Browsing by Subject "Treatment planning"

Introduction: VMAT treatment planning requires time-consuming DVH-based inverse optimization process, which impedes its application in time-sensitive situations. This work aims to develop a deep-learning based algorithm, Automatic VMAT Planning via MLC Dynamic Sequence Prediction (AVP-DSP), for rapid prostate VMAT treatment planning.

Methods: AVP-DSP utilizes a series of 2D projections of a patient’s dose prediction and contour structures to generate a single 360º dynamic MLC sequence in a VMAT plan. The backbone of AVP-DSP is a novel U-net implementation which has a 4-resolution-step analysis path and a 4-resolution-step synthesis path. AVP-DSP was developed based on 131 previous prostate patients who received simultaneously-integrated-boost (SIB) treatment (58.8Gy/70Gy to PTV58.8/PTV70 in 28fx). All patients were planned by a 360º single-arc VMAT technique using an in-house intelligent planning tool in a commercial treatment planning system (TPS). 120 plans were used in AVP-DSP training/validation, and 11 plans were used as independent tests. Key dosimetric metrics achieved by AVP-DSP were compared against the ones planned by the commercial TPS.

Results: After dose normalization (PTV70 V70Gy=95%), all 11 AVP-DSP test plans met institutional clinic guidelines of dose distribution outside PTV. Bladder (V70Gy=6.8±3.6cc, V40Gy=19.4±9.2%) and rectum (V70Gy=2.8±1.8cc, V40Gy=26.3±5.9%) results in AVP-DSP plans were comparable with the commercial TPS plan results (bladder V70Gy=4.1±2.0cc, V40Gy=17.7±8.9%; rectum V70Gy=1.4±0.7cc, V40Gy=24.0±5.0%). 3D max dose results in AVP-DSP plans(D1cc=118.9±4.1%) were higher than the commercial TPS plans results(D1cc=106.7±0.8%). On average, AVP-DSP used 30 seconds for a plan generation in contrast to the current clinical practice (>20 minutes).

Conclusion: Results suggest that AVP-DSP can generate a prostate VMAT plan with clinically-acceptable dosimetric quality. With its high efficiency, AVP-DSP may hold great potentials of real-time planning application after further validation.

Introduction: Several studies have realized fluence-map-prediction-based DL IMRT planning algorithms. However, DL-based VMAT planning remains unsolved. A main difficult in DL-based VMAT planning is how to generate leaf sequences from the predicted radiation intensity maps. Leaf sequences are required for a large number of control points and meet physical restrictions of MLC. A previous study1 reported a DL algorithm to generate 64-beam IMRT plans to approximate VMAT plans with certain dose distributions as input. As a step forward, another study2 reported a DL algorithm to generate one-arc VMAT plans from patient anatomy. This study generated MLC leaf sequence from thresholded predicted intensity maps for one-arc VMAT plans. Based on this study, we developed an algorithm to convert DL-predicted intensity maps to multi-segment VMAT plans to improve the performance of one-arc plans.

Methods: Our deep learning model utilizes a series of 2D projections of a patient’s dose prediction and contour structures to generate a multi-arc 360º dynamic MLC sequence in a VMAT plan. The backbone of this model is a novel U-net implementation which has a 4-resolution-step analysis path and a 4-resolution-step synthesis path. In the pretrained DL model, a total of 130 patients were involved, with 120 patients in the training and 11 patients in testing groups, respectively. These patients were prescribed with 70Gy/58.8Gy to the primary/boost PTVs in 28 fractions in a simulated integrated boost (SIB) regime. In this study, 7-8 arcs with the same collimator angle are used to simulate the predicted intensity maps. The predicted intensity maps are separated into 7-8 segments along the collimator angle. Hence, the arcs could separately simulate predicted intensity maps with independent weight factors. This separation also potentially allows MLC leaves to simulate more dose gradient in the predicted intensity mapsResults: After dose normalization (PTV70 V70Gy=95%), all 11 multi-segment test plans met institutional clinic guidelines of dose distribution outside PTV. Bladder (V70Gy=5.3±3.3cc, V40Gy=16.1±8.6%) and rectum (V70Gy=4.5±2.3cc, V40Gy=33.4±8.1%) results in multi-segment plans were comparable with the commercial TPS plan results. 3D max dose results in AVP-DSP plans(D1cc=112.6±1.9%) were higher than the commercial TPS plans results(D1cc=106.7±0.8%). On average, AVP-DSP used 600 seconds for a plan generation in contrast to the current clinical practice (>20 minutes).

