W D' Souza

An evaluation of planning techniques for stereotactic body radiation therapy in lung tumors

PURPOSE To evaluate four planning techniques for stereotactic body radiation therapy (SBRT) in lung tumors. METHODS AND MATERIALS Four SBRT plans were performed for 12 patients with stage I/II non-small-cell lung cancer under the following conditions: (1) conventional margins on free-breathing CT (plan 1), (2) generation of an internal target volume (ITV) using 4DCT with beam delivery under free-breathing conditions (plan 2), (3) gating at end-exhale (plan 3), and (4) gating at end-inhale (plan 4). Planning was performed following the RTOG 0236 protocol with a prescription dose of 54 Gy (3 fractions). For each plan 4D dose was calculated using deformable-image registration. RESULTS There was no significant difference in tumor dose delivered by the 4 plans. However, compared with plan 1, plans 2-4 reduced total lung BED by 1.9+/-1.2, 3.1+/-1.6 and 3.5+/-2.1 Gy, reduced mean lung dose by 0.8+/-0.5, 1.5+/-0.8, and 1.6+/-1.0 Gy, reduced V20 by 1.5+/-1.0%, 2.7+/-1.4%, and 2.8+/-1.8%, respectively, with p<0.01. Compared with plan 2, plans 3-4 reduced lung BED by 1.2+/-1.0 and 1.6+/-1.5 Gy, reduced mean lung dose by 0.6+/-0.5 and 0.8+/-0.7 Gy, reduced V20 by 1.2+/-1.1% and 1.3+/-1.5%, respectively, with p<0.01. The differences in lung BED, mean dose and V20 of plan 4 compared with plan 3 were insignificant. CONCLUSIONS Tumor dose coverage was statistically insignificant between all plans. However, compared with plan 1, plans 2-4 significantly reduced lung doses. Compared with plan 2, plan 3-4 also reduced lung toxicity. The difference in lung doses between plan 3 and plan 4 was not significant.

Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches

Radiation therapy is extensively used to treat a wide range of cancers. Due to the increasing complexities of delivery mechanisms, and the improved imaging devices that allow more accurate determination of cancer location, determination of high quality treatment plans via trial-and-error methods is impractical and computer optimization approaches to planning are becoming more critical and more difficult.

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.

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 Daisnes 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.

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.

Individually optimized contrast-enhanced 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinoma.

PURPOSE To develop an individually optimized contrast-enhanced (CE) 4D-computed tomography (CT) for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA). METHODS Ten PDA patients were enrolled. Each underwent three CT scans: a 4D-CT immediately following a CE 3D-CT and an individually optimized CE 4D-CT using test injection. Three physicians contoured the tumor and pancreatic tissues. Image quality scores, tumor volume, motion, tumor-to-pancreas contrast, and contrast-to-noise ratio (CNR) were compared in the three CTs. Interobserver variations were also evaluated in contouring the tumor using simultaneous truth and performance level estimation. RESULTS Average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, respectively; P = 0.082), and both were significantly better than that for 4D-CT (2.6, P < 0.001). Tumor-to-pancreas contrast results were comparable in CE 3D-CT and CE 4D-CT (15.5 and 16.7 Hounsfield units (HU), respectively; P = 0.21), and the latter was significantly higher than in 4D-CT (9.2 HU, P = 0.001). Image noise in CE 3D-CT (12.5 HU) was significantly lower than in CE 4D-CT (22.1 HU, P = 0.013) and 4D-CT (19.4 HU, P = 0.009). CNRs were comparable in CE 3D-CT and CE 4D-CT (1.4 and 0.8, respectively; P = 0.42), and both were significantly better in 4D-CT (0.6, P = 0.008 and 0.014). Mean tumor volumes were significantly smaller in CE 3D-CT (29.8 cm3, P = 0.03) and CE 4D-CT (22.8 cm3, P = 0.01) than in 4D-CT (42.0 cm3). Mean tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, P = 0.17). Interobserver variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%, respectively) and were worse for 4D-CT (55.6%) than CE 3D-CT. CONCLUSIONS CE 4D-CT demonstrated characteristics comparable to CE 3D-CT, with high potential for simultaneously delineating the tumor and quantifying tumor motion with a single scan.

