A sensitivity-guided algorithm for automated determination of IMRT objective function parameters

Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning

Intensity-Modulated Radiation Therapy requires the manual adjustment to numerous treatment plan parameters (TPPs) through a trial-and-error process to deliver precise radiation doses to the target while minimizing exposure to surrounding healthy tissues. The goal is to achieve a dose distribution that adheres to a prescribed plan tailored to each patient. Developing an automated approach to optimize patient-specific prescriptions is valuable in scenarios where trade-off selection is uncertain and varies among patients. This study presents a proof-of-concept artificial intelligence (AI) system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to guide IMRT planning and achieve optimal, patient-specific prescriptions in aligned with a radiation oncologist's treatment objectives. We developed an in-house ANFIS-AI system utilizing Prescription Dose (PD) constraints to guide the optimization process toward achievable prescriptions. Mimicking human planning behavior, the AI system adjusts TPPs, represented as dose-volume constraints, to meet the prescribed dose goals. This process is informed by a Fuzzy Inference System (FIS) that incorporates prior knowledge from experienced planners, captured through "if-then" rules based on routine planning adjustments. The innovative aspect of our research lies in employing ANFIS's adaptive network to fine-tune the FIS components (membership functions and rule strengths), thereby enhancing the accuracy of the system. Once calibrated, the AI system modifies TPPs for each patient, progressing through acceptable prescription levels, from restrictive to clinically allowable. The system evaluates dosimetric parameters and compares dose distributions, dose-volume histograms, and dosimetric statistics between the conventional FIS and ANFIS. Results demonstrate that ANFIS consistently met dosimetric goals, outperforming FIS with a 0.7% improvement in mean dose conformity for the planning target volume (PTV) and a 28% reduction in mean dose exposure for organs at risk (OARs) in a C-Shape phantom. In a mock prostate phantom, ANFIS reduced the mean dose by 17.4% for the rectum and by 14.1% for the bladder. These findings highlight ANFIS's potential for efficient, accurate IMRT planning and its integration into clinical workflows.

Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer

BACKGROUND

Intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) are standard physical technologies of stereotactic body radiotherapy (SBRT) that are used for patients with non-small-cell lung cancer (NSCLC). The treatment plan quality depends on the experience of the planner and is limited by planning time. An automated planning process can save time and ensure a high-quality plan. This study aimed to introduce and demonstrate an automated planning procedure for SBRT for patients with NSCLC based on machine-learning algorithms. The automated planning was conducted in two steps: (1) determining patient-specific optimized beam orientations; (2) calculating the organs at risk (OAR) dose achievable for a given patient and setting these dosimetric parameters as optimization objectives. A model was developed using data of historical expertise plans based on support vector regression. The study cohort comprised patients with NSCLC who were treated using SBRT. A training cohort (N = 125) was used to calculate the beam orientations and dosimetric parameters for the lung as functions of the geometrical feature of each case. These plan-geometry relationships were used in a validation cohort (N = 30) to automatically establish the SBRT plan. The automatically generated plans were compared with clinical plans established by an experienced planner.

RESULTS

All 30 automated plans (100%) fulfilled the dose criteria for OARs and planning target volume (PTV) coverage, and were deemed acceptable according to evaluation by experienced radiation oncologists. An automated plan increased the mean maximum dose for ribs (31.6 ± 19.9 Gy vs. 36.6 ± 18.1 Gy, P < 0.05). The minimum, maximum, and mean dose; homogeneity index; conformation index to PTV; doses to other organs; and the total monitor units showed no significant differences between manual plans established by experts and automated plans (P > 0.05). The hands-on planning time was reduced from 40-60 min to 10-15 min.

CONCLUSION

An automated planning method using machine learning was proposed for NSCLC SBRT. Validation results showed that the proposed method decreased planning time without compromising plan quality. Plans generated by this method were acceptable for clinical use.