Conclusion: Results suggest that multi-segment plans can generate a prostate VMAT plan with clinically-acceptable dosimetric quality. the proposed multi-segment plan generation algorithm has the capability to achieve higher modulation and lower maximum dose. With its high efficiency, multi-segment may hold great potentials of real-time planning application after further validation.

With the development of medical linear accelerator technologies, the precision and complexity of external beam radiation therapy have increased tremendously over the years. The goal of radiation therapy has always been to push the limit to irradiate the target volume while preserving normal tissues. To achieve this goal, treatment planning for radiation therapy has become a labor-intensive and time-consuming task, which requires a high level of experience and knowledge from the planner. Therefore, automated treatment planning, or auto-planning, is of particular interest in radiation therapy research. The advantages of auto-planning are reduced planning time and increased plan quality consistency.Since the treatment planning workflow has multiple steps, auto-planning includes the automation of different planning procedures, such as contouring, beam placement, and inverse optimization, which can be achieved in different approaches. The main approaches are knowledge-based planning, automated rule implementation and reasoning, and multicriteria optimization. We can generally consider such novel auto-planning applications as artificial intelligence (AI). This study primarily focuses on treatment plan generation using knowledge-based planning and machine learning techniques. The study includes two main projects: automated beam setting for whole breast radiation therapy (WBRT) and fluence map prediction for intensity modulated radiation therapy (IMRT). In WBRT planning, tangential beams are used to irradiate the entire breast volume and avoid the organs-at-risk (OARs) (i.e., the lungs and the heart) as much as possible. The placement of the beams is vital in determining the planning target volume (PTV) coverage and normal tissue sparing. Furthermore, planners need to take multiple clinical considerations into account, e.g., avoiding the contralateral breast and the heart, and use a variety of techniques to meet the demands. Therefore, we developed an automated beam setting program which takes simple user settings and optimizes target coverage and OAR sparing. The program can be launched from the Eclipse Treatment Planning System (TPS) as a binary plug-in script, which generates a graphical user interface to accept user inputs. Several beam geometries are supported: tangential beams only (supine), tangential plus supraclavicular (SCV) beams (supine), and prone beams. For all geometries, the program calculates the optimal gantry angles, collimator angles, isocenter location, jaw sizes, and MLC shapes. The borders of the SCV beams are also matched to the tangential beams by using couch kicks on the main tangential fields. For the supine geometries, a coefficient was learned from existing clinical plans to balance between the PTV and lung coverages. The program searches from an initial setting based on breast wires and finds the optimal setting. For the prone geometry, the planner can set a margin to customize the coverage near the PTV-lung interface. The program has been implemented together with a WBRT fluence prediction program, which creates electronic compensation (ECOMP) plans from the given beam settings. This automated workflow can significantly reduce the workload of the forward planned ECOMP plans. The results showed that the AI plans achieved similar or better plan quality compared to the manual plans. In IMRT planning, inverse optimization is the standard practice to create treatment plans. Dose-volume histogram (DVH) constraints and priorities are set by the planner to start the optimization and often continuously tuned throughout the planning process until the optimal dose distribution is achieved. The actual parameters to be optimized are fluence map intensities of the IMRT beams. Numerous efforts have been devoted in KBP to predict either the DVH or the dose of the optimal plan. The rationale is that, given the patient anatomy and the physician’s prescription, the DVH or dose in the final plan can be predicted based on similar previous plans. The predicted DVH or dose can then be used as a reference to either evaluate the plan quality or generate new plans by converting them into inverse optimization objectives, which is a process also known as dose mimicking. However, most dose mimicking techniques are still in the development stage and not yet commercially available. We explored the feasibility to directly predict optimal fluence maps and generate IMRT plans without inverse optimization. In order to achieve fluence map prediction, we first investigated the correlation between patient anatomy and fluence maps. A database of patient anatomy and fluence maps was built with pancreas SBRT cases. Treatment planning was done on 2D axial slices with in-house dose calculation and fluence optimization algorithms. For a new slice, an atlas matching method was developed to search for the most anatomically similar slice in the database and initialize the optimization with the existing fluence. The atlas-guided fluence optimization reduced the optimization cost and offered a small dosimetric improvement compared to uniform initialization. With more training data, deep learning methods were experimented to predict fluence maps from patient anatomy. A deep learning framework consisting of two convolutional neural networks (CNN) was developed. As each plan has several beams, all beam doses must add up to the optimal plan’s total dose, while each beam dose is deposited only by said beam’s fluence map. Therefore, the BD-CNN predicts the individual beam doses (BD) for an IMRT plan, which tries to minimize the prediction error for both the beam doses and the total dose. Once the beam doses are available, each fluence map (FM) is generated separately by the FM-CNN. As the fluence maps exist in the beam’s eye view (BEV), a projection of the 3D beam dose onto the 2D BEV is necessary. The resulting dose map is used as the input to the FM-CNN, which predicts the fluence map as the output. The predicted fluence maps are imported into the TPS for leaf sequencing and dose calculation, generating a deliverable plan. These projects are retrospective studies using anonymized patient data for training and testing. The development of the deep learning framework was split into several stages: the initial test of the feasibility was conducted for pancreas stereotactic body radiation therapy (SBRT) with a single PTV, unified dose constraints, and a fixed 9-beam geometry; the networks were then modified to allow variable dose inputs and multiple PTVs for pancreas SBRT with simultaneous integrated boost (SIB); a transfer learning technique was applied to the training of the framework for adrenal SBRT plans with different beam settings and dose constraints, using the pancreas model as the base model. The framework has evolved to be more robust and support different sites and planning styles over time. The AI plans with predicted fluence maps achieved similar plan quality as manual plans for most cases. For some cases with particularly challenging patient anatomies, the AI plans can struggle to reach the high standard of the expert plans. Fluence map prediction is a viable way to directly generate IMRT plans without inverse optimization. This application may be especially useful for adaptive treatment planning.

