of the system as well as a protocol for absorbed dose calibration are provided. Dosimetric verifications of dynamically delivered patient plans are performed for seven patients using radiochromic films in hypothetical preop, postop, and target-in-target treatment scenarios.

RESULTS

Loaded with 36 (60)Co sources with cumulative activity of 4320 Ci, the prototype GammaPod™ unit delivers 5.31 Gy/min at the isocenter using the largest 2.5 cm diameter collimator. Due to the noncoplanar beam arrangement and dynamic dose shaping features, the GammaPod™ device is found to deliver uniform doses to targets with good conformity. The spatial accuracy of the device to locate the radiation isocenter is determined to be less than 1 mm. Single shot profiles with 2.5 cm collimator are measured with radiochromic film and found to be in good agreement with respect to the Monte Carlo based calculations (congruence of FWHM less than 1 mm). Dosimetric verifications corresponding to all hypothetical treatment plans corresponding to three target scenarios for each of the seven patients demonstrated good agreement with gamma index pass rates of better than 97% (99.0% ± 0.7%).

CONCLUSIONS

Dosimetric evaluation of the first GammaPod™ stereotactic breast radiotherapy unit was performed and the dosimetric and spatial accuracy of this novel technology is found to be feasible with respect to clinical radiotherapy standards. The observed level of agreement between the treatment planning system calculations and dosimetric measurements has confirmed that the system can deliver highly complex treatment plans with remarkable geometric and dosimetric accuracy.

A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning

An important element of radiation treatment planning for cancer therapy is the selection of beam angles (out of all possible coplanar and non-coplanar angles in relation to the patient) in order to maximize the delivery of radiation to the tumor site and minimize radiation damage to nearby organs-at-risk. This category of combinatorial optimization problem is particularly difficult because direct evaluation of the quality of treatment corresponding to any proposed selection of beams requires the solution of a large-scale dose optimization problem involving many thousands of variables that represent doses delivered to volume elements (voxels) in the patient. However, if the quality of angle sets can be accurately estimated without expensive computation, a large number of angle sets can be considered, increasing the likelihood of identifying a very high quality set. Using a computationally efficient surrogate beam set evaluation procedure based on single-beam data extracted from plans employing equallyspaced beams (eplans), we have developed a global search metaheuristic process based on the nested partitions framework for this combinatorial optimization problem. The surrogate scoring mechanism allows us to assess thousands of beam set samples within a clinically acceptable time frame. Tests on difficult clinical cases demonstrate that the beam sets obtained via our method are of superior quality.

Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy

PURPOSE

To extract and study comprehensive spatial-temporal (18)F-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography (PET) features for the prediction of pathologic tumor response to neoadjuvant chemoradiation therapy (CRT) in esophageal cancer.

METHODS AND MATERIALS

Twenty patients with esophageal cancer were treated with trimodal therapy (CRT plus surgery) and underwent [(18)F]FDG-PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The 2 scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold standardized uptake value (SUV) of ≥2.5, followed by manual editing. Comprehensive features were extracted to characterize SUV intensity distribution, spatial patterns (texture), tumor geometry, and associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC) value.

RESULTS

The best traditional response measure was decline in maximum SUV (SUVmax; AUC, 0.76). Two new intensity features, decline in mean SUV (SUVmean) and skewness, and 3 texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUC values ≥0.76. According to these features, a tumor was more likely to be a responder when the SUVmean decline was larger, when there were relatively fewer voxels with higher SUV values pre-CRT, or when [(18)F]FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessment alone.

CONCLUSION

Spatial-temporal [(18)F]FDG-PET features were found to be useful predictors of pathologic tumor response to neoadjuvant CRT in esophageal cancer.

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.

Mitigating errors in external respiratory surrogate-based models of tumor position

PURPOSE

To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position.

METHODS AND MATERIALS

Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor-surrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data.

RESULTS

The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor-surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares.

CONCLUSIONS

The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.

