Delivering radiation therapy based on erroneous or corrupted treatment plan data has previously and unfortunately resulted in severe, sometimes grave patient harm. Aiming to prevent such harm and improve safety in radiation therapy treatment, this work introduces a novel, yet intuitive algorithm for strategically structuring the complex and unstructured data typical of modern treatment plans so their treatment sites may automatically be verified with deep-learning architectures. The proposed algorithm utilizes geometric and dose plan parameters to represent each plan’s data as a heat map to feed a deep-learning classifier that will predict the plan’s treatment site. Once it is returned by the classifier, a plan’s predicted site can be compared to its documented intended site, and a warning raised should the two differ.Using real head-neck, breast, and prostate treatment plan data retrieved at two hospitals in the United States, the algorithm is evaluated by observing the accuracy of convolutional neural networks (ConvNets) in correctly classifying the structured heat map data. Many well-known ConvNet architectures are tested, and ResNet-18 performs the best with a testing accuracy of 97.8% and 0.979 F-1 score. Clearly, the heat maps generated by the proposed algorithm, despite using only a few of the many available plan parameters, retain enough information for correct treatment site classification. The simple construction and ease of interpretation make the heat maps an attractive choice for classification and error detection.

In radiation oncology, the intricate process of delivering radiation to a patient is detailed by the patient's treatment plan, which is data describing the geometry, construction and strength of the radiation machine and the radiation beam it emits. The patient's life depends upon the accuracy of the treatment plan, which is left in the hands of the vendor-specific software automatically generating the plan after an initial patient consultation and planning with a medical professional. However, corrupted and erroneous treatment plan data have previously resulted in severe patient harm when errors go undetected and radiation proceeds. The aim of this paper is to develop an automatic error-checking system to prevent the accidental delivery of radiation treatment to an area of the human body (i.e., the treatment site) that differs from the plan's documented intended site. To this end, we develop a method for structuring treatment plan data in order to feed machine-learning (ML) classifiers and predict a plan's treatment site. In practice, a warning may be raised if the prediction disagrees with the documented intended site. The contribution of this paper is in the strategic structuring of the complex, intricate, and nonuniform data of modern treatment planning and from multiple vendors in order to easily train ML algorithms. A three-step process utilizing up- and down-sampling and dimension reduction, the method we develop in this paper reduces the thousands of parameters comprising a single treatment plan to a single two-dimensional heat map that is independent of the specific vendor or construction of the machine used for treatment. Our heat-map structure lends itself well to feed well-established ML algorithms, and we train-test random forest, softmax, k-nearest neighbors, shallow neural network, and support vector machine using real clinical treatment plans from several hospitals in the United States. The paper demonstrates that the proposed method characterizes treatment sites so well that ML classifiers may predict head-neck, breast, and prostate treatment sites with an accuracy of about 94%. The proposed method is the first step towards a thorough, fully automated error-checking system in radiation therapy.

In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.

Purpose: To evaluate the dosimetric and temporal effects of high-dose-rate respiratory-gated radiation therapy in patients with lung cancer. Methods: Treatment plans from 5 patients with lung cancer (3 nongated and 2 gated at 80EX-80IN) were retrospectively evaluated. Prescription dose for these patients varied from 8 to 18 Gy/fraction with 3 to 5 treatment fractions. Using the same treatment planning criteria, 4 new treatment plans, corresponding to 4 gating windows (20EX-20IN, 40EX-40IN, 60EX-60IN, and 80EX-80IN), were generated for each patient. Mean tumor dose, mean lung dose, and lung V20 were used to assess the dosimetric effects. A MATLAB algorithm was developed to compute treatment time. Results: Mean lung dose and lung V20 were on average reduced between -16.1% to -6.0% and -20.0% to -7.2%, respectively, for gated plans when compared to the corresponding nongated plans, and between -5.8% to -4.2% and -7.0% to -5.4%, respectively, for plans with smaller gating windows when compared to the corresponding plans gated at 80EX-80IN. Treatment delivery times of gated plans using high-dose rate were reduced on average between -19.7% (-0.10 min/100 MU) and -27.2% (-0.13 min/100 MU) for original nongated plans and -15.6% (-0.15 min/100 MU) and -20.3% (-0.19 min/100 MU) for original 80EX-80IN-gated plans. Conclusion: Respiratory-gated radiation therapy in patients with lung cancer can reduce lung dose while maintaining tumor dose. Because treatment delivery during gated therapy is discontinuous, total treatment time may be prolonged. However, this increase in treatment time can be offset by increasing the dose delivery rate. Estimation of treatment time may be helpful in selecting patients for respiratory gating and choosing appropriate gating windows.

