A new predictive parameter for dose-volume metrics in intensity-modulated radiation therapy planning for prostate cancer: Initial phantom study.

BACKGROUND: Organ-at-risk (OAR) sparing is often assessed using an overlap volume-based parameter, defined as the ratio of the volume of OAR that overlaps the planning target volume (PTV) to the whole OAR volume. However, this conventional overlap-based predictive parameter (COPP) does not consider the volume relationship between the PTV and OAR. PURPOSE: We propose a new overlap-based predictive parameter that consider the PTV volume. The effectiveness of proposed overlap-based predictive parameter (POPP) is evaluated compared with COPP. METHODS: We defined as POPP = (overlap volume between OAR and PTV/OAR volume) × (PTV volume/OAR volume). We generated intensity modulated radiation therapy (IMRT) based on step and shoot technique, and volumetric modulated arc therapy (VMAT) plans with the Auto-Planning module of Pinnacle3 treatment planning system (v14.0, Philips Medical Systems, Fitchburg, WI) using the American Association of Physicists in Medicine Task Group (TG119) prostate phantom. The relationship between the position and size of the prostate phantom was systematically modified to simulate various geometric arrangements. The correlation between overlap-based predictive parameters (COPP and POPP) and dose-volume metrics (mean dose, V70Gy, V60Gy, and V37.5 Gy for rectum and bladder) was investigated using linear regression analysis. RESULTS: Our results indicated POPP was better than COPP in predicting intermediate-dose metrics. The bladder results showed a trend similar to that of the rectum. The correlation coefficient of POPP was significantly greater than that of COPP in < 62 Gy (82% of the prescribed dose) region for IMRT and in < 55 Gy (73% of the prescribed dose) region for VMAT regarding the rectum (p < 0.05). CONCLUSIONS: POPP is superior to COPP for creating predictive models at an intermediate-dose level. Because rectal bleeding and bladder toxicity can be associated with intermediate-doses as well as high-doses, it is important to predict dose-volume metrics for various dose levels. POPP is a useful parameter for predicting dose-volume metrics and assisting the generation of treatment plans.

Exploration of overlap volumes for radiotherapy plan evaluation with the aim of healthy tissue sparing.

PURPOSE: Development of a novel interactive visualization approach for the exploration of radiotherapy treatment plans with a focus on overlap volumes with the aim of healthy tissue sparing. METHODS: We propose a visualization approach to include overlap volumes in the radiotherapy treatment plan evaluation process. Quantitative properties can be interactively explored to identify critical regions and used to steer the visualization for a detailed inspection of candidates. We evaluated our approach with a user study covering the individual visualizations and their interactions regarding helpfulness, comprehensibility, intuitiveness, decision-making and speed. RESULTS: A user study with three domain experts was conducted using our software and evaluating five data sets each representing a different type of cancer and location by performing a set of tasks and filling out a questionnaire. The results show that the visualizations and interactions help to identify and evaluate overlap volumes according to their physical and dose properties. Furthermore, the task of finding dose hot spots can also benefit from our approach. CONCLUSIONS: The results indicate the potential to enhance the current treatment plan evaluation process in terms of healthy tissue sparing.

Multi-center dosimetric predictions to improve plan quality for brachytherapy for cervical cancer treatment.