Incidence of changes in respiration-induced tumor motion and its relationship with respiratory surrogates during individual treatment fractions

PURPOSE

To determine how frequently (1) tumor motion and (2) the spatial relationship between tumor and respiratory surrogate markers change during a treatment fraction in lung and pancreas cancer patients.

METHODS AND MATERIALS

A Cyberknife Synchrony system radiographically localized the tumor and simultaneously tracked three respiratory surrogate markers fixed to a form-fitting vest. Data in 55 lung and 29 pancreas fractions were divided into successive 10-min blocks. Mean tumor positions and tumor position distributions were compared across 10-min blocks of data. Treatment margins were calculated from both 10 and 30 min of data. Partial least squares (PLS) regression models of tumor positions as a function of external surrogate marker positions were created from the first 10 min of data in each fraction; the incidence of significant PLS model degradation was used to assess changes in the spatial relationship between tumors and surrogate markers.

RESULTS

The absolute change in mean tumor position from first to third 10-min blocks was >5 mm in 13% and 7% of lung and pancreas cases, respectively. Superior-inferior and medial-lateral differences in mean tumor position were significantly associated with the lobe of lung. In 61% and 54% of lung and pancreas fractions, respectively, margins calculated from 30 min of data were larger than margins calculated from 10 min of data. The change in treatment margin magnitude for superior-inferior motion was >1 mm in 42% of lung and 45% of pancreas fractions. Significantly increasing tumor position prediction model error (mean ± standard deviation rates of change of 1.6 ± 2.5 mm per 10 min) over 30 min indicated tumor-surrogate relationship changes in 63% of fractions.

CONCLUSIONS

Both tumor motion and the relationship between tumor and respiratory surrogate displacements change in most treatment fractions for patient in-room time of 30 min.

Inferring positions of tumor and nodes in Stage III lung cancer from multiple anatomical surrogates using four-dimensional computed tomography

PURPOSE

To investigate the feasibility of modeling Stage III lung cancer tumor and node positions from anatomical surrogates.

METHODS AND MATERIALS

To localize their centroids, the primary tumor and lymph nodes from 16 Stage III lung cancer patients were contoured in 10 equal-phase planning four-dimensional (4D) computed tomography (CT) image sets. The centroids of anatomical respiratory surrogates (carina, xyphoid, nipples, mid-sternum) in each image set were also localized. The correlations between target and surrogate positions were determined, and ordinary least-squares (OLS) and partial least-squares (PLS) regression models based on a subset of respiratory phases (three to eight randomly selected) were created to predict the target positions in the remaining images. The three-phase image sets that provided the best predictive information were used to create models based on either the carina alone or all surrogates.

RESULTS

The surrogate most correlated with target motion varied widely. Depending on the number of phases used to build the models, mean OLS and PLS errors were 1.0 to 1.4 mm and 0.8 to 1.0 mm, respectively. Models trained on the 0%, 40%, and 80% respiration phases had mean (+/- standard deviation) PLS errors of 0.8 +/- 0.5 mm and 1.1 +/- 1.1 mm for models based on all surrogates and carina alone, respectively. For target coordinates with motion >5 mm, the mean three-phase PLS error based on all surrogates was 1.1 mm.

CONCLUSIONS

Our results establish the feasibility of inferring primary tumor and nodal motion from anatomical surrogates in 4D CT scans of Stage III lung cancer. Using inferential modeling to decrease the processing time of 4D CT scans may facilitate incorporation of patient-specific treatment margins.

The minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT planning

IMRT treatment planning requires consideration of two competing objectives: achieving the required amount of radiation for the planning target volume and minimizing the amount of radiation delivered to all other tissues. It is important for planners to understand the tradeoff between competing factors so that the time-consuming human interaction loop (plan-evaluate-modify) can be eliminated. Treatment-plan-surface models have been proposed as a decision support tool to aid treatment planners and clinicians in choosing between rival treatment plans in a multi-plan environment. In this paper, an empirical approach is introduced to determine the minimum number of treatment plans (minimum knowledge base) required to build accurate representations of the IMRT plan surface in order to predict organ-at-risk (OAR) dose-volume (DV) levels and complications as a function of input DV constraint settings corresponding to all involved OARs in the plan. We have tested our approach on five head and neck patients and five whole pelvis/prostate patients. Our results suggest that approximately 30 plans were sufficient to predict DV levels with less than 3% relative error in both head and neck and whole pelvis/prostate cases. In addition, approximately 30-60 plans were sufficient to predict saliva flow rate with less than 2% relative error and to classify rectal bleeding with an accuracy of 90%.