Conventional radiotherapy plan evaluation involves sequential 2D review of dose distributions overlaid on registered image datasets. Here we describe a novel method to holographically visualize and interact with 3D isodose volumes. This methodology provides the clinician with the capability for full 3D assessment of dose distributions in the associated patient anatomy in an augmented-reality environment. De-identified radiotherapy plans were exported from a treatment planning system in DICOM format, and these data were processed in a statistical programming environment. Algorithms were developed to identify, import, and reconstruct volumes of interest. Isodose volumes were similarly interpolated from isodose lines imported as DICOM objects. These constructs were then exported into a holographic computing platform where spatial relationships could be further studied and manipulated. Algorithms were successfully developed to process treatment planning data into holographic formats compatible with an established augmented reality platform. Using an augmented reality headset, users were able to intuitively inspect 3D relationships between specific isodose and anatomic volumes. Integrated motion tracking of hand gestures permitted intuitive interaction with holographic projections. For example, pinch gesture can control magnification, or a motion gesture can translate perspective in 360 degrees-of-freedom. These features facilitate rapid visual assessment of therapeutic objectives (e.g., percent coverage of planning target volume). Additional visualization techniques (varying lucency/opacity, heat maps, etc) enable global review of dose conformity and heterogeneity, along with localization of hot/cold spots. We demonstrate a practical method to transition from indirect visualization of radiation dosimetry using isodose lines (2D) to direct evaluation of isodose volumes (3D). This approach can supplement clinically established methods and enhance the treatment review process. Advantages of holographic visualization include rapid global assessment of large treatment fields without loss of critical spatial data (as caused by dose volume histogram based analysis). Further advantages may be realized in complicated treatment scenarios or in editing contours for planning through point interactions with holographic images, enabling multi-slice changes that improve efficiency. Prior treatment plans may also be visualized as holograms to improve understanding of cumulative irradiated volume when considering additional (re)treatment. Of note, the holographic approach described here also offers immediate and wide-ranging application in other medical fields, such as diagnostic radiology, where response assessment and intervention can be guided. Application in brachytherapy is portended.

RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p <; 0.00001). TCP and UTCP presented no statistically significant differences (p ≥ 0.1884). The intra-operator standard deviations (SDs) for TCP, NTCP and UTCP were significantly lower for the semi-automatic segmentation method regardless of STAPLE design (p <; 0.0331). Both intra-and inter-operator SDs of TCP, NTCP and UTCP were significantly lower for semi-automatic segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.

Intraoperative radiation therapy (IORT) involves delivering high doses of radiation directly to tumors while sparing healthy tissues in a surgical setting. Current IORT systems are limited in their lack of image guidance and variable needs for shielded operating rooms. They also lack the capability to deliver non-uniform therapeutic radiation to irregular shaped clinical targets. We developed a scanning beam IORT system (SBIORT) to overcome these limitations. SBIORT consists of a low energy x-ray source, a custom compact dynamic x-ray collimator system, a robotic arm, and a 3D surface imaging module. Here we describe the design and validation of the compact dynamic x-ray collimator system and the 3D surface imaging module for use in SBIORT. The proposed collimator can achieve a leaf position accuracy of ± 0.25 mm (95% confidence interval). Phantom studies indicated the 3D surface-imaging module has an accuracy of 1.0 ± 0.6 mm with ability to obtain high resolution surface image within 5 seconds. SBIORT is a novel approach to deliver conformal intensity-modulated intraoperative radiation therapy.

Purpose: To validate the clinical feasibility and efficacy of a real-time applicator position monitoring system (RAPS) through a phantom study and a prospective clinical trial. Methods and materials: The RAPS measures the brachytherapy applicator displacement in real-time by computing the relative displacement between two infrared reflective targets, one attached to the applicator and the other to the patient's skin. A phantom study was performed to compare RAPS measurements with the ground truth. Six cervical cancer patients were enrolled in the clinical trial using MRI-based high-dose-rate brachytherapy with a Tandem-and-Ovoids applicator. The results from the RAPS are compared with the clinical method. Results: In the phantom study, an average difference between RAPS measurements and known displacements was 0.02 +/- 0.01mmin the superior-inferior direction, 0.02 +/- 0.02mmin the lateral direction, and 0.11 +/- 0.06mmin the anterior-posterior direction. In the clinical trial, the absolute difference in applicator displacement between the RAPS and the clinical method was 1.46 +/- 1.13 mm. In all patient cases, a maximum applicator displacement of 6.66mm (2.0 +/- 1.5mm) was observed using the RAPS. Conclusions: This work demonstrates the clinical efficacy of RAPS to measure applicator displacement.

