CT and cone-beam CT of ablative radiation therapy for pancreatic cancer with expert organ-at-risk contours.

We describe a dataset from patients who received ablative radiation therapy for locally advanced pancreatic cancer (LAPC), consisting of computed tomography (CT) and cone-beam CT (CBCT) images with physician-drawn organ-at-risk (OAR) contours. The image datasets (one CT for treatment planning and two CBCT scans at the time of treatment per patient) were collected from 40 patients. All scans were acquired with the patient in the treatment position and in a deep inspiration breath-hold state. Six radiation oncologists delineated the gastrointestinal OARs consisting of small bowel, stomach and duodenum, such that the same physician delineated all image sets belonging to the same patient. Two trained medical physicists further edited the contours to ensure adherence to delineation guidelines. The image and contour files are available in DICOM format and are publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/TCIA.ESHQ-4D90 , Version 2). The dataset can serve as a criterion standard for evaluating the accuracy and reliability of deformable image registration and auto-segmentation algorithms, as well as a training set for deep-learning-based methods.

Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy.

Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner's expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 4,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.

Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ-at-risk (OAR) locations and shapes and to compute delivered dose. This study describes the development and evaluation of a deep-learning (DL) registration model to predict OAR segmentations on the CBCT derived from segmentations on the planning CT. METHODS: The DL model is trained with CT-CBCT image pairs of the same patient, on which OAR segmentations of the small bowel, stomach, and duodenum have been manually drawn. A transformation map is obtained, which serves to warp the CT image and segmentations. In addition to a regularity loss and an image similarity loss, an OAR segmentation similarity loss is also used during training, which penalizes the mismatch between warped CT segmentations and manually drawn CBCT segmentations. At test time, CBCT segmentations are not required as they are instead obtained from the warped CT segmentations. In an IRB-approved retrospective study, a dataset consisting of 40 patients, each with one planning CT and two CBCT scans, was used in a fivefold cross-validation to train and evaluate the model, using physician-drawn segmentations as reference. Images were preprocessed to remove gas pockets. Network performance was compared to two intensity-based deformable registration algorithms (large deformation diffeomorphic metric mapping [LDDMM] and multimodality free-form [MMFF]) as baseline. Evaluated metrics were Dice similarity coefficient (DSC), change in OAR volume within a volume of interest (enclosing the low-dose PTV plus 1 cm margin) from planning CT to CBCT, and maximum dose to 5 cm3 of the OAR [D(5cc)]. RESULTS: Processing time for one CT-CBCT registration with the DL model at test time was less than 5 seconds on a GPU-based system, compared to an average of 30 minutes for LDDMM optimization. For both small bowel and stomach/duodenum, the DL model yielded larger median DSC and smaller interquartile variation than either MMFF (paired t-test P < 10-4 for both type of OARs) or LDDMM (P < 10-3 and P = 0.03 respectively). Root-mean-square deviation (RMSD) of DL-predicted change in small bowel volume relative to reference was 22% less than for MMFF (P = 0.007). RMSD of DL-predicted stomach/duodenum volume change was 28% less than for LDDMM (P = 0.0001). RMSD of DL-predicted D(5cc) in small bowel was 39% less than for MMFF (P = 0.001); in stomach/duodenum, RMSD of DL-predicted D(5cc) was 18% less than for LDDMM (P < 10-3 ). CONCLUSIONS: The proposed deep network CT-to-CBCT deformable registration model shows improved segmentation accuracy compared to intensity-based algorithms and achieves an order-of-magnitude reduction in processing time.

Clinical Experience of Automated SBRT Paraspinal and Other Metastatic Tumor Planning With Constrained Hierarchical Optimization.

