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Background and Purpose: To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT)., Materials and Methods: The AI planning platform has three components: (1) a graphical user interface (GUI) is built within the framework of a commercial treatment planning system (TPS). The GUI allows AI models to run remotely on a designated workstation configured with GPU acceleration. (2) A template plan is automatically prepared involving both clinical and AI considerations, which include contour evaluation, isocenter placement, and beam/collimator jaw placement. (3) A well-orchestrated suite of AI models predicts optimal fluence maps, which are imported into TPS for dose calculation followed by an optional automatic fine-tuning. Six AI models provide flexible tradeoffs in parotid sparing and Planning Target Volume (PTV)-organ-at-risk (OAR) preferences. Planners could examine the plan dose distribution and make further modifications as clinically needed. The performance of the AI plans was compared to the corresponding clinical plans., Results: The average plan generation time including manual operations was 10-15  min per case, with each AI model prediction taking ∼1 s. The six AI plans form a wide range of tradeoff choices between left and right parotids and between PTV and OARs compared with corresponding clinical plans, which correctly reflected their tradeoff designs., Conclusion: The in-house AI IMRT treatment planning platform was developed and is available for clinical use at our institution. The process demonstrates outstanding performance and robustness of the AI platform and provides sufficient validation., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To assess three advanced radiation therapy treatment planning tools on the intensity-modulated radiation therapy (IMRT) quality and consistency when compared to the clinically approved plans, referred as manual plans, which were planned without using any of these advanced planning tools., Materials and Methods: Three advanced radiation therapy treatment planning tools, including auto-planning, knowledge-based planning, and multiple criteria optimization, were assessed on 20 previously treated clinical cases. Three institutions participated in this study, each with expertise in one of these tools. The twenty cases were retrospectively selected from Cleveland Clinic, including five head-and-neck (HN) cases, five brain cases, five prostate with pelvic lymph nodes cases, and five spine cases. A set of general planning objectives and organs-at-risk (OAR) dose constraints for each disease site from Cleveland Clinic was shared with other two institutions. A total of 60 IMRT research plans (20 from each institution) were designed with the same beam configuration as in the respective manual plans. For each disease site, detailed isodoseline distributions and dose volume histograms for a randomly selected representative case were compared among the three research plans and manual plan. In addition, dosimetric endpoints of five cases for each site were compared., Results: Compared to the manual plans, the research plans using advanced tools showed substantial improvement for the HN patient cases, including the maximum dose to the spinal cord and brainstem and mean dose to the parotid glands. For the brain, prostate, and spine cases, the four types of plans were comparable based on dosimetric endpoint comparisons., Conclusion: With minimal planner interventions, advanced treatment planning tools are clinically useful, producing a plan quality similarly to or better than manual plans, improving plan consistency. For difficult cases such as HN cancer, advanced planning tools can further reduce radiation doses to numerous OARs while delivering adequate dose to the tumor targets., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The purpose of this work was to evaluate the potential of a new transmission detector for real-time quality assurance of dynamic-MLC-based radiotherapy. The accuracy of detecting dose variation and static/dynamic MLC position deviations was measured, as well as the impact of the device on the radiation field (surface dose, transmission). Measured dose variations agreed with the known variations within 0.3%. The measurement of static and dynamic MLC position deviations matched the known deviations with high accuracy (0.7-1.2 mm). The absorption of the device was minimal (~ 1%). The increased surface dose was small (1%-9%) but, when added to existing collimator scatter effects could become significant at large field sizes (≥ 30 × 30 cm2). Overall the accuracy and speed of the device show good potential for real-time quality assurance., (© 2016 The Authors.)

A recent publication indicated that the patient anatomical feature (PAF) model was capable of predicting optimal objectives based on past experience. In this study, the benefits of IMRT optimization using PAF-predicted objectives as guidance for prostate were evaluated. Three different optimization methods were compared.1) Expert Plan: Ten prostate cases (16 plans) were planned by an expert planner using conventional trial-and-error approach started with institutional modified OAR and PTV constraints. Optimization was stopped at 150 iterations and that plan was saved as Expert Plan. 2) Clinical Plan: The planner would keep working on the Expert Plan till he was satisfied with the dosimetric quality and the final plan was referred to as Clinical Plan. 3) PAF Plan: A third sets of plans for the same ten patients were generated fully automatically using predicted DVHs as guidance. The optimization was based on PAF-based predicted objectives, and was continued to 150 iterations without human interaction. DMAX and D98% for PTV, DMAX for femoral heads, DMAX, D10cc, D25%/D17%, and D40% for bladder/rectum were compared. Clinical Plans are further optimized with more iterations and adjustments, but in general provided limited dosimetric benefits over Expert Plans. PTV D98% agreed within 2.31% among Expert, Clinical, and PAF plans. Between Clinical and PAF Plans, differences for DMAX of PTV, bladder, and rectum were within 2.65%, 2.46%, and 2.20%, respectively. Bladder D10cc was higher for PAF but < 1.54% in general. Bladder D25% and D40% were lower for PAF, by up to 7.71% and 6.81%, respectively. Rectum D10cc, D17%, and D40% were 2.11%, 2.72%, and 0.27% lower for PAF, respectively. DMAX for femoral heads were comparable (< 35 Gy on average). Compared to Clinical Plan (Primary + Boost), the average optimization time for PAF plan was reduced by 5.2 min on average, with a maximum reduction of 7.1min. Total numbers of MUs per plan for PAF Plans were lower than Clinical Plans, indicating better delivery efficiency. The PAF-guided planning process is capable of generating clinical-quality prostate IMRT plans with no human intervention. Compared to manual optimization, this automatic optimization increases planning and delivery efficiency, while maintainingplan quality.

