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This report describes the technique and initial experience using cone beam CT (CBCT) for localization of treatment targets in patients undergoing stereotactic body radiation therapy (SBRT). Patients selected for SBRT underwent 3-D or 4-D CT scans in a customized immobilization cradle. GTV, CTV, ITV, and PTV were defined. Intensity-modulated radiation beams, multiple 3-D conformal beams, or dynamic conformal arcs were delivered using a Varian 21EX with 120-leaf MLC. CBCT images were obtained prior to each fraction, and registered to the planning CT by using soft tissue and bony structures to assure accurate isocenter localization. Patients were repositioned for treatment based on the CBCT images. Radiographic images (kV, MV, or CBCT) were taken before and after beam delivery to further assess set-up accuracy. Ten patients with lung, liver, and spine lesions received 29 fractions of treatment using this technique. The prescription doses ranged 1250 ~ 6000 cGy in 1 ~ 5 fractions. Compared to traditional 2-D matching using bony structures, CBCT corrects target deviation from 1 mm to 15 mm, with an average of 5 mm. Comparison of pre-treatment to post-treatment radiographic images demonstrated an average 2 mm deviation (ranging from 0–4 mm). Improved immobilization may enhance positioning accuracy. Typical total “in-room” times for the patients are approximately 1 hour. CBCT-guided SBRT is feasible and enhances setup accuracy using 3-D anatomical information.

Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively (P= .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction (P= .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario (P= .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.

Purpose: To develop an automated optimization program to generate optimal beam settings for whole-breast radiation therapy driven by clinically oriented goals.Materials and Methods: Forty patients were retrospectively included in this study. Each patient’s planning images, contoured structures of planning target volumes, organs-at-risk, and breast wires were used to optimize for patient-specific–beam settings. Two beam geometries were available tangential beams only and tangential plus supraclavicular beams. Beam parameters included isocenter position, gantry, collimator, couch angles, and multileaf collimator shape. A geometry-based goal function was defined to determine such beam parameters to minimize out-of-field target volume and in-field ipsilateral lung volume. For each geometry, the weighting in the goal function was trained with 10 plans and tested on 10 additional plans. For each query patient, the optimal beam setting was searched for different gantry-isocenter pairs. Optimal fluence maps were generated by an in-house automatic fluence optimization program for target coverage and homogeneous dose distribution, and dose calculation was performed in Eclipse. Automatically generated plans were compared with manually generated plans for target coverage and lung and heart sparing.Results: The program successfully produced a set of beam parameters for every patient. Beam optimization time ranged from 10 to 120 s. The automatic plans had overall comparable plan quality to manually generated plans. For all testing cases, the mean target V95%was 91.0% for the automatic plans and 88.5% for manually generated plans. The mean ipsilateral lung V20Gywas lower for the automatic plans (15.2% vs 17.9%). The heart mean dose, maximum dose of the body, and conformity index were all comparable.Conclusion: We developed an automated goal-driven beam setting optimization program for whole-breast radiation therapy. It provides clinically relevant solutions based on previous clinical practice as well as patient specific anatomy on a substantially faster time frame.

Aim: The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method.Methods and Materials: We propose a knowledge-based plan quality inference method that references to similar plans in the historical database for patient-specific plan quality evaluation. First, a novel plan similarity metric with high-dimension geometrical difference quantification is utilized to retrieve similar plans. Subsequently, dosimetric statistical inferences are obtained from the selected similar plans. Two plan quality metrics—dosimetric result probability and dose deviation index—are proposed to quantify plan quality among prior similar plans. To evaluate the performance of the proposed method, we exported 927 clinically approved head and neck treatment plans. Eight organs at risk, including brain stem, cord, larynx, mandible, pharynx, oral cavity, left parotid and right parotid, were analyzed. Twelve suboptimal plans identified by dosimetric result probability were replanned to validate the capability of the proposed methods in identifying inferior plans.Results: After replanning, left and right parotid median doses are reduced by 31.7% and 18.2%, respectively; 83% of these cases would not be identified as suboptimal without the proposed similarity plan selection. Analysis of population plan quality reveals that average parotid sparing has been improving significantly over time (21.7% dosimetric result probability reduction from year 2006-2007 to year 2016-2017). Notably, the increasing dose sparing over time in retrospective plan quality analysis is strongly correlated with the increasing dose prescription ratios to the 2 planning targets, revealing the collective trend in planning conventions.Conclusions: The proposed similar plan retrieval and analysis methodology has been proven to be predictive of the current plan quality. Therefore, the proposed workflow can potentially be applied in the clinics as a real-time plan quality assurance tool. The proposed metrics can also serve the purpose of plan quality analytics in finding connections and historical trends in the clinical treatment planning workflow.