PURPOSE: We report on the clinical performance of a fully automated approach to treatment planning based on a Pareto optimal, constrained hierarchical optimization algorithm, named Expedited Constrained Hierarchical Optimization (ECHO). METHODS AND MATERIALS: From April 2017 to October 2018, ECHO produced 640 treated plans for 523 patients who underwent stereotactic body radiation therapy (RT) for paraspinal and other metastatic tumors. A total of 182 plans were for 24 Gy in a single fraction, 387 plans were for 27 Gy in 3 fractions, and the remainder were for other prescriptions or fractionations. Of the plans, 84.5% were for paraspinal tumors, with 69, 302, and 170 in the cervical, thoracic, and lumbosacral spine, respectively. For each case, after contouring, a template plan using 9 intensity modulated RT fields based on disease site and tumor location was sent to ECHO through an application program interface plug-in from the treatment planning system. ECHO returned a plan that satisfied all critical structure hard constraints with optimal target volume coverage and the lowest achievable normal tissue doses. Upon ECHO completion, the planner received an e-mail indicating the plan was ready for review. The plan was accepted if all clinical criteria were met. Otherwise, a limited number of parameters could be adjusted for another ECHO run. RESULTS: The median planning target volume size was 84.3 cm3 (range, 6.9-633.2). The median time to produce 1 ECHO plan was 63.5 minutes (range, 11-340 minutes) and was largely dependent on the field sizes. Of the cases, 79.7% required 1 run to produce a clinically accepted plan, 13.3% required 1 additional run with minimal parameter adjustments, and 7.0% required ≥2 additional runs with significant parameter modifications. All plans met or bettered the institutional clinical criteria. CONCLUSIONS: We successfully implemented automated stereotactic body RT paraspinal and other metastatic tumors planning. ECHO produced high-quality plans, improved planning efficiency and robustness, and enabled expedited treatment planning at our clinic.

Couch and multileaf collimator tracking: A clinical feasibility study for pancreas and liver treatment.

PURPOSE: Real-time tumor tracking through active correction by the multileaf collimator or treatment couch offers a promising strategy to mitigate delivery uncertainty due to intrafractional tumor motion. This study evaluated the performance of MLC and couch tracking using the prototype iTools Tracking system in TrueBeam Developer Mode and the application for abdominal cancer treatments. METHODS: Experiments were carried out using a phantom with embedded Calypso transponders and a motion simulation platform. Geometric evaluations were performed using a circular conformal field with sinusoidal traces and pancreatic tumor motion traces. Geometric tracking accuracy was retrospectively calculated by comparing the compensational MLC or couch motion extracted from machine log files to the target motion reconstructed from real-time MV and kV images. Dosimetric tracking accuracy was measured with radiochromic films using clinical abdominal VMAT plans and pancreatic tumor traces. RESULTS: Geometrically, the root-mean-square errors for MLC tracking were 0.5 and 1.8 mm parallel and perpendicular to leaf travel direction, respectively. Couch tracking, in contrast, showed an average of 0.8 mm or less geometric error in all directions. Dosimetrically, both MLC and couch tracking reduced motion-induced local dose errors compared to no tracking. Evaluated with five pancreatic tumor motion traces, the average 2%/2 mm global gamma pass rate of eight clinical abdominal VMAT plans was 67.4% (range: 26.4%-92.7%) without tracking, which was improved to 86.0% (range: 67.9%-95.6%) with MLC tracking, and 98.1% (range: 94.9%-100.0%) with couch tracking. In 16 out of 40 deliveries with different plans and motion traces, MLC tracking did not achieve clinically acceptable dosimetric accuracy with 3%/3mm gamma pass rate below 95%. CONCLUSIONS: This study demonstrated the capability of MLC and couch tracking to reduce motion-induced dose errors in abdominal cases using a prototype tracking system. Clinically significant dose errors were observed with MLC tracking for certain plans which could be attributed to the inferior MLC tracking accuracy in the direction perpendicular to leaf travel, as well as the interplay between motion tracking and plan delivery for highly modulated plans. Couch tracking outperformed MLC tracking with consistently high dosimetric accuracy in all plans evaluated, indicating its clinical potential in the treatment of abdominal cancers.

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network.

PURPOSE: To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D-CBCT images. METHOD: Under an IRB-approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free-breathing. Concurrently, an electromagnetic signal-guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross-correlation-based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave-one-out strategy using data from 11 lung patients, including 5500 kV images. The root-mean-square error between the CRNN and motion traces was calculated to evaluate the localization accuracy. RESULT: Three-dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high-performance computer cluster. CONCLUSIONS: CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross-correlation-based registration approach, and has the potential to remove reliance on the implanted fiducials.

Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm.

PURPOSE: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART). METHODS: We monitored 10 lung cancer patients by acquiring weekly MRI-T2w scans over a course of radiotherapy. Under an ART workflow, we developed a predictive neural network (P-net) to predict the spatial distributions of tumors in the coming weeks utilizing images acquired earlier in the course. The three-step P-net consisted of a convolutional neural network to extract relevant features of the tumor and its environment, followed by a recurrence neural network constructed with gated recurrent units to analyze trajectories of tumor evolution in response to radiotherapy, and finally an attention model to weight the importance of weekly observations and produce the predictions. The performance of P-net was measured with Dice and root mean square surface distance (RMSSD) between the algorithm-predicted and experts-contoured tumors under a leave-one-out scheme. RESULTS: Tumor shrinkage was 60% ± 27% (mean ± standard deviation) by the end of radiotherapy across nine patients. Using images from the first three weeks, P-net predicted tumors on future weeks (4, 5, 6) with a Dice and RMSSD of (0.78 ± 0.22, 0.69 ± 0.24, 0.69 ± 0.26), and (2.1 ± 1.1 mm, 2.3 ± 0.8 mm, 2.6 ± 1.4 mm), respectively. CONCLUSION: The proposed deep learning algorithm can capture and predict spatial and temporal patterns of tumor regression in a longitudinal imaging study. It closely follows the clinical workflow, and could facilitate the decision-making of ART. A prospective study including more patients is warranted.

Automated intensity modulated treatment planning: The expedited constrained hierarchical optimization (ECHO) system.

PURPOSE: To develop and implement a fully automated approach to intensity modulated radiation therapy (IMRT) treatment planning. METHOD: The optimization algorithm is developed based on a hierarchical constrained optimization technique and is referred internally at our institution as expedited constrained hierarchical optimization (ECHO). Beamlet contributions to regions-of-interest are precomputed and captured in the influence matrix. Planning goals are of two classes: hard constraints that are strictly enforced from the first step (e.g., maximum dose to spinal cord), and desirable goals that are sequentially introduced in three constrained optimization problems (better planning target volume (PTV) coverage, lower organ at risk (OAR) doses, and smoother fluence map). After solving the optimization problems using external commercial optimization engines, the optimal fluence map is imported into an FDA-approved treatment planning system (TPS) for leaf sequencing and accurate full dose calculation. The dose-discrepancy between the optimization and TPS dose calculation is then calculated and incorporated into optimization by a novel dose correction loop technique using Lagrange multipliers. The correction loop incorporates the leaf sequencing and scattering effects into optimization to improve the plan quality and reduce the calculation time. The resultant optimal fluence map is again imported into TPS for leaf sequencing and final dose calculation for plan evaluation and delivery. The workflow is automated using application program interface (API) scripting, requiring user interaction solely to prepare the contours and beam arrangement prior to launching the ECHO plug-in from the TPS. For each site, parameters and objective functions are chosen to represent clinical priorities. The first site chosen for clinical implementation was metastatic paraspinal lesions treated with stereotactic body radiotherapy (SBRT). As a first step, 75 ECHO paraspinal plans were generated retrospectively and compared with clinically treated plans generated by planners using VMAT (volumetric modulated arc therapy) with 4 to 6 partial arcs. Subsequently, clinical deployment began in April, 2017. RESULTS: In retrospective study, ECHO plans were found to be dosimetrically superior with respect to tumor coverage, plan conformity, and OAR sparing. For example, the average PTV D95%, cord and esophagus max doses, and Paddick Conformity Index were improved, respectively, by 1%, 6%, 14%, and 15%, at a negligible 3% cost of the average skin D10cc dose. CONCLUSION: Hierarchical constrained optimization is a powerful and flexible tool for automated IMRT treatment planning. The dosimetric correction step accurately accounts for detailed dosimetric multileaf collimator and scattering effects. The system produces high-quality, Pareto optimal plans and avoids the time-consuming trial-and-error planning process.