Purpose:Developing a compact collimator system and validating a 3D surface imaging module for a scanning beam low‐energy x‐ray radiation therapy (SBIORT) system that enables delivery of non‐uniform radiation dose to targets with irregular shapes intraoperatively.Methods:SBIORT consists of a low energy x‐ray source, a custom compact collimator module, a robotic arm, and a 3D surface imaging module. The 3D surface imaging system (structure sensor) is utilized for treatment planning and motion monitoring of the surgical cavity. SBIORT can deliver non‐uniform dose distributions by dynamically moving the x‐ray source assembly along optimal paths with various collimator apertures. The compact collimator utilizes a dynamic shutter mechanism to form a variable square aperture. The accuracy and reproducibility of the collimator were evaluated using a high accuracy encoder and a high resolution camera platform. The dosimetrical characteristics of the collimator prototype were evaluated using EBT3 films with a Pantak Therapax unit. The accuracy and clinical feasibility of the 3D imaging system were evaluated using a phantom and a cadaver cavity.Results:The SBIORT collimator has a compact size: 66 mm diameter and 10 mm thickness with the maximum aperture of 20 mm. The mechanical experiment indicated the average accuracy of leaf position was 0.08 mm with a reproducibility of 0.25 mm at 95% confidence level. The dosimetry study indicated the collimator had a penumbra of 0.35 mm with a leaf transmission of 0.5%. 3D surface scans can be acquired in 5 seconds. The average difference between the acquired 3D surface and the ground truth is 1 mm with a standard deviation of 0.6 mm.Conclusion:This work demonstrates the feasibility of the compact collimator and 3D scanning system for the SBIORT. SBIORT is a way of delivering IORT with a compact system that requires minimum shielding of the procedure room.This research is supported by the University of Iowa Internal Funding Initiatives

It is hypothesized that the real-time applicator position monitoring system (RAPS) can detect intracavitary applicator displacement in real-time when utilized for intracavitary brachytherapy. Applicator displacement during brachytherapy can produce suboptimal dosimetric effects, especially in 3D image guided brachytherapy, which requires high-accuracy applicator localization. Two-D, x-ray imaging devices, such as C-arm, are routinely used for measuring applicator displacement; however, they deliver extra radiation dose to patients and lack the capability of continuous applicator monitoring. The RAPS was developed for continuous applicator position monitoring without any radiation dose. The RAPS consists of two custom-designed tracking targets with infrared reflective markers and a calibrated infrared stereo-camera setup. The RAPS can measure the applicator movement in real-time by computing the relative displacement between the two tracking targets, which are attached to the applicator and the patient. Both 3D printed tracking targets were custom designed for optimal tracking performance and to be easily attached to the tandem and ovoids applicator. A phantom study was conducted to compare RAPS' measurements with known displacements from a high-accuracy positioning stage (0.03 mm accuracy) in the range of +/-25 mm. An IRB-approved patient study is underway to compare RAPS' measurements with those based upon C-arm image analysis. A semi-automatic image registration based method was used to compute the applicator displacement from the C-arm images before/after patient was transferred from MRI scans. The RAPS achieved 120 frames per second using a laptop. At a camera-to-marker distance of 50 cm, the mean difference between RAPS' measurements and the positioning stage was 0.068 mm with a standard deviation (STD) of 0.044 mm in superior-inferior direction, 0.013 mm with a STD of 0.015 mm in lateral direction, and 0.061 mm with a STD of 0.024 mm in anterior-posterior direction. In the first patient study, a difference of 0.97 mm was observed between the RAPS' measurement and those from C-arm images. This work demonstrates the feasibility of RAPS to detect applicator motion in real-time. An accuracy of 0.1 mm was achieved in the phantom study and a difference of 0.97 mm was observed in the first patient study.

