With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.

/pdf/a-feasibility-study-for-predicting-optimal-radiation-therapy-49ntedgvnb.pdf

A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.

Purpose To incorporate a quality control tool, according to previous planning experience and patient-specific anatomic information, into the intensity-modulated radiotherapy (IMRT) plan generation process and to determine whether the tool improved treatment plan quality. Methods and Materials A retrospective study of 42 IMRT plans demonstrated a correlation between the fraction of organs at risk (OARs) overlapping the planning target volume and the mean dose. This yielded a model, predicted dose = prescription dose (0.2 + 0.8 [1 − exp(−3 overlapping planning target volume/volume of OAR)]), that predicted the achievable mean doses according to the planning target volume overlap/volume of OAR and the prescription dose. The model was incorporated into the planning process by way of a user-executable script that reported the predicted dose for any OAR. The script was introduced to clinicians engaged in IMRT planning and deployed thereafter. The script’s effect was evaluated by tracking δ = (mean dose-predicted dose)/predicted dose, the fraction by which the mean dose exceeded the model. Results All OARs under investigation (rectum and bladder in prostate cancer; parotid glands, esophagus, and larynx in head-and-neck cancer) exhibited both smaller δ and reduced variability after script implementation. These effects were substantial for the parotid glands, for which the previous δ = 0.28 ± 0.24 was reduced to δ = 0.13 ± 0.10. The clinical relevance was most evident in the subset of cases in which the parotid glands were potentially salvageable (predicted dose Conclusions This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process.

Experience-based quality control of clinical intensity-modulated radiotherapy planning.

Radiotherapy planning involves inherent tradeoffs: the primary mission, to treat the tumor with a high, uniform dose, is in conflict with normal tissue sparing. We seek to understand these tradeoffs on a case-to-case basis, by computing for each patient a database of Pareto optimal plans. A treatment plan is Pareto optimal if there does not exist another plan which is better in every measurable dimension. The set of all such plans is called the Pareto optimal surface. This article presents an algorithm for computing well distributed points on the (convex) Pareto optimal surface of a multiobjective programming problem. The algorithm is applied to intensity-modulated radiation therapy inverse planning problems, and results of a prostate case and a skull base case are presented, in three and four dimensions, investigating tradeoffs between tumor coverage and critical organ sparing.

/pdf/approximating-convex-pareto-surfaces-in-multiobjective-526ahhiwbb.pdf

Approximating convex pareto surfaces in multiobjective radiotherapy planning.

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.

As the use of breast reconstruction and postmastectomy radiotherapy (PMRT) has increased over the past decade, the typical approach to integrating radiotherapy with breast reconstruction has provoked intense controversy in the management of breast cancer. PMRT can lead to an increased frequency of complications in the reconstructed breast. Conversely, the reconstructed breast can increase the complexity of radiotherapy delivery. How to minimise the frequency of complications without compromising oncological or cosmetic outcomes of the reconstructed breast is an important shared multidisciplinary goal for oncologists and their patients. Several questions remain, however, regarding the type of reconstruction that should be used with PMRT, when reconstruction should be done relative to PMRT and whether radiotherapy treatment should be directed towards the tissue expander or the implant for women who opt for a two-stage expander-implant reconstruction. Following advances in the planning of radiotherapy treatment, new questions about the application of these technologies in the setting of breast reconstruction have arisen. In this Review, we address these questions by reviewing contemporary evidence on the optimal integration of radiotherapy and breast reconstruction in the management of breast cancer.

Radiotherapy in the setting of breast reconstruction: types, techniques, and timing

Purpose: To introduce and demonstrate a practical multiobjective treatment planning procedure for intensity-modulated radiation therapy (IMRT) planning Methods and Materials: The creation of a database of Pareto optimal treatment plans proceeds in two steps The first step solves an optimization problem that finds a single treatment plan which is close to a set of clinical aspirations This plan provides an example of what is feasible, and is then used to determine mutually satisfiable hard constraints for the subsequent generation of the plan database All optimizations are done using linear programming Results: The two-step procedure is applied to a brain, a prostate, and a lung case The plan databases created allow for the selection of a final treatment plan based on the observed tradeoffs between the various organs involved Conclusions: The proposed method reduces the human iteration time common in IMRT treatment planning Additionally, the database of plans, when properly viewed, allows the decision maker to make an informed final plan selection

An Approach for Practical Multiobjective IMRT Treatment Planning

Intensity modulated proton therapy for postmastectomy radiation of bilateral implant reconstructed breasts: A treatment planning study

Unlike conventional optimization with dose-volume (DV) constraints, multi-criteria optimization (MCO) with DV objectives provides tradeoff information which we believe is necessary for choosing better treatment plans. We show that the MCO formulation with DV objectives is better suited to convex approximation than conventional formulations with DV constraints. We provide a relaxation of the integer programming formulation which reduces the computation time for a single plan from over 5 h to about 2 min, without significantly compromising the results. We also derive a heuristic to improve on the relaxed solutions, adding only a few additional minutes of computation time. We apply these techniques to a skull based tumour case and a paraspinal tumour case. Based on a careful examination of the driving terms in the relaxed formulation and the heuristic, we argue that our techniques should apply more generally for DV objectives in multi-objective IMRT treatment planning.

http://iopscience.iop.org/article/10.1088/0031-9155/51/15/014/pdf

Dose?volume objectives in multi-criteria optimization

While a few physicists have designed new plan check automation solutions for their clinics, fewer, if any, managed to adapt existing solutions. As complex and varied as the systems they check, these programs must gain the full confidence of those who would run them on countless patient plans. The present automation effort, planCheck, therefore focuses on versatility and ease of implementation and verification. To demonstrate this, we apply planCheck to proton gantry, stereotactic proton gantry, stereotactic proton fixed beam (STAR), and IMRT treatments.

Automating checks of plan check automation

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