Evaluation of plan quality improvements in PlanIQ-guided Autoplanning

Aim

Philips recently integrated PlanIQ with Autoplan® in Pinnacle³ TPS (V16.2). The objective of the present work is to quantitatively demonstrate how this integration improves the plan quality.

Background

Pinnacle³ Autoplan® is the tool that generates the treatment plans with clinically acceptable plan quality with less manual intervention. In the recent past, a new tool called PlanIQ (Sun Nuclear Corp.) was introduced for a priori estimation of the best possible sparing of an organ at risk (OAR) for a given patient anatomy. Philips has recently integrated PlanIQ tool with Autoplan® for a seamless and efficient planning workflow.

Materials and methods

We have performed this evaluation in Pinnacle³ TPS (V.16.2) for the VMAT treatment technique. All plans were created using Varian True beam machine with the dual arc technique. Basically, we created two sets of VMAT plans using 6 MV photons. In the first set of VMAT plans (AP_RTOG), we used OAR goals from either RTOG guidelines to perform optimization using Autoplan®. Subsequently, we exported the same dataset to the PlanIQ system to perform feasibility analysis on the OAR goals. These newly obtained OAR goals from PlanIQ were used to generate the other set of plans (AP_PlanIQ plans). We compared the dosimetric results from these two sets of plans in five cases, such as brain, head & neck, lung, abdomen and prostate.

Results

We compared the dosimetric results for AP_RTOG and AP_PlanIQ plans. We used RTOG guidelines to evaluate the plans and observed that while both sets of plans were meeting the RTOG guidelines in terms of OAR sparing, the AP_PlanIQ plans were significantly better in terms of OAR sparing as compared to AP_RTOG plans without any compromise in the target coverage.

Conclusion

The results indicate that, although Autoplan helps achieve the user-defined goals without much manual intervention, the plan quality (OAR sparing) can be significantly improved without taking many iterative steps when PlanIQ suggested clinical goals are used in the Autoplan-based optimization.

Advances in knowledge

At present, there are no published material available about the efficacy of the integration of PlanIQ with Autoplanning®. In the present work, our objective is to evaluate the improvements in plan quality resulting from this integration.

Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations

Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.

Determination of optimal number of beams in direct machine parameter optimization-based intensity modulated radiotherapy for head and neck cases

This paper aims to introduce an algorithm called “sensitivity-based beam number selection (SBBNS)” for fully automated and case-specific determination of an optimal number of equispaced beams in intensity-modulated radiotherapy (IMRT). We tested the algorithm in five head and neck cases of varying complexity. We used direct machine parameter optimization method coupled with Auto Plan feature available in Pinnacle TPS (Version 9.10.0) for optimization. The Pearson correlation test shows a correlation of 0.88 between predicted and actual optimal number of beams, which indicates that SBBNS method is capable of predicting optimal number of beams for head and neck cases with reasonable accuracy. The major advantage of the algorithm is that it intrinsically takes into account various case- and machine-specific factors for the determination of optimal number. The study demonstrates that the algorithm can be effectively applied to IMRT scenarios to determine case specific and optimal number of beams for head and neck cases.

Automated IMRT planning with regional optimization using planning scripts

Intensity‐modulated radiation therapy (IMRT) has become a standard technique in radiation therapy for treating different types of cancers. Various class solutions have been developed for simple cases (e.g., localized prostate, whole breast) to generate IMRT plans efficiently. However, for more complex cases (e.g., head and neck, pelvic nodes), it can be time‐consuming for a planner to generate optimized IMRT plans. To generate optimal plans in these more complex cases which generally have multiple target volumes and organs at risk, it is often required to have additional IMRT optimization structures such as dose limiting ring structures, adjust beam geometry, select inverse planning objectives and associated weights, and additional IMRT objectives to reduce cold and hot spots in the dose distribution. These parameters are generally manually adjusted with a repeated trial and error approach during the optimization process. To improve IMRT planning efficiency in these more complex cases, an iterative method that incorporates some of these adjustment processes automatically in a planning script is designed, implemented, and validated. In particular, regional optimization has been implemented in an iterative way to reduce various hot or cold spots during the optimization process that begins with defining and automatic segmentation of hot and cold spots, introducing new objectives and their relative weights into inverse planning, and turn this into an iterative process with termination criteria. The method has been applied to three clinical sites: prostate with pelvic nodes, head and neck, and anal canal cancers, and has shown to reduce IMRT planning time significantly for clinical applications with improved plan quality. The IMRT planning scripts have been used for more than 500 clinical cases.