Purpose:To evaluate the dosimetric and temporal effects of high dose rate treatment mode for respiratory‐gated radiation therapy in lung cancer patients.Methods:Treatment plans from five lung cancer patients (3 nongated (Group 1), 2 gated at 80EX‐80IN (Group 2)) were retrospectively evaluated. The maximum tumor motions range from 6–12 mm. Using the same planning criteria, four new treatment plans, corresponding to four gating windows (20EX–20IN, 40EX–40IN, 60EX–60IN, and 80EX–80IN), were generated for each patient. Mean tumor dose (MTD), mean lung dose (MLD), and lung V20 were used to assess the dosimetric effects. A MATLAB algorithm was developed to compute treatment time by considering gantry rotation time, time to position collimator leaves, dose delivery time (scaled relative to the gating window), and communication overhead. Treatment delivery time for each plan was estimated using a 500 MU/min dose rate for the original plans and a 1500 MU/min dose rate for the gated plans.Results:Differences in MTD were less than 1Gy across plans for all five patients. MLD and lung V20 were on average reduced between −16.1% to −6.0% and −20.0% to −7.2%, respectively for non‐gated plans when compared with the corresponding gated plans, and between − 5.8% to −4.2% and −7.0% to −5.4%, respectively for plans originally gated at 80EX–80IN when compared with the corresponding 20EX‐20IN to 60EX– 60IN gated plans. Treatment delivery times of gated plans using high dose rate were reduced on average between −19.7% (−1.9min) to −27.2% (−2.7min) for originally non‐gated plans and −15.6% (−0.9min) to −20.3% (−1.2min) for originally 80EX‐80IN gated plans.Conclusion:Respiratory‐gated radiation therapy in lung cancer patients can reduce lung toxicity, while maintaining tumor dose. Using a gated high‐dose‐rate treatment, delivery time comparable to non‐gated normal‐dose‐rate treatment can be achieved.This research is supported by Siemens Medical Solutions USA, Inc

The purpose of this study was to evaluate the dosimetric uncertainty in 4D dose calculation using three temporal probability distributions: uniform distribution, sinusoidal distribution, and patient-specific distribution derived from the patient respiratory trace. Temporal probability, defined as the fraction of time a patient spends in each respiratory amplitude, was evaluated in nine lung cancer patients. Four-dimensional computed tomography (4D CT), along with deformable image registration, was used to compute 4D dose incorporating the patient's respiratory motion. First, the dose of each of 10 phase CTs was computed using the same planning parameters as those used in 3D treatment planning based on the breath-hold CT. Next, deformable image registration was used to deform the dose of each phase CT to the breath-hold CT using the deformation map between the phase CT and the breath-hold CT. Finally, the 4D dose was computed by summing the deformed phase doses using their corresponding temporal probabilities. In this study, 4D dose calculated from the patient-specific temporal probability distribution was used as the ground truth. The dosimetric evaluation matrix included: 1) 3D gamma analysis, 2) mean tumor dose (MTD), 3) mean lung dose (MLD), and 4) lung V20. For seven out of nine patients, both uniform and sinusoidal temporal probability dose distributions were found to have an average gamma passing rate > 95% for both the lung and PTV regions. Compared with 4D dose calculated using the patient respiratory trace, doses using uniform and sinusoidal distribution showed a percentage difference on average of -0.1% ± 0.6% and -0.2% ± 0.4% in MTD, -0.2% ± 1.9% and -0.2% ± 1.3% in MLD, 0.09% ± 2.8% and -0.07% ± 1.8% in lung V20, -0.1% ± 2.0% and 0.08% ± 1.34% in lung V10, 0.47% ± 1.8% and 0.19% ± 1.3% in lung V5, respectively. We concluded that four-dimensional dose computed using either a uniform or sinusoidal temporal probability distribution can approximate four-dimensional dose computed using the patient-specific respiratory trace.

