Search

A study has been carried out to explore the impact by varying the number of arcs and beam arrangement on dose distributions. For this volumetric modulated arc therapy and 7-field, intensity-modulated radiation therapy plans have use for prostate cancer cases. The eclipse treatment planning system version 13.6 (Varian California, USA) was used to assess dosimetry data for 20 patients. All patients received intensity-modulated radiation therapy and volumetric modulated arc therapy plans with a varying number of arcs. 6MV X-Ray photon beam energy uses for each patient. Statistical plan assessments have been carried out for various dosimetric parameters to evaluate execution efficiency. There were no statistically significant changes (p>0.05) observed in D98% dose coverage while D2%, conformity index, homogeneity index, monitor unit, and treatment delivery time were showing statistically significant changes (p

The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning efficiency. The following method was adopted: AIP-SFFP generates a prostate IMRT plan through predictions of fluence maps using patient anatomy. No inverse planning is required. AIP-SFFP is centered on a custom-built deep learning (DL) neural network for fluence map prediction. Predictions are imported to a commercial treatment-planning system for dose calculation and plan generation. AIP-SFFP was demonstrated for prostate IMRT simultaneously-integrated-boost planning (58.8 Gy/70 Gy to PTV58.8 Gy/PTV70 Gy in 28 fx, PTV = Planning Target Volume). Training data was generated from 106 patients using a knowledge-based planning (KBP) plan generator. Two types of 2D projection images were designed to represent structures' sizes and locations, and a total of eight projections were utilized to describe targets and organs-at-risk. Projections at nine template beam angles were stacked as inputs for artificial intelligence (AI) training. 14 patients were used as independent tests. The generated test plans were compared with the plans from the KBP training plan generator and clinic practice. The following results were obtained: After normalization (PTV70 Gy V70 Gy = 95%), all 14 AI plans met institutional criteria. The coverage of PTV58.8 Gy in the AI plans was comparable to KBP and clinic plans without statistical significance. The whole body (BODY) D1cc and rectum D0.1cc of AI plans were slightly higher (

The selection of the incident angles of the treatment beams is a critical component of intensity modulated radiation therapy (IMRT) planning for lung cancer due to significant variations in tumor location, tumor size and patient anatomy. We investigate the feasibility of establishing a small set of standardized beam bouquets for planning. The set of beam bouquets were determined by learning the beam configuration features from 60 clinical lung IMRT plans designed by experienced planners. A k-medoids cluster analysis method was used to classify the beam configurations in the dataset. The appropriate number of clusters was determined by maximizing the value of average silhouette width of the classification. Once the number of clusters had been determined, the beam arrangements in each medoid of the clusters were designated as the standardized beam bouquet for the cluster. This standardized bouquet set was used to re-plan 20 cases randomly selected from the clinical database. The dosimetric quality of the plans using the beam bouquets was evaluated against the corresponding clinical plans by a paired t-test. The classification with six clusters has the largest average silhouette width value and hence would best represent the beam bouquet patterns in the dataset. The results shows that plans generated with a small number of standardized bouquets (e.g. 6) have comparable quality to that of clinical plans. These standardized beam configuration bouquets will potentially help improve plan efficiency and facilitate automated planning.

IMRT treatment planning requires consideration of two competing objectives: achieving the required amount of radiation for the planning target volume and minimizing the amount of radiation delivered to all other tissues. It is important for planners to understand the tradeoff between competing factors so that the time-consuming human interaction loop (plan-evaluate-modify) can be eliminated. Treatment-plan-surface models have been proposed as a decision support tool to aid treatment planners and clinicians in choosing between rival treatment plans in a multi-plan environment. In this paper, an empirical approach is introduced to determine the minimum number of treatment plans (minimum knowledge base) required to build accurate representations of the IMRT plan surface in order to predict organ-at-risk (OAR) dose-volume (DV) levels and complications as a function of input DV constraint settings corresponding to all involved OARs in the plan. We have tested our approach on five head and neck patients and five whole pelvis/prostate patients. Our results suggest that approximately 30 plans were sufficient to predict DV levels with less than 3% relative error in both head and neck and whole pelvis/prostate cases. In addition, approximately 30–60 plans were sufficient to predict saliva flow rate with less than 2% relative error and to classify rectal bleeding with an accuracy of 90%.

The aim of this study was to evaluate the effectiveness and efficiency in inverse IMRT planning of one-step optimization with the step-and-shoot (SS) technique as compared to traditional two-step optimization using the sliding windows (SW) technique. The Pinnacle IMRT TPS allows both one-step and two-step approaches. The same beam setup for five head-and-neck tumor patients and dose-volume constraints were applied for all optimization methods. Two-step plans were produced converting the ideal fluence with or without a smoothing filter into the SW sequence. One-step plans, based on direct machine parameter optimization (DMPO), had the maximum number of segments per beam set at 8, 10, 12, producing a directly deliverable sequence. Moreover, the plans were generated whether a split-beam was used or not. Total monitor units (MUs), overall treatment time, cost function and dose-volume histograms (DVHs) were estimated for each plan. PTV conformality and homogeneity indexes and normal tissue complication probability (NTCP) that are the basis for improving therapeutic gain, as well as non-tumor integral dose (NTID), were evaluated. A two-sided t-test was used to compare quantitative variables. All plans showed similar target coverage. Compared to two-step SW optimization, the DMPO-SS plans resulted in lower MUs (20%), NTID (4%) as well as NTCP values. Differences of about 15-20% in the treatment delivery time were registered. DMPO generates less complex plans with identical PTV coverage, providing lower NTCP and NTID, which is expected to reduce the risk of secondary cancer. It is an effective and efficient method and, if available, it should be favored over the two-step IMRT planning.