A two-stage sequential linear programming approach to IMRT dose optimization

The conventional IMRT planning process involves two stages in which the first stage consists of fast but approximate idealized pencil beam dose calculations and dose optimization and the second stage consists of discretization of the intensity maps followed by intensity map segmentation and a more accurate final dose calculation corresponding to physical beam apertures. Consequently, there can be differences between the presumed dose distribution corresponding to pencil beam calculations and optimization and a more accurately computed dose distribution corresponding to beam segments that takes into account collimator-specific effects. IMRT optimization is computationally expensive and has therefore led to the use of heuristic (e.g., simulated annealing and genetic algorithms) approaches that do not encompass a global view of the solution space. We modify the traditional two-stage IMRT optimization process by augmenting the second stage via an accurate Monte Carlo-based kernel-superposition dose calculations corresponding to beam apertures combined with an exact mathematical programming-based sequential optimization approach that uses linear programming (SLP). Our approach was tested on three challenging clinical test cases with multileaf collimator constraints corresponding to two vendors. We compared our approach to the conventional IMRT planning approach, a direct-aperture approach and a segment weight optimization approach. Our results in all three cases indicate that the SLP approach outperformed the other approaches, achieving superior critical structure sparing. Convergence of our approach is also demonstrated. Finally, our approach has also been integrated with a commercial treatment planning system and may be utilized clinically.

Towards respiration management in radiation treatment of lung tumors: transferring regions of interest from planning CT to kilovoltage X-ray images

Tracking of lung tumors is imperative for improved radiotherapy treatment. However, the motion of the thoracic organs makes it a complicated task. 4D CT images acquired prior to treatment provide valuable information regarding the motion of organs and tumor, since it is manually annotated. In order to track tumors using treatment-day X-ray images (kV images), we need to find the correspondence with CT images so that projection of tumor region of interest will provide a good estimate about the position of the tumor on the X-ray image. In this study, we propose a method to estimate the alignment and respiration phase corresponding to X-ray images using 4D CT data. Our approach generates Digitally Reconstructed Radiographs (DRRs) using bilateral filter smoothing and computes rigid registration with kV images since the position and orientation of patient might differ between CT and treatment-day image acquisition processes. Instead of using landmark points, our registration method makes use of Kernel Density Estimation over the edges that are not affected much by respiration. To estimate the phase of X-ray, we apply template matching techniques between the lung regions of X-ray and registered DRRs. Our approach gives accurate results for rigid registration and provides a starting point to track tumors using the X-ray images during the treatment.

Moderate predictive value of demographic and behavioral characteristics for a diagnosis of HPV16-positive and HPV16-negative head and neck cancer

Patients with HPV-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) are significantly different with regard to sociodemographic and behavioral characteristics that clinicians may use to assume tumor HPV status. Machine learning methods were used to evaluate the predictive value of patient characteristics and laboratory biomarkers of HPV exposure for a diagnosis of HPV16-positive HNSCC compared to in situ hybridization, the current gold-standard. Models that used a combination of demographic characteristics such as age, tobacco use, gender, and race had only moderate predictive value for tumor HPV status among all patients with HNSCC (positive predictive value [PPV]=75%, negative predictive value [NPV]=68%) or when limited to oropharynx cancer patients (PPV=55%, NPV=65%) and thus included a sizeable number of false positive and false negative predictions. Prediction was not improved by the addition of other demographic or behavioral factors (sexual behavior, income, education) or biomarkers of HPV16 exposure (L1, E6/7 antibodies or DNA in oral exfoliated cells). Patient demographic and behavioral characteristics as well as HPV biomarkers are not an accurate substitute for clinical testing of tumor HPV status.