Purpose: To develop an automated program that can generate the optimal beams for whole breast radiation therapy (WBRT).

Methods and Materials: A total of twenty patients receiving WBRT were included in this study. The computed tomography (CT) simulation images and structures of all 20 patients were used to develop and validate the program. All patients had the breast planning target volume (PTV) contour drawn by physicians and radio-opaque catheters placed on the skin during CT simulation. First, an initial beam was calculated based on the CT images, the radio-opaque catheters, and the breast PTV contour. The beam includes five main parameters: the gantry angles, the isocenter location, the field size, the collimator angles, and the initial multi-leaf collimator (MLC) shape.

To optimize the beam parameters, a geometry-based objective function was constructed to optimize target coverage and organ-at-risk (OAR) sparing. The objective function is the weighted sum of the square of the relative volumes of the PTV outside the field and the ipsilateral lung inside the field. Due to the curvature of the chest wall, a portion of the ipsilateral lung will be included in the irradiated volume. The balance between PTV coverage and OAR sparing is embodied by the relative weight of the lung volume in the objective function, which was trained and validated from the clinical plans of the twenty patients. Two different optimization schemes were developed to minimize the objective function: the exhaustive search and the local search. The search was conducted in a 2-dimensional grid with the gantry angle (1° increments) and the isocenter location (1 mm increments) as two axes and the initial beam as the origin point. For the exhaustive search, the ranges of the gantry angle and the isocenter location are ±12° and ±21 mm. The local search does not require a search range. The beam with the minimal objective function value in the grid is considered optimal. The optimal beam was transferred to an in-house automatic fluence optimization program developed specifically for WBRT. The automatic plans were compared with the manually generated clinical plans for target coverage, dose conformity and homogeneity, and OAR dose.