To quantify bone marrow radiation dose response using voxel by voxel analysis of sequentially obtained FLT PET images prior to and during chemoradiation therapy in pelvic cancer patients. Twenty-one pelvic cancer patients were enrolled in IRB approved protocols to obtain FLT PET images prior to and during chemoradiation therapy. Pre-therapy images were used as a control for pelvic bone marrow FLT uptake change. Time series FLT PET images were acquired after 1 week (5 fractions), 2 weeks (10 fractions), and 3 weeks (15 fractions). Weekly low dose attenuation correction CTs (AC CTs) were registered to the planning CT by pelvic bone based automatic rigid image registration. FLT PET images were then registered to the planning CT using the same transformation matrix as its corresponding AC CT. All FLT images and dose volumes were resampled according to the voxel size of the planning CT. The dose volume was then scaled to reflect dose delivered after 1 week, 2 weeks, and 3 weeks. The region of interest (ROI) was defined as the bony pelvis shrunk by a uniform 5 mm margin. The reduction in ROI volume removed a region that was most likely cortical bone and reduced potential noise due to partial volume effect from the large voxel size of the FLT PET image. The resampled FLT images were used to calculate the voxel by voxel value change in FLT activity as a surrogate for marrow activity which was then correlated to dose in 1 Gy increments. The results for each subject were plotted on the same frame of reference for inter subject comparison. Voxel by voxel analysis of bone marrow activity change represented in time series FLT PET images shows an exponential decrease in FLT SUV during chemoradiation therapy when normalized by the pre-therapy image for all subjects. This relationship can be fit by the exponential equation FLTn = 1.3 e-1.17(Dose), where FLTn = weekly normalized FLT SUV and Dose = radiation dose (Gy) at the time of FLTn. The average coefficient of determination (R2) = 0.84 for all 21 subjects. Out of 21 subjects, 19 had R2 > 0.7. This relationship appears to be independent of time or fractionation. FLT SUV voxel values decreased 50% when compared to pretherapy values for a range of radiation doses between 2.2 Gy - 6.8 Gy for all subjects after 1, 2, or 3 weeks. The mean dose to reach 50% of pretherapy FLT SUV was 4.5 Gy. However, the range in FLT SUV voxel change decrease after receiving 4.5 Gy was 25 - 83% for all subjects after 1, 2, or 3 weeks suggesting significant patient variation that was not related to fraction size. Bone marrow activity changes as a function of dose can be measured using voxel by voxel analysis of time serial FLT PET images. This method allows quantitative analysis of FLT PET images at a resolution equal to the voxel size in the PET image for determining the relationship between FLT uptake change and radiation dose.

Purpose:To evaluate dosimetric uncertainty in 4D dose calculation for lung cancer patients using three different temporal probabilities.Methods:The impact of temporal probability, defined as the fraction of time a patient spends in each respiratory amplitude, was evaluated in nine lung cancer patients. For each patient, 4D dose was computed using 4DCT and three temporal probability distributions: 1) uniform distribution, 2) sinusoidal distribution, and 3) patient‐specific distribution. To calculate 4D dose, the dose for each of 10 binned CTs was first computed using the same planning parameters as those used in the breath‐hold CT. Next, deformable image registration was used to deform the dose of each binned CT to the breathhold CT using the deformation map between each binned CT and the breathhold CT. Finally, 4D dose volume was computed by summing the 10 deformed doses using corresponding temporal probabilities. In this study, 4D dose calculated from patient‐specific temporal probabilities was used as the ground truth. Dosimetric comparison included: 1) 3D gamma (3% dose difference, 3mm distance to agreement tolerance), 2) mean tumor dose (MTD), 3) mean lung dose (MLD), and 4) lung V20.Results:For all patients, both uniform and sinusoidal dose distributions were found to have an average gamma passing rate >99% for both lung and PTV volume. Compared with 4D dose calculated using the patient respiratory trace, uniform distribution and sinusoidal distribution showed a percentage difference on average of ‐0.1±0.6% and ‐0.2±0.4% in MTD, ‐0.2±2.0% and ‐0.2±1.3% in MLD, 0.9±2.8% and ‐0.7±1.8% in lung V20, respectively.Conclusion:Both uniformly and sinusoidally‐distributed temporal probabilities can be used to approximate 4D dose calculation. The dosimetric difference among the three temporal probability distributions is not clinically significant.This research is supported by Siemens Medical Solutions USA, Inc

