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.

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 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.

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.

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.

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.

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.

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.

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.

Audio-visual biofeedback does not improve the reliability of target delineation using maximum intensity projection in 4-dimensional computed tomography radiation therapy planning.

PURPOSE: To investigate whether coaching patients' breathing would improve the match between ITVMIP (internal target volume generated by contouring in the maximum intensity projection scan) and ITV10 (generated by combining the gross tumor volumes contoured in 10 phases of a 4-dimensional CT [4DCT] scan). METHODS AND MATERIALS: Eight patients with a thoracic tumor and 5 patients with an abdominal tumor were included in an institutional review board-approved prospective study. Patients underwent 3 4DCT scans with: (1) free breathing (FB); (2) coaching using audio-visual (AV) biofeedback via the Real-Time Position Management system; and (3) coaching via a spirometer system (Active Breathing Coordinator or ABC). One physician contoured all scans to generate the ITV10 and ITVMIP. The match between ITVMIP and ITV10 was quantitatively assessed with volume ratio, centroid distance, root mean squared distance, and overlap/Dice coefficient. We investigated whether coaching (AV or ABC) or uniform expansions (1, 2, 3, or 5 mm) of ITVMIP improved the match. RESULTS: Although both AV and ABC coaching techniques improved frequency reproducibility and ABC improved displacement regularity, neither improved the match between ITVMIP and ITV10 over FB. On average, ITVMIP underestimated ITV10 by 19%, 19%, and 21%, with centroid distance of 1.9, 2.3, and 1.7 mm and Dice coefficient of 0.87, 0.86, and 0.88 for FB, AV, and ABC, respectively. Separate analyses indicated a better match for lung cancers or tumors not adjacent to high-intensity tissues. Uniform expansions of ITVMIP did not correct for the mismatch between ITVMIP and ITV10. CONCLUSIONS: In this pilot study, audio-visual biofeedback did not improve the match between ITVMIP and ITV10. In general, ITVMIP should be limited to lung cancers, and modification of ITVMIP in each phase of the 4DCT data set is recommended.

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.

Evaluation of fractional regional ventilation using 4D-CT and effects of breathing maneuvers on ventilation.

PURPOSE: Current implementations of methods based on Hounsfield units to evaluate regional lung ventilation do not directly incorporate tissue-based mass changes that occur over the respiratory cycle. To overcome this, we developed a 4-dimensional computed tomography (4D-CT)-based technique to evaluate fractional regional ventilation (FRV) that uses an individualized ratio of tidal volume to end-expiratory lung volume for each voxel. We further evaluated the effect of different breathing maneuvers on regional ventilation. The results from this work will help elucidate the relationship between global and regional lung function. METHODS AND MATERIALS: Eight patients underwent 3 sets of 4D-CT scans during 1 session using free-breathing, audiovisual guidance, and active breathing control. FRV was estimated using a density-based algorithm with mass correction. Internal validation between global and regional ventilation was performed by use of the imaging data collected during the use of active breathing control. The impact of breathing maneuvers on FRV was evaluated comparing the tidal volume from 3 breathing methods. RESULTS: Internal validation through comparison between the global and regional changes in ventilation revealed a strong linear correlation (slope of 1.01, R2 of 0.97) between the measured global lung volume and the regional lung volume calculated by use of the "mass corrected" FRV. A linear relationship was established between the tidal volume measured with the automated breathing control system and FRV based on 4D-CT imaging. Consistently larger breathing volumes were observed when coached breathing techniques were used. CONCLUSIONS: The technique presented improves density-based evaluation of lung ventilation and establishes a link between global and regional lung ventilation volumes. Furthermore, the results obtained are comparable with those of other techniques of functional evaluation such as spirometry and hyperpolarized-gas magnetic resonance imaging. These results were demonstrated on retrospective analysis of patient data, and further research using prospective data is under way to validate this technique against established clinical tests.

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.

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.

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.