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 l...

/pdf/automation-in-intensity-modulated-radiotherapy-treatment-2cxdf27c5i.pdf

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

Purpose Intensity-Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge-intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge-based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge-based approaches in IMRT and recent clinical validation results. Methods In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose-volume points, voxel-level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation. Conclusions The number of KBP-related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi-institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology.

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13526

Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches

Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant ...

https://www.birpublications.org/doi/pdf/10.1259/bjr.20190001

Applications and limitations of machine learning in radiation oncology

This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

Knowledge-based radiation treatment planning: A data-driven method survey.

To determine if the performance of a knowledge based RapidPlan (RP) planning model could be improved with an iterative learning process, i.e. if plans generated by an RP model could be used as new input to re-train the model and achieve better performance. Clinical VMAT plans from 83 patients presenting with head and neck cancer were selected to train an RP model, CL-1. With this model, new plans on the same patients were generated, and subsequently used as input to train a novel model, CL-2. Both models were validated on a cohort of 20 patients and dosimetric results compared. Another set of 83 plans was realised on the same patients with different planning criteria, by using a simple template with no attempt to manually improve the plan quality. Those plans were employed to train another model, TP-1. The differences between the plans generated by CL-1 and TP-1 for the validation cohort of patients were compared with respect to the differences between the original plans used to build the two models. The CL-2 model presented an improvement relative to CL-1, with higher R2 values and better regression plots. The mean doses to parallel organs decreased with CL-2, while D1% to serial organs increased (but not significantly). The different models CL-1 and TP-1 were able to yield plans according to each original strategy. A refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.

/pdf/rapidplan-knowledge-based-planning-iterative-learning-4n9omb4j5u.pdf

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

Knowledge-based planning (KBP) is a promising technique that can improve plan quality and increase planning efficiency. However, no attempts have been made to extend the domain of KBP for planners with different planning experiences so far. The purpose of this study was to quantify the potential gains for planners with different planning experiences after implementing KBP in intensity modulated radiation therapy (IMRT) plans for left-sided breast cancer patients. The model libraries were populated with 80 expert clinical plans from treated patients who previously received left-sided breast-conserving surgery and IMRT with simultaneously integrated boost. The libraries were created on the RapidPlanTM. 6 planners with different planning experiences (2 beginner planners, 2 junior planners and 2 senior planners) generated manual and KBP optimized plans for additional 10 patients, similar to those included in the model libraries. The plan qualities were compared between manual and KBP plans. All plans were capable of achieving the prescription requirement. There were almost no statistically significant differences in terms of the planning target volume (PTV) coverage and dose conformality. It was demonstrated that the doses for most of organs-at-risk (OARs) were on average lower or equal in KBP plans compared to manual plans except for the senior planners, where the very small differences were not statistically significant. KBP data showed a systematic trend to have superior dose sparing at most parameters for the heart and ipsilateral lung. The observed decrease in the doses to these OARs could be achieved, particularly for the beginner and junior planners. Many differences were statistically significant. It is feasible to generate acceptable IMRT plans after implementing KBP for left-sided breast cancer. KBP helps to effectively improve the quality of IMRT plans against the benchmark of manual plans for less experienced planners without any manual intervention. KBP showed promise for homogenizing the plan quality by transferring planning expertise from more experienced to less experienced planners.

/pdf/is-it-possible-for-knowledge-based-planning-to-improve-17l0afml8x.pdf

Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients?

To identify dosimetric parameters associated with acute hematologic toxicity (HT) in rectal cancer patients undergoing concurrent chemotherapy and intensity-modulated pelvic radiotherapy. Ninety-three rectal cancer patients receiving concurrent capecitabine and pelvic intensity-modulated radiation therapy (IMRT) were analyzed. Pelvic bone marrow (PBM) was contoured for each patient and divided into three subsites: lumbosacral spine (LSS), ilium, and lower pelvis (LP). The volume of each site receiving 5–40 Gy (V 5, V10, V15, V20, V30, and V40, respectively) as well as patient baseline clinical characteristics was calculated. The endpoint for hematologic toxicity was grade ≥ 2 (HT2+) leukopenia, neutropenia, anemia or thrombocytopenia. Logistic regression was used to analyze correlation between dosimetric parameters and grade ≥ 2 hematologic toxicity. Twenty-four in ninety-three patients experienced grade ≥ 2 hematologic toxicity. Only the dosimetric parameter V40 of lumbosacral spine was correlated with grade ≥ 2 hematologic toxicity. Increased pelvic lumbosacral spine V40 (LSS-V40) was associated with an increased grade ≥ 2 hematologic toxicity (p = 0.041). Patients with LSS-V40 ≥ 60 % had higher rates of grade ≥ 2 hematologic toxicity than did patients with lumbosacral spine V40 < 60 % (38.3 %, 18/47 vs.13 %, 6/46, p =0.005). On univariate and multivariate logistic regression analysis, lumbosacral spine V40 and gender was also the variable associated with grade ≥ 2 hematologic toxicity. Female patients were observed more likely to have grade ≥ 2 hematologic toxicity than male ones (46.9 %, 15/32 vs 14.8 %, 9/61, p =0.001). Lumbosacral spine -V40 was associated with clinically significant grade ≥ 2 hematologic toxicity. Keeping the lumbosacral spine -V40 < 60 % was associated with a 13 % risk of grade ≥ 2 hematologic toxicity in rectal cancer patients undergoing concurrent chemoradiotherapy.