PACS numbers: 87.55.D, 87.55.de

A methodology for automatic intensity-modulated radiation treatment planning for lung cancer

In intensity-modulated radiotherapy (IMRT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and is often the most time-consuming aspect of the RT process. In this paper, we describe a methodology to automate the IMRT planning process in lung cancer cases, the goal being to improve the quality and consistency of treatment planning. This methodology (1) automatically sets beam angles based on a beam angle automation algorithm, (2) judiciously designs the planning structures, which were shown to be effective for all the lung cancer cases we studied, and (3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from a clinical trial of IMRT for lung cancer run at our institution. The 'autoplans' were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. We conclude that the mdaccAutoPlan system can potentially improve the quality and consistency of treatment planning for lung cancer.

Toward truly optimal IMRT dose distribution: inverse planning with voxel-specific penalty

PURPOSE

To establish an inverse planning framework with adjustable voxel penalty for more conformal IMRT dose distribution as well as improved interactive controllability over the regional dose distribution of the resultant plan.

MATERIALS AND METHOD

In the proposed coarse-to-fine planning scheme, a conventional inverse planning with organ specific parameters is first performed. The voxel penalty scheme is then "switched on" by allowing the prescription dose to change on an individual voxel scale according to the deviation of the actual voxel dose from the ideally desired dose. The rationale here is intuitive: when the dose at a voxel does not meet its ideal dose, it simply implies that this voxel is not competitive enough when compared with the ones that have met their planning goal. In this case, increasing the penalty of the voxel by varying the prescription can boost its competitiveness and thus improve its dose. After the prescription adjustment, the plan is re-optimized. The dose adjustment/re-optimization procedure is repeated until the resultant dose distribution cannot be improved anymore. The prescription adjustment on a finer scale can be accomplished either automatically or manually. In the latter case, the regions/voxels where a dose improvement is needed are selected visually, unlike in the automatic case where the selection is done purely based on the difference of the actual dose at a given voxel and its ideal prescription. The performance of the proposed method is evaluated using a head and neck and a prostate case.

RESULTS

An inverse planning framework with the voxel-specific penalty is established. By adjusting voxel prescriptions iteratively to boost the region where large mismatch between the actual calculated and desired doses occurs, substantial improvements can be achieved in the final dose distribution. The proposed method is applied to a head and neck case and a prostate case. For the former case, a significant reduction in the maximum dose to the brainstem is achieved while the PTV dose coverage is greatly improved. The doses to other organs at risk are also reduced, ranging from 10% to 30%. For the prostate case, the use of the voxel penalty scheme also results in vast improvements to the final dose distribution. The PTV experiences improved dose uniformity and the mean dose to the rectum and bladder is reduced by as much as 15%.

CONCLUSION

Introduction of the spatially non-uniform and adjustable prescription provides room for further improvements of currently achievable dose distributions and equips the planner with an effective tool to modify IMRT dose distributions interactively. The technique is easily implementable in any existing inverse planning platform, which should facilitate clinical IMRT planning process and, in future, off-line/on-line adaptive IMRT.

The minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT 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%.

Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework

PURPOSE

To predict organ-at-risk (OAR) complications as a function of dose-volume (DV) constraint settings without explicit plan computation in a multiplan intensity-modulated radiotherapy (IMRT) framework.