Results: The calculation time of the beam optimization was under one minute for all cases. The local search (~15 s) took less time than the exhaustive search (~45 s), and the two methods produced the same result for the same patient. The automatic plans have overall comparable plan quality to the clinical plans, which usually take 1 to 4 hours to make. Generally, the PTV coverage is improved while the dose to the ipsilateral lung and the heart is similar. The breast PTV Eval V95% of all cases are above 95%, and the mean V95% (97.7%) is increased compared with the clinical plans (96.8%). The ipsilateral lung V16Gy is reduced for 14 out of 20 cases, and the mean V16Gy is decreased in the automatic plans (12.6% vs. 13.6%). The average heart mean dose is slightly increased in the automatic plans (2.06% vs. 1.99%).

Conclusion: Optimal beams for WBRT can be automatically generated in one minute given the patient’s simulation CT images and structures. The automated beam setup program offers a valuable tool for WBRT planning, as it provides clinically relevant solutions based on previous clinical practice as well as patient specific anatomy.

Since 2010, Duke University Medical Center has used the single-isocenter technique to treat patients with multiple brain metastases. The purpose of this project is to compare treatment planning techniques for these patients who received treatment. First, we want to determine if volumetric modulated arc therapy (VMAT) or dynamic conformal arc therapy (DCAT) is the better method for treatment for incoming patients. Next, we want to know if using U-frame or frameless masks provide better plan quality. Lastly, we want to test the use of a stationary couch to simulate imaging while treating with the moving gantry. DCAT plans were created for each of the 40 single-isocenter patients who received VMAT at Duke University Medical Center from 2016 to 2018. These patients were randomly selected based only on the number of metastases, from 2 to 14. We created the DCAT plans using 5 couch positions, 2 collimator angles, and 100° arcs on BrainLab Elements. We modeled U-frame and frameless masks using 100° and 180° arcs, respectively. To simulate imaging, we kept the couch at 0° while using only 180° arcs. The clinical VMAT plans delivered to the 40 patients had an average conformity index of 1.47 and average gradient index of 8.57. Average whole-brain V3 Gy and V5 Gy were 14.07% and 5.80%, respectively. In comparison, using DCAT the conformity index was 1.75 and the gradient index was 6.87. Whole-brain V3 Gy and V5 Gy were 11.25% and 5.59%, respectively. The frameless mask plans had conformity and gradient indexes of 1.68 and 6.39 and V3 Gy and V5 Gy of 11.39% and 5.09%, respectively. Using VMAT for the imaging cases, we found conformity and gradient indexes of 1.59 and 11.99 and V3 Gy and V5 Gy of 18.04% and 8.41%. Using DCAT for the imaging cases had conformity and gradient indexes of 2.02 and 9.86 and V3 Gy and V5 Gy of 14.21% and 7.25%, respectively. Overall, VMAT plans had higher conformity index with lower gradient index at the cost of healthy brain protection compared to DCAT. Frameless masks also increased the conformity index and decreased the gradient index with no significant impact on low doses to the brain. The use of imaging while treating should be considered with the benefit when imaging on a case-by-case basis.

Knowledge-based radiation treatment is an emerging concept in radiotherapy. It

mainly refers to the technique that can guide or automate treatment planning in

clinic by learning from prior knowledge. Dierent models are developed to realize

it, one of which is proposed by Yuan et al. at Duke for lung IMRT planning. This

model can automatically determine both beam conguration and optimization ob-

jectives with non-coplanar beams based on patient-specic anatomical information.

Although plans automatically generated by this model demonstrate equivalent or

better dosimetric quality compared to clinical approved plans, its validity and gener-

ality are limited due to the empirical assignment to a coecient called angle spread

constraint dened in the beam eciency index used for beam ranking. To eliminate

these limitations, a systematic study on this coecient is needed to acquire evidences

for its optimal value.