Developing a MLC modifier program to improve fiducial detection for MV/kV imaging during hypofractionated prostate volumetric modulated arc therapy.

PURPOSE: To develop an Eclipse plug-in (MLC_MODIFIER) that automatically modifies control points to expose fiducials obscured by MLC during VMAT, thereby facilitating tracking using periodic MV/kV imaging. METHOD: Three-dimensional fiducial tracking was performed during VMAT by pairing short-arc (3°) MV digital tomosynthesis (DTS) images to triggered kV images. To evaluate MLC_MODIFIER efficacy, two cohorts of patients were considered. For first 12 patients, plans were manually edited to expose one fiducial marker. Next for 15 patients, plans were modified using MLC_MODIFIER script. MLC_MODIFIER evaluated MLC apertures at appropriate angles for marker visibility. Angles subtended by control points were compressed and low-dose "imaging" control points were inserted and exposed one marker with 1 cm margin. Patient's images were retrospectively reviewed to determine rate of MV registration failures. Failure categories were poor DTS image quality, MLC blockage of fiducials, or unknown reasons. Dosimetric differences in rectum, bladder, and urethra D1 cc, PTV maximum dose, and PTV dose homogeneity (PTV HI) were evaluated. Statistical significance was evaluated using Fisher's exact and Student's t test. RESULT: Overall MV registration failures, failures due to poor image quality, MLC blockage, and unknown reasons were 33% versus 8.9% (P < 0.0001), 8% versus 6.4% (P < 0.05), 13.6% versus 0.1% (P < 0.0001), and 7.6% versus 2.4% (P < 0.0001) for manually edited and MLC_MODIFIER plans, respectively. PTV maximum and HI increased on average from unmodified plans by 2.1% and 0.3% (P < 0.004) and 22.0% and 3.3% (P < 0.004) for manually edited and MLC_MODIFIED plans, respectively. Changes in bladder, rectum, and urethra D1CC were similar for each method and less than 0.7%. CONCLUSION: Increasing fiducial visibility via an automated process comprised of angular compression of control points and insertion of additional "imaging" control points is feasible. Degradation of plan quality is minimal. Fiducial detection and registration success rates are significantly improved compared to manually edited apertures.

Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network.

PURPOSE: To design a deep learning algorithm that automatically delineates lung tumors seen on weekly magnetic resonance imaging (MRI) scans acquired during radiotherapy and facilitates the analysis of geometric tumor changes. METHODS: This longitudinal imaging study comprised 9 lung cancer patients who had 6-7 weekly T2-weighted MRI scans during radiotherapy. Tumors on all scans were manually contoured as the ground truth. Meanwhile, a patient-specific adaptive convolutional neural network (A-net) was developed to simulate the workflow of adaptive radiotherapy and to utilize past weekly MRI and tumor contours to segment tumors on the current weekly MRI. To augment the training data, each voxel inside the volume of interest was expanded to a 3 × 3 cm patch as the input, whereas the classification of the corresponding patch, background or tumor, was the output. Training was updated weekly to incorporate the latest MRI scan. For comparison, a population-based neural network was implemented, trained, and validated on the leave-one-out scheme. Both algorithms were evaluated by their precision, DICE coefficient, and root mean square surface distance between the manual and computerized segmentations. RESULTS: Training of A-net converged well within 2 h of computations on a computer cluster. A-net segmented the weekly MR with a precision, DICE, and root mean square surface distance of 0.81 ±â€¯0.10, 0.82 ±â€¯0.10, and 2.4 ±â€¯1.4 mm, and outperformed the population-based algorithm with 0.63 ±â€¯0.21, 0.64 ±â€¯0.19, and 4.1 ±â€¯3.0 mm, respectively. CONCLUSION: A-net can be feasibly integrated into the clinical workflow of a longitudinal imaging study and become a valuable tool to facilitate decision- making in adaptive radiotherapy.

Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images.

Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring tumor response to therapy. Hence, we developed two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN. Our networks simultaneously combine features across multiple image resolution and feature levels through residual connections to detect and segment the lung tumors. We evaluated our method on a total of 1210 non-small cell (NSCLC) lung tumors and nodules from three data sets consisting of 377 tumors from the open-source Cancer Imaging Archive (TCIA), 304 advanced stage NSCLC treated with anti- PD-1 checkpoint immunotherapy from internal institution MSKCC data set, and 529 lung nodules from the Lung Image Database Consortium (LIDC). The algorithm was trained using 377 tumors from the TCIA data set and validated on the MSKCC and tested on LIDC data sets. The segmentation accuracy compared to expert delineations was evaluated by computing the dice similarity coefficient, Hausdorff distances, sensitivity, and precision metrics. Our best performing incremental-MRRN method produced the highest DSC of 0.74 ± 0.13 for TCIA, 0.75±0.12 for MSKCC, and 0.68±0.23 for the LIDC data sets. There was no significant difference in the estimations of volumetric tumor changes computed using the incremental-MRRN method compared with the expert segmentation. In summary, we have developed a multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung.

Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation.

We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images. In comparison, state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors. All networks were trained using labeled CT images from 377 patients with non-small cell lung cancer obtained from the Cancer Imaging Archive and unlabeled T2w MRIs from a completely unrelated cohort of 6 patients with pre-treatment and 36 on-treatment scans. Next, we combined 6 labeled pre-treatment MRI scans with the synthesized MRIs to boost tumor segmentation accuracy through semi-supervised learning. Semi-supervised training of cycle-GAN produced a segmentation accuracy of 0.66 computed using Dice Score Coefficient (DSC). Our method trained with only synthesized MRIs produced an accuracy of 0.74 while the same method trained in semi-supervised setting produced the best accuracy of 0.80 on test. Our results show that tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets.

Design and validation of a MV/kV imaging-based markerless tracking system for assessing real-time lung tumor motion.

PURPOSE: Localizing lung tumors during treatment delivery is critical for managing respiratory motion, ensuring tumor coverage, and reducing toxicities. The purpose of this project is to develop a real-time system that performs markerless tracking of lung tumors using simultaneously acquired MV and kV images during radiotherapy of lung cancer with volumetric modulated arc therapy. METHOD: Continuous MV/kV images were simultaneously acquired during dose delivery. In the subsequent analysis, a gantry angle-specific region of interest was defined according to the treatment aperture. After removing imaging artifacts, processed MV/kV images were directly registered to the corresponding daily setup cone-beam CT (CBCT) projections that served as reference images. The registration objective function consisted of a sum of normalized cross-correlation, weighted by the contrast-to-noise ratio of each MV and kV image. The calculated 3D shifts of the tumor were corrected by the displacements between the CBCT projections and the planning respiratory correlated CT (RCCT) to generate motion traces referred to a specific respiratory phase. The accuracy of the algorithm was evaluated on both anthropomorphic phantom and patient studies. The phantom consisted of localizing a 3D printed tumor, embedded in a thorax phantom, in an arc delivery. In an IRB-approved study, data were obtained from VMAT treatments of two lung cancer patients with three electromagnetic (Calypso) beacon transponders implanted in airways near the lung tumor. RESULT: In the phantom study, the root mean square error (RMSE) between the registered and actual (programmed couch movement) target position was 1.2 mm measured by the MV/kV imaging system, which was smaller compared to the MV or kV alone, of 4.1 and 1.3 mm, respectively. In the patient study, the mean and standard deviation discrepancy between electromagnetic-based tumor position and the MV/KV-markerless approach was -0.2 ± 0.6 mm, 0.2 ± 1.0 mm, and -1.2 ± 1.5 mm along the superior-inferior, anterior-posterior, and left-right directions, respectively; resulting in a 3D displacement discrepancy of 2.0 ± 1.1 mm. Poor contrast around the tumor was the main contribution to registration uncertainties. CONCLUSION: The combined MV/kV imaging system can provide real-time 3D localization of lung tumor, with comparable accuracy to the electromagnetic-based system when features of tumors are detectable. Careful design of a registration algorithm and a VMAT plan that maximizes the tumor visibility are key elements for a successful MV/KV localization strategy.