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.

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.

Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework

PURPOSE

To predict organ-at-risk (OAR) complications as a function of dose-volume (DV) constraint settings without explicit plan computation in a multiplan intensity-modulated radiotherapy (IMRT) framework.

METHODS AND MATERIALS

Several plans were generated by varying the DV constraints (input features) on the OARs (multiplan framework), and the DV levels achieved by the OARs in the plans (plan properties) were modeled as a function of the imposed DV constraint settings. OAR complications were then predicted for each of the plans by using the imposed DV constraints alone (features) or in combination with modeled DV levels (plan properties) as input to machine learning (ML) algorithms. These ML approaches were used to model two OAR complications after head-and-neck and prostate IMRT: xerostomia, and Grade 2 rectal bleeding. Two-fold cross-validation was used for model verification and mean errors are reported.

RESULTS

Errors for modeling the achieved DV values as a function of constraint settings were 0-6%. In the head-and-neck case, the mean absolute prediction error of the saliva flow rate normalized to the pretreatment saliva flow rate was 0.42% with a 95% confidence interval of (0.41-0.43%). In the prostate case, an average prediction accuracy of 97.04% with a 95% confidence interval of (96.67-97.41%) was achieved for Grade 2 rectal bleeding complications.

CONCLUSIONS

ML can be used for predicting OAR complications during treatment planning allowing for alternative DV constraint settings to be assessed within the planning framework.

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.

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.

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.

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.

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.

Gated CT imaging using a free-breathing respiration signal from flow-volume spirometry

Respiration-induced tumor motion is known to cause artifacts on free-breathing spiral CT images used in treatment planning. This leads to inaccurate delineation of target volumes on planning CT images. Flow-volume spirometry has been used previously for breath-holds during CT scans and radiation treatments using the active breathing control (ABC) system. We have developed a prototype by extending the flow-volume spirometer device to obtain gated CT scans using a PQ 5000 single-slice CT scanner. To test our prototype, we designed motion phantoms to compare image quality obtained with and without gated CT scan acquisition. Spiral and axial (nongated and gated) CT scans were obtained of phantoms with motion periods of 3-5 s and amplitudes of 0.5-2 cm. Errors observed in the volume estimate of these structures were as much as 30% with moving phantoms during CT simulation. Application of motion-gated CT with active breathing control reduced these errors to within 5%. Motion-gated CT was then implemented in patients and the results are presented for two clinical cases: lung and abdomen. In each case, gated scans were acquired at end-inhalation, end-exhalation in addition to a conventional free-breathing (nongated) scan. The gated CT scans revealed reduced artifacts compared with the conventional free-breathing scan. Differences of up to 20% in the volume of the structures were observed between gated and free-breathing scans. A comparison of the overlap of structures between the gated and free-breathing scans revealed misalignment of the structures. These results demonstrate the ability of flow-volume spirometry to reduce errors in target volumes via gating during CT imaging.

Using a photon phase-space source for convolution/superposition dose calculations in radiation therapy

For a given linac design, the dosimetric characteristics of a photon beam are determined uniquely by the energy and radial distributions of the electron beam striking the x-ray target. However, in the usual commissioning of a beam from measured data, a large number of variables can be independently tuned, making it difficult to derive a unique and self-consistent beam model. For example, the measured dosimetric penumbra in water may be attributed in various proportions to the lateral secondary electron range, the focal spot size and the transmission through the tips of a non-divergent collimator; the head-scatter component in the tails of the transverse profiles may not be easy to resolve from phantom scatter and head leakage; and the head-scatter tails corresponding to a certain extra-focal source model may not agree self-consistently with in-air output factors measured on the central axis. To reduce the number of adjustable variables in beam modelling, we replace the focal and extra-focal sources with a single phase-space plane scored just above the highest adjustable collimator in a EGS/BEAM simulation of the linac. The phase-space plane is then used as photon source in a stochastic convolution/superposition dose engine. A photon sampled from the uncollimated phase-space plane is first propagated through an arbitrary collimator arrangement and then interacted in the simulation phantom. Energy deposition kernel rays are then randomly issued from the interaction points and dose is deposited along these rays. The electrons in the phase-space file are used to account for electron contamination. 6 MV and 18 MV photon beams from an Elekta SL linac are used as representative examples. Except for small corrections for monitor backscatter and collimator forward scatter for large field sizes (<0.5% with <20 x 20 cm2 field size), we found that the use of a single phase-space photon source provides accurate and self-consistent results for both relative and absolute dose calculations.