BACKGROUND AND PURPOSE: Image-guided adaptive brachytherapy (IGABT) is an important modality in the cervical cancer treatment, and plan quality is sensitive to time pressure in the workflow. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) has been demonstrated to detect suboptimal plans (outliers). This analysis quantifies the possible improvement of plans detected as outliers, and investigates its suitability as a clinical QA tool in a multi-center setting. MATERIALS AND METHODS: In previous work OVH-based models were investigated for the use of QA. In this work a total of 160 plans of 68 patients treated in accordance with the current state-of-the-art IGABT protocol from Erasmus MC (EMC) were analyzed, with a model based on 120 plans (60 patients) from UMC Utrecht (UMCU). Machine-learning models were trained to define QA thresholds, and to predict dose D2cm3 to bladder, rectum, sigmoid and small bowel with the help of OVHs of the EMC cohort. Plans out of set thresholds (outliers) were investigated and retrospectively replanned based on predicted D2cm3 values. RESULTS: Analysis of replanned plans demonstrated a median improvement of 0.62 Gy for all Organs At Risk (OARs) combined and an improvement for 96 % of all replanned plans. Outlier status was resolved for 36 % of the replanned plans. The majority of the plans that could not be replanned were reported having implantation complications or insufficient coverage due to tumor geometry. CONCLUSION: OVH-based QA models can detect suboptimal plans, including both unproblematic BT applications and suboptimal planning circumstances in general. OVH-based QA models demonstrate potential for clinical use in terms of performance and user-friendliness, and could be used for knowledge transfer between institutes. Further research is necessary to differentiate between (sub)optimal planning circumstances.

Multi-center analysis of machine-learning predicted dose parameters in brachytherapy for cervical cancer.

BACKGROUND AND PURPOSE: Image-guided adaptive brachytherapy (IGABT) is a key component in the treatment of cervical cancer, but the nature of the clinical workflow makes it vulnerable to suboptimal plans, as the theoretical optimal plan depends heavily on organ configuration. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) is a promising tool to detect such suboptimal plans, and in this analysis its suitability as a multi-institutional clinical QA tool is investigated. MATERIALS AND METHODS: A total of 223 plans of 145 patients treated in accordance with the current state-of-the-art IGABT protocols from UMC Utrecht (UMCU) and Erasmus MC (EMC) were included. Machine-learning models were trained to predict dose D2cm3 to bladder, rectum, sigmoid and small bowel with the help of OVHs. For this strategy, points are sampled on the organs-at-risk (OARs), and the distances of the sampled points to the target are computed and combined in a histogram. Machine-learning models can then be trained to predict dose-volume histograms (DVHs) for unseen data. Single-center model robustness to needle use and applicator type and multi-center model translatability were investigated. Performance of models was assessed by the difference between planned (clinical) and predicted D2cm3 values. RESULTS: Intra-validation of UMCU data demonstrated OVH model robustness to needle use and applicator type. The model trained on UMCU data was found to be robust within the same protocol on EMC data, for all investigated OARs. Mean squared error between planned and predicted D2cm3 values of OARs ranged between 0.13 and 0.40 Gy within the same protocol, indicating model translatability. For the former protocol cohort of Erasmus MC large deviations were found between the planned and predicted D2cm3 values, indicating plan deviation from protocol. Mean squared error for this cohort ranged from 0.84 to 4.71 Gy. CONCLUSION: OVH-based models can provide a solid basis for multi-institutional QA when trained on a sufficiently strict protocol. Further research will quantify the model's impact as a QA tool.

Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics.

BACKGROUND AND PURPOSE: Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis. MATERIAL AND METHODS: A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort). RESULTS: For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and -4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: -0.1 ± 1.1 Gy and -0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning. CONCLUSION: An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.

A simple algorithm to predict non-compliance with organ at risk dose-volume constraints when planning intensity modulated post-prostatectomy radiation treatment: 'Why we should put the CART before the horse'.

INTRODUCTION: It is not always apparent when the optimal IMRT/VMAT plan for post-prostatectomy radiotherapy (PPRT) has been achieved. Individual variation in patient anatomy is a key contributor. This study aimed to create a model to determine the probability of rectum and/or bladder doses exceeding planning goals based on individual patient anatomy prior to planning. METHODS: The IMRT/VMAT PPRT plans from 200 men were randomly and evenly allocated into the Training Cohort and the Validation Cohort. Univariate and multivariate analysis of the Training Cohort identified variables which impacted bladder and rectal doses. Significant variables were included in a Classification and Regression Tree (CART) analysis. The resultant algorithm was then applied to the Validation Cohort. RESULTS: On multivariate analysis, prescription dose; bladder and rectal volume; lymph node treatment; and percentage of bladder and rectal overlap with the PTV were significant variables. Following CART analysis, the overlap volume (OV) for both rectum (rectum overlap > 20%) and bladder (bladder overlap > 20%) were the key drivers of meeting planning goals. Treatment of pelvic lymph nodes was included as the secondary driving factor for bladder (but not rectal) dose. On application to the Validation Cohort, CART analysis predicted 95% and 87% of patients who would meet bladder and rectal planning goals respectively. CONCLUSIONS: A simple algorithm was developed to predict plan quality by using the OV of the bladder and rectum with the PTV. This algorithm may be used a priori to assess the planning process in the context of variable anatomy, and to optimise planning quality and efficiency.