To achieve this purpose, eleven lung cancer patients with complex tumor shape

with non-coplanar beams adopted in clinical approved plans were retrospectively

studied in the frame of the automatic lung IMRT treatment algorithm. The primary

and boost plans used in three patients were treated as dierent cases due to the

dierent target size and shape. A total of 14 lung cases, thus, were re-planned using

the knowledge-based automatic lung IMRT planning algorithm by varying angle

spread constraint from 0 to 1 with increment of 0.2. A modied beam angle eciency

index used for navigate the beam selection was adopted. Great eorts were made to assure the quality of plans associated to every angle spread constraint as good

as possible. Important dosimetric parameters for PTV and OARs, quantitatively

re

ecting the plan quality, were extracted from the DVHs and analyzed as a function

of angle spread constraint for each case. Comparisons of these parameters between

clinical plans and model-based plans were evaluated by two-sampled Students t-tests,

and regression analysis on a composite index built on the percentage errors between

dosimetric parameters in the model-based plans and those in the clinical plans as a

function of angle spread constraint was performed.

Results show that model-based plans generally have equivalent or better quality

than clinical approved plans, qualitatively and quantitatively. All dosimetric param-

eters except those for lungs in the automatically generated plans are statistically

better or comparable to those in the clinical plans. On average, more than 15% re-

duction on conformity index and homogeneity index for PTV and V40, V60 for heart

while an 8% and 3% increase on V5, V20 for lungs, respectively, are observed. The

intra-plan comparison among model-based plans demonstrates that plan quality does

not change much with angle spread constraint larger than 0.4. Further examination

on the variation curve of the composite index as a function of angle spread constraint

shows that 0.6 is the optimal value that can result in statistically the best achievable

plans.

Radiation has been utilized in medical domain for multiple purposes. Treating cancer using radiation has increasing popularity during the last century. Radiation beam is directed to the tumor cells while the surrounding healthy tissue is attempted to be avoided. Radiation therapy treatment planning serves the goal of delivering high concentrated radiation to the treatment volume while minimizing the normal tissue as much as possible. With the advent of more sophisticated delivery technology, treatment planning time increases over time. In addition, the treatment plan quality relies on the experience of the planner. Several computer assistance techniques emerged to help the treatment planning process, among which knowledge-based planning (KBP) has been successful in inverse planning IMRT. KBP falls under the umbrella of Knowledge Discovery in Databases (KDD) which originated in industry. The philosophy is to extract useful knowledge from previous application/data/observations to make predictions in the future practice. KBP reduces the iterative trial-and-error process in manual planning, and more importantly guarantees consistent plan quality. Despite the great potential of treatment planning KDD (TPKDD), three major challenges remain before TPKDD can be widely implemented in the clinical environment: 1. a good knowledge model asks for sufficient amount of training data to extract useful knowledge and is therefore less efficient; 2. a knowledge model is usually only applicable for the specific treatment site and treatment technique and is therefore less generalizable; 3. a knowledge model needs meticulous inspection before implementing in the clinic to verify the robustness.

This study aims at filling in the niche in TPKDD and improves current TPKDD workflow by tackling the aforementioned challenges. This study is divided into three parts. The first part of the study aims to improve the modeling efficiency by introducing an atlas-based treatment planning guidance. In the second part of the study, an automated treatment planning technique for whole breast radiation therapy (WBRT) is proposed to provide a solution for the area where TPKDD has not yet set foot on. In the third part of the study, several topics related to the knowledge model quality are addressed, including improving the model training workflow, identifying geometric novelty and dosimetric outlier case, building a global model and facilitating incremental learning.

