Is the introduction of more advanced radiotherapy techniques for locally-advanced head and neck cancer associated with improved quality of life and reduced symptom burden?

Technical improvements in head and neck cancer radiotherapy over the last decade have resulted in substantial reductions in dose to organs-at-risk. For a mix of tumors, we saw less xerostomia moving from 3D-conformal to more advanced techniques. For oropharynx-only there were additional improvements, including in global quality-of-life and sticky saliva.

Analysis of EORTC-1219-DAHANCA-29 trial plans demonstrates the potential of knowledge-based planning to provide patient-specific treatment plan quality assurance.

Introduction: Radiotherapy treatment plan quality can influence clinical trial outcomes and general QA may not identify suboptimal organ-at-risk (OAR) sparing. We retrospectively performed patient-specific quality assurance (QA) of 100 head-and-neck cancer (HNC) plans from the EORTC-1219-DAHANCA-29 study.

Materials And Methods: A 177-patient RapidPlan (Varian Medical Systems) model comprising institutional HNC plans was used to QA trial plans (P).

Personalized automated treatment planning for breast plus locoregional lymph nodes using Hybrid RapidArc.

Purpose: Breast cancer patients who require locoregional lymph node (LLN) irradiation can be treated using a hybrid RapidArc technique combining 2 tangential and 3 RapidArc fields. Because the creation of hybrid RapidArc plans is complex and labor-intensive, we developed an automated treatment planning workflow using the scripting application programming interface of the Eclipse treatment planning system.

Methods And Materials: Fifteen patients (5 right- and 10 left-sided) previously treated with breast + LLN radiation therapy were replanned using the script.

Using a knowledge-based planning solution to select patients for proton therapy.

Background And Purpose: Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy.

A longitudinal evaluation of improvements in radiotherapy treatment plan quality for head and neck cancer patients.

Purpose: To investigate changes in head-and-neck cancer (HNC) plan quality following the introduction of new technologies and planning techniques in the last decade.

Methods And Materials: Thirty plans were selected from each of four successive periods (P). P1: 7-field static intensity-modulated radiotherapy (IMRT) with parotid gland sparing; P2: dual-arc volumetric-modulated arc therapy (VMAT, similar to P3-P4), including submandibular gland sparing; P3: inclusion of individual swallowing muscles and attempts to further reduce parotid and oral cavity doses through manual interactive optimization; P4: containing the same organs-at-risk (OARs) as P3, but automatically interactively optimized.

Detailed evaluation of an automated approach to interactive optimization for volumetric modulated arc therapy plans.

Purpose: Interactive optimization during treatment planning requires intermittent adjustment of organ-at-risk (OAR) objectives relative to the dose-volume histogram line. This is a labor-intensive process and the resulting plans are prone to variations in quality. The authors' in-house developed approach to automated interactive optimization (AIO) automatically moves the mouse cursor to adjust the position of on-screen optimization objectives.

Effect of Dosimetric Outliers on the Performance of a Commercial Knowledge-Based Planning Solution.

Purpose: RapidPlan, a commercial knowledge-based planning solution, uses a model library containing the geometry and associated dosimetry of existing plans. This model predicts achievable dosimetry for prospective patients that can be used to guide plan optimization. However, it is unknown how suboptimal model plans (outliers) influence the predictions or resulting plans.

Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans?

Background: Treatment plan quality assurance (QA) is important for clinical studies and for institutions aiming to generate near-optimal individualized treatment plans. However, determining how good a given plan is for that particular patient (individualized patient/plan QA, in contrast to running through a checklist of generic QA parameters applied to all patients) is difficult, time consuming and operator-dependent. We therefore evaluated the potential of RapidPlan, a commercial knowledge-based planning solution, to automate this process, by predicting achievable OAR doses for individual patients based on a model library consisting of historical plans with a range of organ-at-risk (OAR) to planning target volume (PTV) geometries and dosimetries.

Comparison of organ-at-risk sparing and plan robustness for spot-scanning proton therapy and volumetric modulated arc photon therapy in head-and-neck cancer.

Purpose: Proton radiotherapy for head-and-neck cancer (HNC) aims to improve organ-at-risk (OAR) sparing over photon radiotherapy. However, it may be less robust for setup and range uncertainties. The authors investigated OAR sparing and plan robustness for spot-scanning proton planning techniques and compared these with volumetric modulated arc therapy (VMAT) photon plans.

Automatic interactive optimization for volumetric modulated arc therapy planning.

Background: Intensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data.

Evaluation of a knowledge-based planning solution for head and neck cancer.

Purpose: Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries.

Different treatment planning protocols can lead to large differences in organ at risk sparing.

Background And Purpose: Different planning protocols may define varying planning target volume (PTV) dose criteria. We investigated the hypothesis that this could result in differences in organ-at-risk (OAR) sparing.

Material And Methods: Volumetric modulated arc therapy plans were created for ten locally advanced head and neck cancer patients following PTV criteria specified by the RTOG, EORTC and institutional (VUmc) protocols.

Toward optimal organ at risk sparing in complex volumetric modulated arc therapy: an exponential trade-off with target volume dose homogeneity.

Purpose: Conventional radiotherapy typically aims for homogenous dose in the planning target volume (PTV) while sparing organs at risk (OAR). The authors quantified and characterized the trade-off between PTV dose inhomogeneity (IH) and OAR sparing in complex head and neck volumetric modulated arc therapy plans.

Methods: Thirteen simultaneous integrated boost plans were created per patient, for ten patients.