The purpose of this study was to evaluate the effect of dose calculation accuracy and the use of an intermediate dose calculation step during the optimization of intensity-modulated radiation therapy (IMRT) planning on the final plan quality for lung cancer patients. This study included replanning for 11 randomly selected free-breathing lung IMRT plans. The original plans were optimized using a fast pencil beam convolution algorithm. After optimization, the final dose calculation was performed using the analytical anisotropic algorithm (AAA). The Varian Treatment Planning System (TPS) Eclipse v11, includes an option to perform intermediate dose calculation during optimization using the AAA. The new plans were created using this intermediate dose calculation during optimization with the same planning objectives and dose constraints as in the original plan. Differences in dosimetric parameters for the planning target volume (PTV) dose coverage, organs-at-risk (OARs) dose sparing, and the number of monitor units (MU) between the original and new plans were analyzed. Statistical significance was determined with a p-value of less than 0.05. All plans were normalized to cover 95% of the PTV with the prescription dose. Compared with the original plans, the PTV in the new plans had on average a lower maximum dose (69.45 vs. 71.96Gy, p = 0.005), a better homogeneity index (HI) (0.08 vs. 0.12, p = 0.002), and a better conformity index (CI) (0.69 vs. 0.59, p = 0.003). In the new plans, lung sparing was increased as the volumes receiving 5, 10, and 30 Gy were reduced when compared to the original plans (40.39% vs. 42.73%, p = 0.005; 28.93% vs. 30.40%, p = 0.001; 14.11%vs. 14.84%, p = 0.031). The volume receiving 20 Gy was not significantly lower (19.60% vs. 20.38%, p = 0.052). Further, the mean dose to the lung was reduced in the new plans (11.55 vs. 12.12 Gy, p = 0.024). For the esophagus, the mean dose, the maximum dose, and the volumes receiving 20 and 60 Gy were lower in the new plans than in the original plans (17.91 vs. 19.24 Gy, p = 0.004; 57.32vs. 59.81 Gy, p = 0.020; 39.34% vs. 41.59%, p = 0.097; 12.56%vs. 15.35%, p = 0.101). For the heart, the mean dose, the maximum dose, and the volume receiving 40 Gy were also lower in new plans (11.07 vs. 12.04 Gy, p = 0.007; 56.41 vs. 57.7 Gy, p = 0.027; 7.16% vs. 9.37%, p= 0.012). The maximum dose to the spinal cord in the new plans was significantly lower than in the original IMRT plans (29.1 vs. 31.39Gy, p = 0.014). Difference in MU between the IMRT plans was not significant (1216.90 vs. 1198.91, p = 0.328). In comparison to the original plans, the number of iterations needed to meet the optimization objectives in the new plans was reduced by a factor of 2 (2-3 vs. 5-6 iterations). Further, optimization was 30% faster corresponding to an average time savings of 10-15 min for the reoptimized plans. Accuracy of the dose calculation algorithm during optimization has an impact on planning efficiency, as well as on the final plan dosimetric quality. For lung IMRT treatment planning, utilizing the intermediate dose calculation during optimization is feasible for dose homogeneity improvement of the PTV and for improvement of optimization efficiency.

We present a technique for planning and verification of craniospinal treatment with the patient in the supine position. Treatment delivery and verification is streamlined through the use of modern imaging techniques. Treatments use two lateral brain fields abutted to a single or pair of posterior spine fields. Treatment delivery is simplified by aligning all isocenters in the anterior-posterior and lateral directions. Patient positioning is accomplished via on-board kV imaging. Verification of field shape and junctions is accomplished with BB placement and MV portal imaging. Daily treatment is simplified by using only longitundinal couch shifts, which are recorded in the patient chart and RV database. The technique is simple to implement in a clinic that is already using a similar beam arrangement with the patient prone. It requires no additional devices to be fabricated (for immobilization or QA), and it takes advantage of all the existing elements of a modern linac.

The Leksell stereotactic Gamma Knifetrade mark uses radiation from 201 (60)Co sources that are focused to the center of a collimator helmet to deliver a high dose of radiation with minimal irradiation of proximal structures. This paper presents a method for fast verification of the irradiation time as calculated by the Leksell Gamma Knifetrade mark treatment planning software GammaPlan((R)). To obtain the irradiation time for each shot in the treatment plan, one must first accurately calculate the tissue maximum ratio (TMR) for each of the individual 201 beams. The algorithm presented in this paper begins with the determination of the geometrical relationship between the Gamma Knifetrade mark collimator helmet and the stereotactic frame. A group of reference points is measured to build a head model simulating the patient skull geometry. During radiosurgery, the isocenter of the collimator helmet is moved to the shot center. A group of spatial vectors describing the reference points at the skull surface is obtained by converting the Cartesian coordinates to Polar coordinates. For each individual beam, the three nearest reference vectors are found by ranking the relative angles. The depth that each beam penetrates the patient's skull to the isocenter is obtained via linear interpolation. The TMR for each beam then is compared with the TMR for the calibration setup, which is done using a spherical 8 cm radius phantom. This algorithm is applied to verify the treatment time calculated in GammaPlan((R)) Version 5.2. The results are shown to agree with GammaPlan((R)) within 3%.