AAPM medical physics practice guideline 10.a.: Scope of practice for clinical medical physics.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline (MPPG) represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiation requires specific training, skills, and techniques as described in each document. As the review of the previous version of AAPM Professional Policy (PP)-17 (Scope of Practice) progressed, the writing group focused on one of the main goals: to have this document accepted by regulatory and accrediting bodies. After much discussion, it was decided that this goal would be better served through a MPPG. To further advance this goal, the text was updated to reflect the rationale and processes by which the activities in the scope of practice were identified and categorized. Lastly, the AAPM Professional Council believes that this document has benefitted from public comment which is part of the MPPG process but not the AAPM Professional Policy approval process. The following terms are used in the AAPM's MPPGs: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances.

Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer.

PURPOSE: To cross-validate and expand a predictive atlas that can estimate geometric patterns of lung tumor shrinkage during radiation therapy using data from 2 independent institutions and to model its integration into adaptive radiation therapy (ART) for enhanced dose escalation. METHODS AND MATERIALS: Data from 22 patients at a collaborating institution were obtained to cross-validate an atlas, originally created with 12 patients, for predicting patterns of tumor shrinkage during radiation therapy. Subsequently, the atlas was expanded by integrating all 34 patients. Each study patient was selected via a leave-one-out scheme and was matched with a subgroup of the remaining 33 patients based on similarity measures of tumor volume and surroundings. The spatial distribution of residual tumor was estimated by thresholding the superimposed shrinkage patterns in the subgroup. A Bayesian method was also developed to recalibrate the prediction using the tumor observed on the midcourse images. Finally, in a retrospective predictive treatment planning (PTP) study, at the initial planning stage, the predicted residual tumors were escalated to the highest achievable dose while maintaining the original prescription dose to the remainder of the tumor. The PTP approach was compared isotoxically to ART that replans with midcourse imaging and to PTP-ART with the recalibrated prediction. RESULTS: Predictive accuracy (true positive plus true negative ratios based on predicted and actual residual tumor) were comparable across institutions, 0.71 versus 0.73, and improved to 0.74 with an expanded atlas including 2 institutions. Recalibration further improved accuracy to 0.76. PTP increased the mean dose to the actual residual tumor by an averaged 6.3Gy compared to ART. CONCLUSION: A predictive atlas found to perform well across institutions and benefit from more diversified shrinkage patterns and tumor locations. Elevating tumoricidal dose to the predicted residual tumor throughout the entire treatment course could improve the efficacy and efficiency of treatment compared to ART with midcourse replanning.

Effects of irregular respiratory motion on the positioning accuracy of moving target with free breathing cone-beam computerized tomography.

For positioning a moving target, a maximum intensity projection (MIP) or average intensity projection (AIP) image derived from 4DCT is often used as the reference image which is matched to free breathing cone-beam CT (FBCBCT) before treatment. This method can be highly accurate if the respiratory motion of the patient is stable. However, a patient's breathing pattern is often irregular. The purpose of this study is to investigate the effects of irregular respiration on positioning accuracy for a moving target aligned with FBCBCT. Nine patients' respiratory motion curves were selected to drive a Quasar motion phantom with one embedded cubic and two spherical targets. A 4DCT of the phantom was acquired on a CT scanner (Philips Brilliance 16) equipped with a Varian RPM system. The phase binned 4DCT images and the corresponding MIP and AIP images were transferred into Eclipse for analysis. FBCBCTs of the phantom driven by the same respiratory curves were also acquired on a Varian TrueBeam and fused such that both CBCT and MIP/AIP images share the same target zero positions. The sphere and cube volumes and centroid differences (alignment error) determined by MIP, AIP and FBCBCT images were calculated, respectively. Compared to the volume determined by MIP, the volumes of the cube, large sphere, and small sphere in AIP and FBCBCT images were smaller. The alignment errors for the cube, large sphere and small sphere with center to center matches between MIP and FBCBCT were 2.5 ± 1.8mm, 2.4±2.1 mm, and 3.8±2.8 mm, and the alignment errors between AIP and FBCBCT were 0.5±1.1mm, 0.3±0.8mm, and 1.8±2.0 mm, respectively. AIP images appear to be superior reference images to MIP images. However, irregular respiratory pattern could compromise the positioning accuracy, especially for smaller targets.