Predictive performance of an OVH-based treatment planning quality assurance model for prostate VMAT: Assessing dependence on training cohort size and composition.

Radiotherapy treatment planning quality assurance models are used to assess overall plan quality in terms of dose-volume characteristics, by predicting an optimal dosimetry based on a dataset of prior cases (the training cohort). In this study, a treatment planning quality assurance model for prostate cancer patients treated with volumetric modulated arc therapy was developed using the concept of the overlap volume histogram for geometric comparison to the training cohort. The model was developed on the publically available Erasmus iCycle dataset in order to remove the effect of plan quality/inter-planner variability on the model's predictive capabilities. The model was used to predict anus, rectum, and bladder dose volume histograms. Two versions were developed: the n = 114 case (leave-one-out method) which made predictions using the complete Erasmus dataset, and the similarity index (SI)-based model which used a smaller training cohort allocated in order of geometric similarity determined using an overlap volume histogram-derived SI. The difference in mean dose (predicted-achieved) of the SI model at cohort sizes of 10, 20, 30, 40, 50, 75, and 100 was compared to the leave-one-out method for 5 patients, in an attempt to determine the "optimum" cohort size for the SI-based model in this dataset. Performance of the optimized SI model was compared to the leave-one-out method for all patients using the following metrics: difference in mean and median dose, difference in V65Gy and V75Gy (rectum only), similarity of predicted and achieved mean dose, and mean dose volume histograms residual. The "optimum" cohort size for the SI-based model was determined to be 45. The SI-based model implementing this cohort size yielded slightly better outcomes in all performance metrics for the rectum and anus, but worse for the bladder. SI-based training cohort allocation can lead to better predictive efficacy, but the cohort size should be optimized for each individual organ.

A method of respiratory phase optimization for better dose sparing of organs at risks: A validation study in patients with lung cancer.

BACKGROUND: To propose an effective and simple cost value function to determine an optimal respiratory phase for lung treatment using either respiratory gating or breath-hold technique. RESULTS: The optimized phase was obtained at a phase close to end inhalation in 11 out of 15 patients. For the rest of patients, the optimized phase was obtained at a phase close to end exhalation indicating that optimal phase can be patient specific. The mean doses of the Organs-at-risk (OARs) significantly decreased at the optimized phase without compromising the planning target volume (PTV) coverage (about 8% for all 3 OARs considered). MATERIALS AND METHODS: Fifteen lung patients were included for the feasibility test of the cost function. For all patients and all phases, delineation of the target volume and selected OARs such as esophagus, heart, and spinal cord was performed, and then cost values were calculated for all phases. After the breathing phases were ranked according to the cost values obtained, the relationship between score and dose distribution was evaluated by comparing dose volume histogram (DVH). CONCLUSIONS: The proposed cost value function can play an important role in choosing an optimal phase with minimal effort, that is, without actual plan optimization at all phases.

Prospective clinical validation of independent DVH prediction for plan QA in automatic treatment planning for prostate cancer patients.