Purpose:To evaluate the dosimetric difference between 3D and 4Dweighted dose calculation using patient specific respiratory trace and deformable image registration for stereotactic body radiation therapy in lung tumors.Methods:Two dose calculation techniques, 3D and 4D‐weighed dose calculation, were used for dosimetric comparison for 9 lung cancer patients. The magnitude of the tumor motion varied from 3 mm to 23 mm. Breath‐hold exhale CT was used for 3D dose calculation with ITV generated from the motion observed from 4D‐CT. For 4D‐weighted calculation, dose of each binned CT image from the ten breathing amplitudes was first recomputed using the same planning parameters as those used in the 3D calculation. The dose distribution of each binned CT was mapped to the breath‐hold CT using deformable image registration. The 4D‐weighted dose was computed by summing the deformed doses with the temporal probabilities calculated from their corresponding respiratory traces. Dosimetric evaluation criteria includes lung V20, mean lung dose, and mean tumor dose.Results:Comparing with 3D calculation, lung V20, mean lung dose, and mean tumor dose using 4D‐weighted dose calculation were changed by −0.67% ± 2.13%, −4.11% ± 6.94% (−0.36 Gy ± 0.87 Gy), −1.16% ± 1.36%(−0.73 Gy ± 0.85 Gy) accordingly.Conclusion:This work demonstrates that conventional 3D dose calculation method may overestimate the lung V20, MLD, and MTD. The absolute difference between 3D and 4D‐weighted dose calculation in lung tumor may not be clinically significant.This research is supported by Siemens Medical Solutions USA, Inc and Iowa Center for Research By Undergraduates

PURPOSE To develop an automated system to safeguard radiation therapy treatments by analyzing electronic treatment records and reporting treatment events. METHODS CATERS (Computer Aided Treatment Event Recognition System) was developed to detect treatment events by retrieving and analyzing electronic treatment records. CATERS is designed to make the treatment monitoring process more efficient by automating the search of the electronic record for possible deviations from physician's intention, such as logical inconsistencies as well as aberrant treatment parameters (e.g., beam energy, dose, table position, prescription change, treatment overrides, etc). Over a 5 month period (July 2012-November 2012), physicists were assisted by the CATERS software in conducting normal weekly chart checks with the aims of (a) determining the relative frequency of particular events in the authors' clinic and (b) incorporating these checks into the CATERS. During this study period, 491 patients were treated at the University of Iowa Hospitals and Clinics for a total of 7692 fractions. RESULTS All treatment records from the 5 month analysis period were evaluated using all the checks incorporated into CATERS after the training period. About 553 events were detected as being exceptions, although none of them had significant dosimetric impact on patient treatments. These events included every known event type that was discovered during the trial period. A frequency analysis of the events showed that the top three types of detected events were couch position override (3.2%), extra cone beam imaging (1.85%), and significant couch position deviation (1.31%). The significant couch deviation is defined as the number of treatments where couch vertical exceeded two times standard deviation of all couch verticals, or couch lateral/longitudinal exceeded three times standard deviation of all couch laterals and longitudinals. On average, the application takes about 1 s per patient when executed on either a desktop computer or a mobile device. CONCLUSIONS CATERS offers an effective tool to detect and report treatment events. Automation and rapid processing enables electronic record interrogation daily, alerting the medical physicist of deviations potentially days prior to performing weekly check. The output of CATERS could also be utilized as an important input to failure mode and effects analysis.

PURPOSE:To evaluate conventional brachytherapy (BT) plans using dose-volume parameters and high resolution (3 Tesla) MRI datasets, and to quantify dosimetric benefits and limitations when MRI-guided, conformal BT (MRIG-CBT) plans are generated. MATERIAL AND METHODS:Fifty-five clinical high-dose-rate BT plans from 14 cervical cancer patients were retrospectively studied. All conventional plans were created using MRI with titanium tandem-and-ovoid applicator (T&O) for delivery. For each conventional plan, a MRIG-CBT plan was retrospectively generated using hybrid inverse optimization. Three categories of high risk (HR)-CTV were considered based on volume: non-bulky (< 20 cc), low-bulky (> 20 cc and < 40 cc) and bulky (≥ 40 cc). Dose-volume metrics of D90 of HR-CTV and D2cc and D0.1cc of rectum, bladder, and sigmoid colon were analyzed. RESULTS:Tumor coverage (HR-CTV D90) of the conventional plans was considerably affected by the HR-CTV size. Sixteen percent of the plans covered HR-CTV D90 with the prescription dose within 5%. At least one OAR had D2cc values over the GEC-ESTRO recommended limits in 52.7% of the conventional plans. MRIG-CBT plans showed improved target coverage for HR-CTV D90 of 98 and 97% of the prescribed dose for non-bulky and low-bulky tumors, respectively. No MRIG-CBT plans surpassed the D2cc limits of any OAR. Only small improvements (D90 of 80%) were found for large targets (> 40 cc) when using T&O applicator approach. CONCLUSIONS:MRIG-CBT plans displayed considerable improvement for tumor coverage and OAR sparing over conventional treatment. When the HR-CTV volume exceeded 40 cc, its improvements were diminished when using a conventional intracavitary applicator.