I. Improvement of the modeling efficiency. First, a prostate cancer patient anatomy atlas was established to generate 3D dose distribution guidance for the new patient. The anatomy pattern of the prostate cancer patient was parameterized with two descriptors. Each training case was represented in 2D feature space. All training cases were clustered using the k-medoids algorithm. The optimal number of clusters was determined by the largest average silhouette width. For the new case, the most similar case in the atlas was identified and used to generate dose guidance. The anatomy of the atlas case and the query case was registered and the deformation field was applied to the 3D radiation dose of the atlas case. The deformed dose served as the goal dose for the query case. Dose volume objectives were then extracted from the goal dose to guide the inverse IMRT planning. Results showed that the plans generated with atlas guidance had similar dosimetric quality as compared to the clinical manual plans. The monitor units (MU) of the auto plan were also comparable with the clinical plan. Atlas-guided radiation therapy has proven to be effective and efficient in inverse IMRT planning.

II. Improvement of model generalization. An automatic WBRT treatment planning workflow was developed. First of all, an energy selection tool was developed based on previous single energy and dual energy WBRT plans. The DRR intensity histograms of training cases were collected and the principal component analysis (PCA) was performed to reduce the dimension of the histogram. First two components were used to represent each case and the classification was performed in the 2D space. This tool helps new patient to select appropriate energy based on the anatomy information. Secondly, an anatomy feature based random forest (RF) model was proposed to predict the fluence map for the patient. The model took the input of multiple anatomical features and output the fluence intensity of each pixel within the fluence map. Finally, a physics rule based method was proposed to further fine tune the fluence map to achieve optimal dose distribution within the irradiated volume. Extra validation cases were tested on the proposed workflow. Results showed similar dosimetric quality between auto plan and clinical manual plan. The treatment planning time was reduced from between 1-4 hours for the manual planning to within 1 minute for the auto planning. The proposed automatic WBRT planning technique has proven to be efficient.

III. Rapid learning of radiation therapy KBP. Several topics were analyzed in this part of the study. First of all, a systematic workflow was established to improve the KBP model quality. The workflow started with identifying geometric novelty case using the statistical metric “leverage”, followed by removing the novelty case. Then the dosimetric outlier was identified using studentized residual and then cleaned. The cleaned model was compared with the uncleaned model using the extra validation cases. This study used pelvic cases as an example. Results showed that the existence of novelty and outlier cases did degrade the model quality. The proposed statistical tools can effectively identify novelty and outlier cases. The workflow is able to improve the quality of the knowledge-based model.

Secondly, a clustering-based method was proposed to identify multiple geometric novelty cases and dosimetric outlier cases at the same time. One class support vector machine (OCSVM) was applied to the feature vectors of all training cases to generate one class of inliers while cases falling out of the frontier belonged to the novelty case group. Once the novelty cases were identified and cleaned, the robust regression followed by outlier identification (ROUT) was applied to all remaining cases to identify dosimetric outliers. A cleaned model was trained with the novelty and outlier free case pool and was tested using 10 fold cross validation. Initial training pool included intentionally added outlier cases to evaluate the efficacy of the proposed method. The model prediction on the inlier cases was compared with that of novelty and outlier cases. Results showed that the method can successfully identify geometric novelty and dosimetric outliers. The model prediction accuracy between the inliers and novelty/outliers was significantly different, indicating different dosimetric behavior between two groups. The proposed method proved to be effective in identifying multiple geometric novelty and dosimetric outliers.

Thirdly, a global model using the model tree and the clustering-based model was proposed to include cases with different clinical conditions and indications. The model tree is a combination of decision tree and linear regression, where all cases are branched into leaves and regression is performed within each leaf. A clustering-based model used k-means algorithm to segment all cases into more aggregated groups, and then the regression was performed within each small group. The overall philosophy of both the model tree and the clustering-based method is that cases with similar features have similar geometry-dosimetry relation. Training cases within small feature range gives better model accuracy. The proposed method proved to be effective in improving the model accuracy over the model trained with all cases without segmenting the cases.

At last, the incremental learning was analyzed in radiation therapy treatment planning model. This study tries to answer the question when model re-training should be invoked. In the clinical environment, it is often unnecessary to re-train the model whenever there is a new case. The scenario of incrementally adapting the model was simulated using the pelvic cases with different number of training cases and new incoming cases. The result showed that re-training was often necessary for small training dataset and as the number of cases increased, re-training became less frequent.