PURPOSE: To prospectively investigate the use of an independent DVH prediction tool to detect outliers in the quality of fully automatically generated treatment plans for prostate cancer patients. MATERIALS/METHODS: A plan QA tool was developed to predict rectum, anus and bladder DVHs, based on overlap volume histograms and principal component analysis (PCA). The tool was trained with 22 automatically generated, clinical plans, and independently validated with 21 plans. Its use was prospectively investigated for 50 new plans by replanning in case of detected outliers. RESULTS: For rectum Dmean, V65Gy, V75Gy, anus Dmean, and bladder Dmean, the difference between predicted and achieved was within 0.4 Gy or 0.3% (SD within 1.8 Gy or 1.3%). Thirteen detected outliers were re-planned, leading to moderate but statistically significant improvements (mean, max): rectum Dmean (1.3 Gy, 3.4 Gy), V65Gy (2.7%, 4.2%), anus Dmean (1.6 Gy, 6.9 Gy), and bladder Dmean (1.5 Gy, 5.1 Gy). The rectum V75Gy of the new plans slightly increased (0.2%, p = 0.087). CONCLUSION: A high accuracy DVH prediction tool was developed and used for independent QA of automatically generated plans. In 28% of plans, minor dosimetric deviations were observed that could be improved by plan adjustments. Larger gains are expected for manually generated plans.

An effective calculation method for an overlap volume histogram descriptor and its application in IMRT plan retrieval.

To effectively calculate an overlap volume histogram (OVH) descriptor and improve intensity modulated radiation treatment (IMRT) planning by basing it on previous plans with similar features, a method based on morphology for OVH calculation was proposed and a novel similarity measurement was employed for retrieval of a suitable IMRT plan. First, the minimum and maximum distances between the tumor and organs at risk (OARs) were calculated as the start and end points for contraction or expansion, and a suitable step size for contraction or expansion was determined according to these distances. Then, a dilation or erosion morphology operator was employed to compute the OVH descriptor. Finally, the performance of IMRT plan retrieval was evaluated, where the area between OVH descriptors was taken as the similarity measurement, and a 3D reconstruction for each case was also performed for visual comparison. Twenty-eight nasopharyngeal carcinoma (NPC) cases were evaluated. The results show that OVH descriptors can be calculated effectively with the proposed method, and match well to the 3D geometrical features of the tumor and OARs. Further, the IMRT plan retrieval results match well based on a visual inspection of their 3D geometrical features, and an increase of the area between OVH descriptors leads to a decrease of visual similarity. Therefore, the proposed method can be used effectively for the calculation of an OVH descriptor as well as the retrieval of similar IMRT cases.

Feasibility of automatic IMRT planning for cervical cancer based on a database of previously-treated patients / 中华放射肿瘤学杂志

Objective To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer based on a database of overlap volume histogram (OVH) and high-quality cervical IMRT plans for previously-treated patients.Methods A database consisting of high-quality IMRT plans and OVHs from 200 cervical cancer patients was established.OVHs of another 26 cervical cancer patients were converted into gray level images to calculate the image similarity compared with those from the database.The planning optimization function of the patients from the database with the highest image similarity was selected and inherent Pinnacle3 scripts were utilized to automatically generate IMRT plan.Finally,the dosimetric parameters,plan quality and design time were statistically compared between the automatic and manual plans.Results The target coverage,conformity index and homogeneity index did not significantly differ between two plans (all P＞0.05).The V40,V45 and mean dose for the rectum in the automatic plans were significantly decreased by 6.1％,1.3％ and 50.7 cGy than those in the manual plans (all P＜0.05).Compared with the manual plans,the mean dose for the intestine and femur in the automatic plans were significantly reduced by 31.7 cGy and 188.9 cGy (both P＜0.05),whereas the mean dose for the ilium was slightly decreased by 92.3 cGy in the automatic plans (P＞ 0.05).The plan design time was shortened by 71％ in the automatic plans.Conclusions The automatic IMRT plans based on a database of OVH and high-quality IMRT plans can not only significantly shorten the plan design time,but also reduce the irradiated dose of normal tissues without compromising the target coverage and conformity index.