Intensity-modulated radiotherapy (IMRT) plans are often complex, increasing the potential for dosimetric errors and prolonged treatment times. The purpose of this study is to evaluate the effectiveness and efficiency of one-step optimization as compared to conventional two-step optimization in inverse IMRT planning and to investigate the tradeoff between the number of segments and IMRT plan quality. Ten IMRT cases were studied, including five prostate patients and five nasopharynx patients. For each patient, seven research plans were generated with the same beam angles and objectives as the clinical plan using a commercial treatment planning system with the ability to perform one-step and two-step optimization. Two-step plans had the number of intensity levels set to 10, 5 and 3. One-step plans had the maximum number of segments set at 80, 60, 40 and 20 for prostate, and 100, 65, 50 and 25 for nasopharynx. When compared with two-step plans with similar numbers of segments, one-step plans resulted in lower MUs, higher homogeneity and conformal indices, and lower doses to sensitive structures. One-step optimization is an effective method for simplifying IMRT plans, resulting in a significant reduction in the number of segments for step and shoot delivery without significantly sacrificing plan quality.

Intensity-modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time manually adjusting IMRT optimization parameters such as dose limits and costlet weights in order to obtain a clinically acceptable plan. In this paper, we describe two main advances that simplify the parameter adjustment process for five-field prostate IMRT planning. First, we report the results of a sensitivity analysis that quantifies the effect of each hand-tunable parameter of the IMRT cost function on each clinical objective and the overall quality of the resulting plan. Second, we show that a recursive random search over the six most sensitive parameters as an outer loop in IMRT planning can quickly and automatically determine parameters for the cost function that lead to a plan meeting the clinical requirements. Our experiments on a ten-patient dataset show that for 70% of the cases, we can automatically determine a plan in 10 min (on the average) that is either clinically acceptable or requires only minor adjustment by the planner. The outer-loop optimization can be easily integrated into a traditional IMRT planning system.

Purpose: For lung cancer patients treated with intensity modulated radiation therapy (IMRT) technique, several dosimetric indices of lungs such as the mean lung dose, V30, V20 and V10 have drawn high attention, given their close correlations with the occurrence of radiation pneumonitis. In this study, we developed a method to predict the indices V30, V20 and V10 before treatment planning. Methods and Materials: The work in this study utilises anatomical information to predict dosimetric indices of lungs for IMRT plans. 15 shells surrounding the PTV were created and each shell comprised voxels whose distances to PTV surface were within a certain distance. The overlap regions of the lungs and the 15 shells were determined. A linear predicting function is employed to map volumes of these regions to dosimetric indices, where parameters were fitted using previous IMRT plans from a training database consisting of 43 cases. The effectiveness of the predicting function was validated using another 14 plans. Results: Dosimetric indices of the 14 patients in validation cohort were calculated using the linear predicting function. The predicted indices were compared with the indices derived from the clinically accepted treatment plans. The absolute differences between calculated V30 and the actual clinical values of 14 plans had a mean value of 1.6% and a standard deviation of 1.0%. The mean value and standard deviation for absolute differences of V20 were 1.5% and 1.0%. And for V10, these values were 3.6% and 2.6%. Conclusions: The method proposed here can predict dosimetric indices of lungs before treatment planning using geometric information of delineated PTVs and lungs. The results show that the prediction error of V30 and V20 are less than about 2.0%.

Background and purpose: Treatment of the contralateral neck after previous ipsilateral intensity modulated radiation therapy (IMRT) for head and neck cancer is a challenging problem. We have developed a technique that limits the cumulative dose to the spinal cord and brainstem while maximizing coverage of a planning target volume (PTV) in the contralateral neck. Our case involves a patient with right tonsil carcinoma who was given ipsilateral IMRT with 70Gy in 35 fractions (Plan A). A left neck recurrence was detected 14 months later. The patient underwent a neck dissection followed by postoperative left neck radiation to a dose of 66 Gy in 33 fractions (Plan B). Materials and Methods: The spinal cord-brainstem margin (SCBM) was defined as the spinal cord and brainstem with a 1.0 cm margin. Plan A was recalculated on the postoperative CT scan but the fluence outside of SCBM was deleted. A further modification of Plan A resulted in a base plan that was summed with Plan B to evaluate the cumulative dose received by the spinal cord and brainstem. Plan B alone was used to evaluate for coverage of the contralateral neck PTV. Results: The maximum cumulative doses to the spinal cord with 0.5cm margin and brainstem with 0.5cm margin were 51.96 Gy and 45.60 Gy respectively. For Plan B, 100% of the prescribed dose covered 95% of PTVb1. Conclusion: The use of a modified ipsilateral IMRT plan as a base plan is an effective way to limit the cumulative dose to the spinal cord and brainstem while enabling coverage of a PTV in the contralateral neck.