Evaluation of respiratory motion-corrected cone-beam CT at end expiration in abdominal radiotherapy sites: a prospective study.

BACKGROUND: Cone beam computed tomography (CBCT) for radiotherapy image guidance suffers from respiratory motion artifacts. This limits soft tissue visualization and localization accuracy, particularly in abdominal sites. We report on a prospective study of respiratory motion-corrected (RMC)-CBCT to evaluate its efficacy in localizing abdominal organs and improving soft tissue visibility at end expiration. MATERIAL AND METHODS: In an IRB approved study, 11 patients with gastroesophageal junction (GEJ) cancer and five with pancreatic cancer underwent a respiration-correlated CT (4DCT), a respiration-gated CBCT (G-CBCT) near end expiration and a one-minute free-breathing CBCT scan on a single treatment day. Respiration was recorded with an external monitor. An RMC-CBCT and an uncorrected CBCT (NC-CBCT) were computed from the free-breathing scan, based on a respiratory model of deformations derived from the 4DCT. Localization discrepancy was computed as the 3D displacement of the GEJ region (GEJ patients), or gross tumor volume (GTV) and kidneys (pancreas patients) in the NC-CBCT and RMC-CBCT relative to their positions in the G-CBCT. Similarity of soft-tissue features was measured using a normalized cross correlation (NCC) function. RESULTS: Localization discrepancy from the end-expiration G-CBCT was reduced for RMC-CBCT compared to NC-CBCT in eight of eleven GEJ cases (mean ± standard deviation, respectively, 0.21 ± 0.11 and 0.43 ± 0.28 cm), in all five pancreatic GTVs (0.26 ± 0.21 and 0.42 ± 0.29 cm) and all ten kidneys (0.19 ± 0.13 and 0.51 ± 0.25 cm). Soft-tissue feature similarity around GEJ was higher with RMC-CBCT in nine of eleven cases (NCC =0.48 ± 0.20 and 0.43 ± 0.21), and eight of ten kidneys (0.44 ± 0.16 and 0.40 ± 0.17). CONCLUSIONS: In a prospective study of motion-corrected CBCT in GEJ and pancreas, RMC-CBCT yielded improved organ visibility and localization accuracy for gated treatment at end expiration in the majority of cases.

Real-time intraoperative evaluation of implant quality and dose correction during prostate brachytherapy consistently improves target coverage using a novel image fusion and optimization program.

PURPOSE: Our purpose was to describe the process and outcome of performing postimplantation dosimetric assessment and intraoperative dose correction during prostate brachytherapy using a novel image fusion-based treatment-planning program. METHODS AND MATERIALS: Twenty-six consecutive patients underwent intraoperative real-time corrections of their dose distributions at the end of their permanent seed interstitial procedures. After intraoperatively planned seeds were implanted and while the patient remained in the lithotomy position, a cone beam computed tomography scan was obtained to assess adequacy of the prescription dose coverage. The implanted seed positions were automatically segmented from the cone-beam images, fused onto a new set of acquired ultrasound images, reimported into the planning system, and recontoured. Dose distributions were recalculated based upon actual implanted seed coordinates and recontoured ultrasound images and were reviewed. If any dose deficiencies within the prostate target were identified, additional needles and seeds were added. Once an implant was deemed acceptable, the procedure was completed, and anesthesia was reversed. RESULTS: When the intraoperative ultrasound-based quality assurance assessment was performed after seed placement, the median volume receiving 100% of the dose (V100) was 93% (range, 74% to 98%). Before seed correction, 23% (6/26) of cases were noted to have V100 <90%. Based on this intraoperative assessment and replanning, additional seeds were placed into dose-deficient regions within the target to improve target dose distributions. Postcorrection, the median V100 was 97% (range, 93% to 99%). Following intraoperative dose corrections, all implants achieved V100 >90%. CONCLUSIONS: In these patients, postimplantation evaluation during the actual prostate seed implant procedure was successfully applied to determine the need for additional seeds to correct dose deficiencies before anesthesia reversal. When applied, this approach should significantly reduce intraoperative errors and chances for suboptimal dose delivery during prostate brachytherapy.

