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.

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.

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.