Real-time intra-fraction-motion tracking using the treatment couch: a feasibility study

Significant differences between planned and delivered treatments may occur due to respiration-induced tumour motion, leading to underdosing of parts of the tumour and overdosing of parts of the surrounding critical structures. Existing methods proposed to counter tumour motion include breath-holds, gating and MLC-based tracking. Breath-holds and gating techniques increase treatment time considerably, whereas MLC-based tracking is limited to two dimensions. We present an alternative solution in which a robotic couch moves in real time in response to organ motion. To demonstrate proof-of-principle, we constructed a miniature adaptive couch model consisting of two movable platforms that simulate tumour motion and couch motion, respectively. These platforms were connected via an electronic feedback loop so that the bottom platform responded to the motion of the top platform. We tested our model with a seven-field step-and-shoot delivery case in which we performed three film-based experiments: (1) static geometry, (2) phantom-only motion and (3) phantom motion with simulated couch motion. Our measurements demonstrate that the miniature couch was able to compensate for phantom motion to the extent that the dose distributions were practically indistinguishable from those in static geometry. Motivated by this initial success, we investigated a real-time couch compensation system consisting of a stereoscopic infra-red camera system interfaced to a robotic couch known as the Hexapod, which responds in real time to any change in position detected by the cameras. Optical reflectors placed on a solid water phantom were used as surrogates for motion. We tested the effectiveness of couch-based motion compensation for fixed fields and a dynamic arc delivery cases. Due to hardware limitations, we performed film-based experiments (1), (2) and (3), with the robotic couch at a phantom motion period and dose rate of 16 s and 100 MU min(-1), respectively. Analysis of film measurements showed near-equivalent dose distributions (<or=2 mm agreement of corresponding isodose lines) for static geometry and motion-synchronized real-time robotic couch tracking-based radiation delivery.

A stochastic convolution/superposition method with isocenter sampling to evaluate intrafraction motion effects in IMRT

Current methods to calculate dose distributions with organ motion can be broadly classified as "dose convolution" and "fluence convolution" methods. In the former, a static dose distribution is convolved with the probability distribution function (PDF) that characterizes the motion. However, artifacts are produced near the surface and around inhomogeneities because the method assumes shift invariance. Fluence convolution avoids these artifacts by convolving the PDF with the incident fluence instead of the patient dose. In this paper we present an alternative method that improves the accuracy, generality as well as the speed of dose calculation with organ motion. The algorithm starts by sampling an isocenter point from a parametrically defined space curve corresponding to the patient-specific motion trajectory. Then a photon is sampled in the linac head and propagated through the three-dimensional (3-D) collimator structure corresponding to a particular MLC segment chosen randomly from the planned IMRT leaf sequence. The photon is then made to interact at a point in the CT-based simulation phantom. Randomly sampled monoenergetic kernel rays issued from this point are then made to deposit energy in the voxels. Our method explicitly accounts for MLC-specific effects (spectral hardening, tongue-and-groove, head scatter) as well as changes in SSD with isocentric displacement, assuming that the body moves rigidly with the isocenter. Since the positions are randomly sampled from a continuum, there is no motion discretization, and the computation takes no more time than a static calculation. To validate our method, we obtained ten separate film measurements of an IMRT plan delivered on a phantom moving sinusoidally, with each fraction starting with a random phase. For 2 cm motion amplitude, we found that a ten-fraction average of the film measurements gave an agreement with the calculated infinite fraction average to within 2 mm in the isodose curves. The results also corroborate the existing notion that the interfraction dose variability due to the interplay between the MLC motion and breathing motion averages out over typical multifraction treatments. Simulation with motion waveforms more representative of real breathing indicate that the motion can produce penumbral spreading asymmetric about the static dose distributions. Such calculations can help a clinician decide to use, for example, a larger margin in the superior direction than in the inferior direction. In the paper we demonstrate that a 15 min run on a single CPU can readily illustrate the effect of a patient-specific breathing waveform, and can guide the physician in making informed decisions about margin expansion and dose escalation.