A geometric atlas to predict lung tumor shrinkage for radiotherapy treatment planning.

To develop a geometric atlas that can predict tumor shrinkage and guide treatment planning for non-small-cell lung cancer. To evaluate the impact of the shrinkage atlas on the ability of tumor dose escalation. The creation of a geometric atlas included twelve patients with lung cancer who underwent both planning CT and weekly CBCT for radiotherapy planning and delivery. The shrinkage pattern from the original pretreatment to the residual posttreatment tumor was modeled using a principal component analysis, and used for predicting the spatial distribution of the residual tumor. A predictive map was generated by unifying predictions from each individual patient in the atlas, followed by correction for the tumor's surrounding tissue distribution. Sensitivity, specificity, and accuracy of the predictive model for classifying voxels inside the original gross tumor volume were evaluated. In addition, a retrospective study of predictive treatment planning (PTP) escalated dose to the predicted residual tumor while maintaining the same level of predicted complication rates for a clinical plan delivering uniform dose to the entire tumor. The effect of uncertainty on the predictive model's ability to escalate dose was also evaluated. The sensitivity, specificity and accuracy of the predictive model were 0.73, 0.76, and 0.74, respectively. The area under the receiver operating characteristic curve for voxel classification was 0.87. The Dice coefficient and mean surface distance between the predicted and actual residual tumor averaged 0.75, and 1.6 mm, respectively. The PTP approach allowed elevation of PTV D95 and mean dose to the actual residual tumor by 6.5 Gy and 10.4 Gy, respectively, relative to the clinical uniform dose approach. A geometric atlas can provide useful information on the distribution of resistant tumors and effectively guide dose escalation to the tumor without compromising the organs at risk complications. The atlas can be further refined by using more patient data sets.

Optimizing fiducial visibility on periodically acquired megavoltage and kilovoltage image pairs during prostate volumetric modulated arc therapy.

PURPOSE: Robust detection of implanted fiducials is essential for monitoring intrafractional motion during hypofractionated treatment. The authors developed a plan optimization strategy to ensure clear visibility of implanted fiducials and facilitate 3D localization during volumetric modulated arc therapy (VMAT). METHODS: Periodic kilovoltage (kV) images were acquired at 20° gantry intervals and paired with simultaneously acquired 4.4° short arc megavoltage digital tomosynthesis (MV-DTS) to localize three fiducials during VMAT delivery for hypofractionated prostate cancer treatment. Beginning with the original optimized plan, control point segments where fiducials were consistently blocked by multileaf collimator (MLC) within each 4.4° MV-DTS interval were first identified. For each segment, MLC apertures were edited to expose the fiducial that led to the least increase in the cost function. Subsequently, MLC apertures of all control points not involved with fiducial visualization were reoptimized to compensate for plan quality losses and match the original dose-volume histogram. MV dose for each MV-DTS was also kept above 0.4 MU to ensure acceptable image quality. Different imaging (gantry) intervals and visibility margins around fiducials were also evaluated. RESULTS: Fiducials were consistently blocked by the MLC for, on average, 36% of the imaging control points for five hypofractionated prostate VMAT plans but properly exposed after reoptimization. Reoptimization resulted in negligible dosimetric differences compared with original plans and outperformed simple aperture editing: on average, PTV D98 recovered from 87% to 94% of prescription, and PTV dose homogeneity improved from 9% to 7%. Without violating plan objectives and compromising delivery efficiency, the highest imaging frequency and largest margin that can be achieved are a 10° gantry interval, and 15 mm, respectively. CONCLUSIONS: VMAT plans can be made to accommodate MV-kV imaging of fiducials. Fiducial visualization rate and workflow efficiency are significantly improved with an automatic modification and reoptimization approach.