Individually optimized uniform contrast enhancement in CT angiography for the diagnosis of pulmonary thromboembolic disease—A simulation study

PURPOSE To improve the diagnostic quality of CT pulmonary angiography (CTPA) by individually optimizing a biphasic contrast injection function to achieve targeted uniform contrast enhancement. To compare the results against a previously reported discrete Fourier transform (DFT) approach. METHODS This simulation study used the CTPA datasets of 27 consecutive patients with pulmonary thromboembolic disease (PE). An optimization approach was developed consisting of (1) computation of the impulse enhancement function (IEF) based on a test bolus scan, and (2) optimization of a biphasic contrast injection function using the IEF in order to achieve targeted uniform enhancement. The injection rates and durations of a biphasic contrast injection function are optimized by minimizing the difference between the resulting contrast enhancement curve and the targeted uniform enhancement curve, while conforming to the clinical constraints of injection rate and total contrast volume. The total contrast volume was limited first to the clinical standard of 65 ml, and then to the same amount used in the DFT approach for comparison. The optimization approach and the DFT approach were compared in terms of the root mean square error (RMSE) and total contrast volume used. RESULTS When the total contrast volume was limited to 65 ml, the optimization approach produced significantly better contrast enhancement (closer to the targeted uniform contrast enhancement) than the DFT approach (RMSE 17 HU vs 56 HU, p < 0.00001). On average, the optimization approach used 63 ml contrast, while the DFT approach used 50 ml with four patients exceeding 65 ml. When equivalent total contrast volume was used for individual patient, the optimization approach still generated significantly better contrast enhancement (RMSE 44 HU vs 56 HU, p < 0.01). Constraints for the injection function could be easily accommodated into the optimization process when searching for the optimal biphasic injection function. CONCLUSIONS The optimization approach generated individually optimized biphasic injection functions yielding significantly better contrast enhancement compared to the DFT approach. This new approach has the potential to improve the diagnostic quality of CTPA for PE.

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.

A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging

Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI, K t r a n s using dynamic contrast-enhanced MRI, and 3-dimensional-MR spectroscopy. A pathologist reviewed all 223 specimens and marked cancerous regions on each and graded them with Gleason scores, which served as the ground truth to validate our prediction model. In cancer aggressiveness prediction, the average area under the receiver operating characteristic curve (AUC) value was 0.73 with 95% confidence interval (0.72-0.74) and the average sensitivity and specificity were 0.72 (0.71-0.73) and 0.73 (0.71-0.75), respectively. For the cancer detection model, the average AUC value was 0.68 (0.66-0.70) and the average sensitivity and specificity were 0.73 (0.70-0.77) and 0.62 (0.60-0.68), respectively. Our method included capability to handle class imbalance using adaptive boosting with random undersampling. In addition, our method was noninvasive and allowed for nonsubjective disease characterization, which provided physician information to make personalized treatment decision.

SU-E-T-39: A Logistic Function-Based Model to Predict Organ-At-Risk (OAR) DVH in IMRT Treatment Planning

Purpose: To investigate the feasibility of a logistic function-based model to predict organ-at-risk (OAR) DVH for IMRT planning. The predicted DVHs are compared to achieved DVHs by expert treatment planners. Methods: A logistic function is used to model the OAR dose-gradient function. This function describes the percentage of the prescription dose as a function of the normal distance to PTV surface. The slope of dose-gradient function is function of relative spatial orientation of the PTV and OARs. The OAR DVH is calculated using the OAR dose-gradient function assuming that the dose is same for voxels with same normal distance to PTV. Ten previously planned prostate IMRT plans were selected to build the model, and the following plan parameters were calculated as possible features to the model: the PTV maximum/minimum dose, PTV volume, bladder/rectum volume in the radiation field, percentage of bladder/rectum overlapping with PTV, and the distance between the bladder/rectum centroid and PTV. The bladder/rectum dose-gradient function was modeled and applied on 10 additional test cases, and the predicted and achieved clinical bladder/rectum DVHs were compared: V70 (percentage of volume receiving 70Gy and above), V65, V60, V55, V50, V45, V40. Results: The following parameters were selected as model features: PTV volume, and distance of centroid of rectum/bladder to PTV. The model was tested with 10 additional patients. For bladder, the absolute difference (mean±standard deviation) between predicted and clinical DVHs is V70=−0.3±3.2, V65=−0.8±3.9, V60=1.5±4.3, V55=1.7±5.3, V50=−0.6±6.4, V45=0.6±6.5, and V40=0.9±5.7, the correlation coefficient is 0.96; for rectum, the difference is V70=1.5±3.8, V65=1.2±4.2, V60=−0.1±5.3, V55=1.0±6.6, V50=1.6±8.7, V45=1.9±9.8, and V40=1.5±10.1, and the correlation coefficient is 0.87. Conclusion: The OAR DVH can be accurately predicted using the OAR dose-gradient function in IMRT plans. This approach may be used as a quality control tool and aid less experienced planners determine benchmarks for plan quality.