Feasibility of automatic IMRT planning for cervical cancer based on a database of previously-treated patients / 中华放射肿瘤学杂志

Objective@#To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer based on a database of overlap volume histogram (OVH) and high-quality cervical IMRT plans for previously-treated patients.@*Methods@#A database consisting of high-quality IMRT plans and OVHs from 200 cervical cancer patients was established. OVHs of another 26 cervical cancer patients were converted into gray level images to calculate the image similarity compared with those from the database. The planning optimization function of the patients from the database with the highest image similarity was selected and inherent Pinnacle3 scripts were utilized to automatically generate IMRT plan. Finally, the dosimetric parameters, plan quality and design time were statistically compared between the automatic and manual plans.@*Results@#The target coverage, conformity index and homogeneity index did not significantly differ between two plans (all P>0.05). The V40, V45 and mean dose for the rectum in the automatic plans were significantly decreased by 6.1%, 1.3% and 50.7 cGy than those in the manual plans (all P<0.05). Compared with the manual plans, the mean dose for the intestine and femur in the automatic plans were significantly reduced by 31.7 cGy and 188.9 cGy (both P<0.05), whereas the mean dose for the ilium was slightly decreased by 92.3 cGy in the automatic plans (P>0.05). The plan design time was shortened by 71% in the automatic plans.@*Conclusions@#The automatic IMRT plans based on a database of OVH and high-quality IMRT plans can not only significantly shorten the plan design time, but also reduce the irradiated dose of normal tissues without compromising the target coverage and conformity index.

A study of quality control method for IMRT planning based on prior knowledge and novel measures derived from both OVHs and DVHs.

Intensity-Modulated Radiation Therapy (IMRT) mathematically forms a large-scale optimization problem. The development of an IMRT plan is computationally expensive resulting in time-consuming, inefficient, and difficult to develop high-quality IMRT plans. By combining prior knowledge with proposed novel measures derived from both Overlap Volume Histogram (OVH) descriptors and Dose Volume Histograms (DVHs), a novel quality control method for IMRT planning is proposed to assure the high quality of IMRT plan. Clinical approved nasopharyngeal IMRT plans were employed for the experiments, where the reference plan is firstly retrieved from IMRT plan database for each query case by using measures derived from both OVH descriptors and DVHs. Then the DVHs of the reference plan are served as additional goals for the IMRT plan re-optimization. The experimental results show that the proposed method can effectively pick out those IMRT plans, whose quality could be improved substantially (i.e. the doses of their Clinical Targets Volume (CTV) could be effectively increased) and the dose of their Organs at Risk (OARs) could be reduced after the IMRT plan has being re-optimized. In conclusion, the proposed methods can effectively guarantee the high quality of the IMRT planning.

A quality control model that uses PTV-rectal distances to predict the lowest achievable rectum dose, improves IMRT planning for patients with prostate cancer.

BACKGROUND AND PURPOSE: To predict the lowest achievable rectum D35 for quality assurance of IMRT plans of prostate cancer patients. MATERIALS AND METHODS: For each of 24 patients from a database of 47 previously treated patients, the anatomy was compared to the anatomies of the other 46 to predict the minimal achievable rectum D35. The 24 patients were then replanned to obtain maximally reduced rectum D35. Next, the newly derived plans were added to the database to replace the original clinical plans, and new predictions of the lowest achievable rectum D35 were made. RESULTS: After replanning, the rectum D35 reduced by 9.3 Gy±6.1 (average±1 SD; p<0.001) compared to the original plan. The first predictions of the rectum D35 were 4.8 Gy±4.2 (average±1 SD; p<0.001) too high when evaluated with the new plans. After updating the database, the replanned and newly predicted rectum D35 agreed within 0.1 Gy±2.8 (average±1 SD; p=0.89). The doses to the bladder, anus and femoral heads did not increase compared to the original plans. CONCLUSIONS: For individual prostate patients, the lowest achievable rectum D35 in IMRT planning can be accurately predicted from dose distributions of previously treated patients by quantitative comparison of patient anatomies. These predictions can be used to quantitatively assess the quality of IMRT plans.