Feasibility of dose escalation using intensity-modulated radiotherapy in posthysterectomy cervical carcinoma

PURPOSE

To evaluate retrospectively the utility of intensity-modulated radiotherapy (IMRT) in reducing the volume of normal tissues receiving radiation at varying dose levels when the female pelvis after hysterectomy is treated to doses of 50.4 Gy and 54 Gy.

METHODS AND MATERIALS

Computed tomography scans from 10 patients who had previously undergone conventional postoperative RT were selected. The clinical tumor volume (vaginal apex and iliac nodes) and organs at risk were contoured. Margins were added to generate the planning tumor volume. The Pinnacle and Corvus planning systems were used to develop conventional and IMRT plans, respectively. Conventional four-field plans were prescribed to deliver 45 Gy (4F(45 Gy)) or 50.4 Gy; eight-field IMRT plans were prescribed to deliver 50.4 Gy (IMRT(50.4 Gy)) or 54 Gy (IMRT(54 Gy)) to the planning tumor volume. All plans were normalized so that > or =97% of the planning tumor volume received the prescribed dose. Student's t test was used to compare the volumes of organs at risk receiving the same doses with different plans.

RESULTS

The mean volume of bowel receiving > or =45 Gy was lower with the IMRT(50.4 Gy) (33% lower) and IMRT(54 Gy) (18% lower) plans than with the 4F(45 Gy) plan. The mean volume of rectum receiving > or =45 Gy or > or =50 Gy was also significantly reduced with the IMRT plans despite an escalation of the prescribed dose from 45 Gy with the conventional plans to 54 Gy with IMRT. The mean volume of bladder treated to 45 Gy was the same or slightly lower with the IMRT(50.4 Gy) and IMRT(54 Gy) plans compared with the 4F(45 Gy) plan. Compared with the 4F(45 Gy) plan, the IMRT(50.4 Gy) plan resulted in a smaller volume of bowel receiving 35-45 Gy and a larger volume of bowel receiving 50-55 Gy. Compared with the 4F(45 Gy) plan, the IMRT(54 Gy) plan resulted in smaller volumes of bowel receiving 45-50 Gy; however, small volumes of bowel received 55-60 Gy with the IMRT plan.

CONCLUSION

Intensity-modulated RT may permit an increase in the radiation dose that can safely be delivered to the central pelvis and pelvic lymph nodes after hysterectomy. However, dose-volume calculations using individual CT scans do not account for internal organ motion. Detailed data concerning the relationships among radiation dose, treatment volume, and treatment effects are lacking, and prospective studies of pelvic IMRT are needed to determine the safety and efficacy of this treatment.

Selection of beam orientations in intensity-modulated radiation therapy using single-beam indices and integer programming

While the process of IMRT planning involves optimization of the dose distribution, the procedure for selecting the beam inputs for this process continues to be largely trial-and-error. We have developed an integer programming (IP) optimization method to optimize beam orientation using mean organ-at-risk (MOD) data from single-beam plans. Two test cases were selected in which one organ-at-risk (OAR) and four OARs were simulated, respectively, along with a PTV. Beam orientation space was discretized in 10 degrees increments. For each beam orientation, a single-beam plan without intensity modulation and without constraints on OAR dose was generated and normalized to yield a mean PTV dose of 2 Gy and the corresponding MOD was calculated. The degree of OAR sparing was related to the average OAR MODs resulting from the beam orientations utilized with improvements of up to 10% at some dose levels. On the other hand, OAR DVHs in the IMRT plans were insensitive to beam numbers (in the 6-9 range) for similar average single-beam MODs. These MOD data were input to an IP optimization process, which then selected specified numbers of beam angles as inputs to a treatment planning system. Our results show that sets of beam angles with lower average single-beam MODs produce IMRT plans with better OAR sparing than manually selected beam angles. To optimize beam orientations, weights were assigned to each OAR following MOD input to the IP which was subsequently solved using the branch-and-cut algorithm. Seven-beam orientations obtained from solving the IP were applied to the test case with four OARs and the resulting plan with a dose prescription of 63 Gy was compared with an equi-spaced beam plan. The IP selected beams produced dose-volume improvements of up to 40% for OARs proximal to the PTV. Further improvement in the DVH can be obtained by increasing the weights assigned to these OARs but at the expense of the remaining OARs.