/pdf/can-dosimetric-parameters-predict-acute-hematologic-toxicity-5c57l8lf4s.pdf

Can dosimetric parameters predict acute hematologic toxicity in rectal cancer patients treated with intensity-modulated pelvic radiotherapy?

Purpose:
To evaluate whether Auto-Planning based volumetric-modulated radiotherapy (auto-VMAT) can reduce manual interaction time during treatment planning and improve plan quality for rectal cancer radiotherapy.

Methods:
Ten rectal cancer patients (stage II and III) after radical resection using Dixon surgery were enrolled. All patients were treated with VMAT technique. The manual VMAT plans (man-VMAT) were designed in the Pinnacle treatment planning system (Version 9.10) following the standard treatment planning procedure developed in our department. Clinical plans were manually designed by our experienced dosimetrists. Additionally, an auto-VMAT plan was created for each patient using Auto-Planning module. However, manual interaction was still applied to meet the clinical requirements. The treatment planning time and plan quality surrogated by the DVH parameters were compared between manual and automated plans.

Results:
The total planning time and manual interaction time were 50.38 and 4.47 min for the auto-VMAT and 36.81 and 16.94 min for the man-VMAT (t=60.14,−23.86; p=0.000, 0.000). In terms of plan quality, both plans meet the clinical requirements. The PTV homogeneity index (HI) and conformity index (CI) were 0.054 and 0.822 for the auto-VMAT and 0.059 and 0.815 for the man-VMAT (t=−1.72, 0.36;p=0.119,0.730).Compared to the man-VMAT, the auto-VMAT showed reduction of 11.9% and 0.7% in V40 and V50 of the bladder, respectively.The V30 and D mean were reduced by 14.0% and 5.1Gy in the left femur and 12.2% and 3.8Gy in the right femur.

Conclusion:
The Auto-Planning based VMAT plans not only shows similar or superior plan quality to the manual ones in the rectal cancer radiotherapy, but also improve the planning efficiency significantly. However, manual interactions are still required to achieve a clinically acceptable plan based on our experiences.

SU-F-T-358: Is Auto-Planning Useful for Volumetric-Modulated Arc Therapy Planning in Rectal Cancer Radiotherapy?

Purpose:
The purpose of this study is to compare dosimetric differences of Auto-planning and manual IMRT plan and investigate the feasibility and efficiency of the Auto-planning in the nasopharyngeal carcinoma (NPC).

Methods:
Ten patients with NPC treated were enrolled in this study. Two IMRT plans were generated for each patient: manually optimized clinical plan and the automatically optimized plan using the Pinnacle Auto-planning (Auto plan). All the plan parameters such as gantry angle, the maximum number of iterations, the maximum number of control points, minimum segment MU and area are same, except the optimization method. For each IMRT plan, dose-volume histograms (DVHs) were calculated for target volumes and organs at risk (OARs). DVHs of targets and OARs were compared. The endpoints of targets were the D2 (Dx means dose to x% of the target volume), D98, V95 (Vx means percent of volume that received x% of dose prescription) and V100. The endpoints of OARs were Dmax for spinal cord and brainstem. The parotid glands were evaluated by V30.

Results:
For PTV66, PTV60, the dose coverage from both plans were met the clinical requirements. For the PTV66 (PTV60), the mean and standard deviation of V95 were 99.85±0.16 (99.86±0.55), 99.93±0.10 (99.02±0.49) and V100 were 97.84±1.58 (96.59±1.44), 98.56±1.06 (96.94±1.02) for Auto-planning and manually plan, respectively. For sensitive structures the doses of spinal cord and brainstem were not exceed the limitation. The V30 of the parotid glands were similar in two planning methods. The DVH of Auto plan and manual IMRT plan were accepted by clinical requirement, but the dose distribution of Auto-planning were not met the clinical requirement.

Conclusion:
According to the preliminary result of this study, The Auto-planning can get feasible DVH to the early stage NPC. However, manual adjustments are still required to get the finally acceptable plans.

SU-F-T-357: Dosimetric Comparison of Automatic IMRT Planning and Manual IMRT Plan for Head and Neck Cancer

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Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients?

Can dosimetric parameters predict acute hematologic toxicity in rectal cancer patients treated with intensity-modulated pelvic radiotherapy?

SU-F-T-358: Is Auto-Planning Useful for Volumetric-Modulated Arc Therapy Planning in Rectal Cancer Radiotherapy?

SU-F-T-357: Dosimetric Comparison of Automatic IMRT Planning and Manual IMRT Plan for Head and Neck Cancer