METHODS AND MATERIALS

Several plans were generated by varying the DV constraints (input features) on the OARs (multiplan framework), and the DV levels achieved by the OARs in the plans (plan properties) were modeled as a function of the imposed DV constraint settings. OAR complications were then predicted for each of the plans by using the imposed DV constraints alone (features) or in combination with modeled DV levels (plan properties) as input to machine learning (ML) algorithms. These ML approaches were used to model two OAR complications after head-and-neck and prostate IMRT: xerostomia, and Grade 2 rectal bleeding. Two-fold cross-validation was used for model verification and mean errors are reported.

RESULTS

Errors for modeling the achieved DV values as a function of constraint settings were 0-6%. In the head-and-neck case, the mean absolute prediction error of the saliva flow rate normalized to the pretreatment saliva flow rate was 0.42% with a 95% confidence interval of (0.41-0.43%). In the prostate case, an average prediction accuracy of 97.04% with a 95% confidence interval of (96.67-97.41%) was achieved for Grade 2 rectal bleeding complications.

CONCLUSIONS

ML can be used for predicting OAR complications during treatment planning allowing for alternative DV constraint settings to be assessed within the planning framework.

A multiplan treatment-planning framework: a paradigm shift for intensity-modulated radiotherapy

PURPOSE

To describe a multiplan intensity-modulated radiotherapy (IMRT) planning framework, and to describe a decision support system (DSS) for ranking multiple plans and modeling the planning surface.

METHODS AND MATERIALS

One hundred twenty-five plans were generated sequentially for a head-and-neck case and a pelvic case by varying the dose-volume constraints on each of the organs at risk (OARs). A DSS was used to rank plans according to dose-volume histogram (DVH) values, as well as equivalent uniform dose (EUD) values. Two methods for ranking treatment plans were evaluated: composite criteria and pre-emptive selection. The planning surface determined by the results was modeled using quadratic functions.

RESULTS

The DSS provided an easy-to-use interface for the comparison of multiple plan features. Plan ranking resulted in the identification of one to three "optimal" plans. The planning surface models had good predictive capability with respect to both DVH values and EUD values and generally, errors of <6%. Models generated by minimizing the maximum relative error had significantly lower relative errors than models obtained by minimizing the sum of squared errors. Using the quadratic model, plan properties for one OAR were determined as a function of the other OAR constraint settings. The modeled plan surface can then be used to understand the interdependence of competing planning objectives.

CONCLUSION

The DSS can be used to aid the planner in the selection of the most desirable plan. The collection of quadratic models constructed from the plan data to predict DVH and EUD values generally showed excellent agreement with the actual plan values.

IMRT treatment planning based on prioritizing prescription goals

Determining the 'best' optimization parameters in IMRT planning is typically a time-consuming trial-and-error process with no unambiguous termination point. Recently we and others proposed a goal-programming approach which better captures the desired prioritization of dosimetric goals. Here, individual prescription goals are addressed stepwise in their order of priority. In the first step, only the highest order goals are considered (target coverage and dose-limiting normal structures). In subsequent steps, the achievements of the previous steps are turned into hard constraints and lower priority goals are optimized, in turn, subject to higher priority constraints. So-called 'slip' factors were introduced to allow for slight, clinically acceptable violations of the constraints. Focusing on head and neck cases, we present several examples for this planning technique. The main advantages of the new optimization method are (i) its ability to generate plans that meet the clinical goals, as well as possible, without tuning any weighting factors or dose-volume constraints, and (ii) the ability to conveniently include more terms such as fluence map smoothness. Lower level goals can be optimized to the achievable limit without compromising higher order goals. The prioritized prescription-goal planning method allows for a more intuitive and human-time-efficient way of dealing with conflicting goals compared to the conventional trial-and-error method of varying weighting factors and dose-volume constraints.