In summary, this study addressed three major challenges in TPKDD. In the first part, an atlas-guided treatment planning technique was proposed to improve the modeling efficiency. In the second part, an automatic whole breast radiation therapy treatment planning technique was proposed to tackle the issue where TPKDD has not yet resolved. In the final part, outlier analysis, global model training and incremental learning was further analyzed to facilitate rapid learning, which lay the foundation of future clinical implementation of radiation therapy knowledge models.

Intensity-modulated radiotherapy (IMRT) remains the standard of care for external beam radiation therapy for head and neck cancers. Planning for IMRT requires a trial-and-error approach that is completely dependent on planner expertise and time available for multiple iterations of manual optimization adjustments. Knowledge-based radiation therapy planning utilizes a database of previously planned Duke University Medical Center patient plans to create clinically comparable treatment plans by comparing the geometrical two-dimensional projections of the planning target volume (PTV) and organs at risk (OAR). These 2D beam's eye view (BEV) images are first aligned with squared error registration, then the similarity is computed using the mutual information (MI) metric. After the closest match is found, computed constraints and deformed fluence maps are entered into Eclipse treatment planning system to generate the new knowledge-based treatment plan. For this study, 20 randomly selected cases were matched against a database of 103 head and neck cancer cases. The resulting new plans were compared to their clinically planned counterparts. For these 20 cases, 13 proved to be dosimetrically comparable by evaluation of the PTV dose-volume histogram. In 92% of cases planned, at least half of the OARs were also deemed comparable or better than the original plan. These cases were planned in less than 25 minutes with no manual constraint objective adjustments, as opposed to many hours needed in clinical planning.

Abstract

Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) have become effective tools for treating cancer with radiation. Designing a high quality IMRT/VMAT treatment plan is time consuming. Different kinds of knowledge-based methods are being developed to reduce planning time and improve the plan quality by extracting knowledge from previous expert plans to form knowledge models and applying such models to the new patient cases. Currently, these methods are mostly limited to a particular cancer type and therefore various diseases types require training of multiple knowledge models with a large number of cases.

To investigate the feasibility of knowledge modeling of IMRT/VMAT treatment planning for multiple cancer types, a progressive study is conducted with a treatment planning knowledge model that quantifies correlations between patient pelvic anatomical features and the OAR sparing features. Low risk prostate plans with relatively simpler PTV-OAR geometry, which is the most common geometry type in previous knowledge based studies, are used to train the model as the starting point of the progressive modeling process. Cases with more complex PTV-OAR anatomies (prostate cancer cases with lymph node irradiation, and anal rectal cancer cases) are added to the training dataset one by one until the model prediction accuracies reach plateau. The DVHs predicted by the knowledge model for bladder, femoral heads and rectum are validated by cases from all three types of cases. Dosimetric parameters are extracted from the predicted DVHs and the corresponding actual plan values measure the prediction accuracy of this multi-disease type model. Further, its accuracy was also compared with the models trained by single disease type cases (including low risk prostate cancer, or type 1, high risk prostate cancer with lymph nodes, or type 2 and anal rectal cancer, or type 3).

Prediction accuracy reaches plateau when 6 high risk prostate cancer with lymph nodes irradiation cases and 8 anal rectal cancer cases were added to the training dataset. The determination coefficients R2 for the OARs are: Bladder: 0.90, rectum: 0.64 and femoral heads: 0.82. The prediction accuracies by the multi-disease type model and single-disease type models have no significant differences by F-test (p-value: bladder: 0.58, femoral head: 0.44, rectum: 0.97).

Conclusion:

Progressive knowledge modeling of OAR sparing for multiple cancer types in in the pelvic region is feasible and has comparable accuracy to single-disease type modeling.