Dose-volume conundrum for response of prostate cancer to brachytherapy: summary dosimetric measures and their relationship to tumor control probability

PURPOSE

Although it is known that brachytherapy dose distributions are highly heterogeneous, the effect of particular dose distribution patterns on tumor control probability (TCP) is unknown. It is unlikely that clinical results will throw light on the question in the near future, given the long follow-up and detailed dosimetry required for each patient. We used detailed dose distribution data from 50 patients combined with radiobiologic parameters consistent with what is known about TCP curves for prostate cancer to study the changes in TCP that accompany gross dosimetric measures and particular dosing irregularities (e.g., moderate underdosing of large volumes vs. extreme underdosing of small volumes).

METHODS AND MATERIALS

For each of the 50 patients with organ-confined prostate cancer who had undergone 125I prostate implants alone at our clinic, postimplant CT scans were obtained approximately 1 month after implantation. Dose distribution information was obtained from postimplant dosimetry. The percentage of the prostate volume receiving a specified dose was recorded from the respective differential dose-volume histograms in 10-Gy bins. In addition, the percentage of prostate volume underdosed at varying fractions of the prescription dose were determined, as was the minimal prostate dose. The log-normal distributions of the radiobiologic parameters [ln(initial clonogen number), alpha, and alpha/beta] were adjusted so that the predicted population parameters (steepness and location) of the dose-response curves for external beam radiotherapy agreed with the published estimates. The variability in the dose-volume details was increased by scaling the dose distributions by factors ranging from 0.7 to 1.5, thereby simulating, for each of the patients, nine new patients with different total doses but identical relative distributions of the dose over the voxels. Radiobiologic variability between the selected dose distributions was then removed by averaging >50 randomly chosen sets of radiobiologic parameters from the log-normal distributions to estimate the TCP for each of the dose distributions, giving some insight into the TCP variations with conventional dosimetric indexes and different patterns of underdosing.

RESULTS

Using the 450 dose distributions created by expanding the 50-patient data set, the volume of the prostate that was extremely underdosed (between 50% and 70% of the prescription dose) was related to the volume that was moderately underdosed (between 80% and 100% of the prescription dose). We found that the individual TCP is greatly dependent on the inhomogeneous dose distribution and the dosimetric indexes, such as the volume of prostate receiving 100% of the prescribed dose (V100) and the maximal dose received by 90% of the prostate volume (D90), which, by themselves, are not always accurate predictors of control probabilities. In a multivariate analysis of the dependence of TCP on these parameters (V100, D90, minimal dose, and moderately and severely underdosed volumes), only D90 and the minimal dose were statistically significant. Generally speaking, however, a lower minimal dose means a lower TCP.

CONCLUSION

The work described here was an hypothesis-generating study. Our results showed that even if the V100 and D90 are nearly identical for 2 patients, there can be (and frequently are) significant differences in the dose distributions in the subvolumes of the prostate. Under simulated dose-response conditions (i.e., with variations in the dose distribution), the D90 and minimal dose significantly affected the TCP but the V100 and the volumes moderately or severely underdosed did not. In general, one must consider the totality of the dose distribution to evaluate the dosimetric quality of a low-dose-rate prostate implant. TCP is not a monotonic function of extreme or moderate underdosing. In some instances, extreme underdosing of relatively small volumes may result in a greater TCP than moderate underdosing of relatively large volumes and vice versa.