Purpose: To retrospectively evaluate the impact of dosimetric parameters in treatment planning and dose discrepancies from patient inter-fractional motion on the high radiation pneumonitis (RP) occurrence rate in breast cancer patients receiving radiotherapy at First People's Hospital of Kunshan associated with Duke Kunshan University Medical Physics Graduate Program.Method: Dose-volume parameters were extracted from breast cancer patients’ treatment plans and were compared with corresponding experience-based thresholds associated with RP, including total dose, mean lung dose (MLD), percent of lung volume that receives a dose of 5 Gy or higher (V5), 13 Gy or higher (V13), 20 Gy or higher (V20), and 30 Gy or higher (V30). In addition, an in-house dose calculation system based on MATLAB and Varian Eclipse treatment planning system (TPS) was used to obtain actual dose distributions based on planning computed tomography (CT) scans, cone-beam computed tomography (CBCT) scans and couch shifts. The calculated actual dose was analyzed and compared to the original planning dose to evaluate inter-fractional motion induced dose discrepancies and their impacts on the occurrence of RP. Result: For patients diagnosed with RP, the median MLD is 15.38 Gy and the median V20 is 25.6%, which are higher than corresponding constraints 14 Gy and 24% respectively. Other dose-volume parameters were also much higher than their corresponding constraints for preventing RP. The inter-fractional patient motion induced discrepancies between planning and actual dose-volume parameters. For Patient 1, V20 increases from 23.93% to 28.33% due to the motion, which exceeds the V20 constraint of 24%. V30 increases to 17.99%, which is very close to the V30 constraint of 18%. For Patient 2, V10 increases from 32.00% to 35.43% and V13 increases from 29.86% to 32.99% due to the motion, both becoming to exceed the constraints. For Patient 3, MLD, V10, V13, V20 and V30 all decrease, where MLD and V20 decrease to the values lower than constraints. Summary: Dose-volume parameters in breast cancer treatment plans at First People's Hospital of Kunshan were reviewed. Existing results show that the dose-volume parameters related to RP were higher than internationally recommended constraints, which contributes to the high RP incidence. In addition, an automated MATLAB-based actual dose calculation system was developed and used to analyze the dose discrepancies between planning and actual dose distributions. Inter-fractional patient motions were found to cause discrepancies between the original planning dose and the actual dose.

Knowledge-based treatment planning models are commonly built from straightforward principles and utilize experience that it is able to learn from previous high-quality plans. This knowledge can be harnessed by having an e-learning system incorporating knowledge-based treatment planning models to serve as informative, efficient bases to train individuals to develop IMRT plans for a particular site while building confidence in utilizing these models in a clinical setting.

A previously developed beam angle selection model and a previously developed DVH prediction model for lung/mediastinum IMRT planning are used as the information centers within a directed e-learning system guided by scoring criteria and communicated with the trainees via a user interface ran from the treatment planning system (Eclipse). The scoring system serves both to illustrate relative quality of plans and to serve as a guide to facilitate directed changes within the plan. One patient serves as a benchmark to show skill development from the e-learning system and is completed without intervention. Five additional lung/mediastinum patients follow in the subsequent training pipeline where the models, graphical user interface (GUI) and trainer work with trainee’s directives and guide meaningful beam selection and tradeoffs within IMRT optimization. Five trainees with minimal treatment planning background were evaluated by both the scoring criteria and a physician to look for improved planning quality and relative effectiveness against the clinically delivered plan.

Trainees scored an average of 22.7% of the total points within the scoring criteria for their benchmark yet improved to an average of 51.9% compared to the clinically delivered plan which achieved 54.1% of the total potential points. Two of the five trainee final plans were rated as comparable to the clinically delivered by a physician and all five were noticeably improved by the physicians standards. For plans within the system, trainees performed on average 24.5% better than the clinically delivered plan with respect to the scoring criteria.

This first attempt at creating a dynamic interface communicating prior experience built in models to an end-user was approximately 10 hours to rapidly improve planning quality. It brings unexperienced planners to a level comparable of experienced dosimetrists for a specific treatment site and when used to inform decisions, the knowledge-based models aided in producing high quality plans.