Dose accumulation to assess the validity of treatment plans with reduced margins in radiotherapy of head and neck cancer

Optimal timing of re-planning for head and neck adaptive radiotherapy

BACKGROUND AND PURPOSE

Adaptive radiotherapy (ART) relies on re-planning to correct treatment variations, but the optimal timing of re-planning to account for dose changes in head and neck organs at risk (OARs) is still under investigation. We aimed to find out the optimal timing of re-planning in head and neck ART.

MATERIALS AND METHODS

A total of 110 head and neck cancer patients were retrospectively enrolled. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. The K-nearest-neighbour method was used for missing data imputation of weekly Dmean. A dose deviation map was built using the planning Dmean and weekly Dmean values and then used to simulate different ART scenarios consisting of 1 to 6 re-plannings. The difference between accumulated Dmean and planning Dmean before re-planning (ΔDmean_acc_noART) and after re-planning (ΔDmean_acc_ART) were evaluated and compared.

RESULTS

Among all the OARs, supraglottic showed the largest ΔDmean_acc_noART (1.23 ± 3.13 Gy) and most cases of ΔDmean_acc_noART > 3 Gy (26 patients). The 3rd week is suggested in the optimal timing of re-planning for 10 OARs. For all the organs except arytenoid, 2 re-plannings were able to guarantee the ΔDmean_acc_ART below 3 Gy while the average |ΔDmean_acc_ART| was below 1 Gy. ART scenarios of 2_4, 3_4, 3_5 (week of re-planning separated with "_") were able to guarantee ΔDmean_acc_ART of 99 % of patients below 3 Gy simultaneously for 19 OARs.

CONCLUSIONS

The optimal timing of re-planning was suggested for different organs at risk in head and neck adaptive radiotherapy. Generic scenarios of timing and frequency for re-planning can be applied to guarantee the increase of accumulated mean dose within 3 Gy simultaneously for multiple organs.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

A novel approach to predict acute radiation dermatitis in patients with head and neck cancer using a model based on Bayesian probability

PURPOSE

In this study, we aimed to establish a method for predicting the probability of each acute radiation dermatitis (ARD) grade during the head and neck Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning phase based on Bayesian probability.

METHODS

The skin dose volume >50 Gy (V50), calculated using the treatment planning system, was used as a factor related to skin toxicity. The empirical distribution of each ARD grade relative to V50 was obtained from the ARD grades of 119 patients (55, 50, and 14 patients with G1, G2, and G3, respectively) determined by head and neck cancer specialists. Using Bayes' theorem, the Bayesian probabilities of G1, G2, and G3 for each value of V50 were calculated with an empirical distribution. Conversely, V50 was obtained based on the Bayesian probabilities of G1, G2, and G3.

RESULTS

The empirical distribution for each graded patient group demonstrated a normal distribution. The method predicted ARD grades with 92.4 % accuracy and provided a V50 value for each grade. For example, using the graph, we could predict that V50 should be ≤24.5 cm3 to achieve G1 with 70 % probability.

CONCLUSIONS

The Bayesian probability-based ARD prediction method could predict the ARD grade at the treatment planning stage using limited patient diagnostic data that demonstrated a normal distribution. If the probability of an ARD grade is high, skin care can be initiated in advance. Furthermore, the V50 value during treatment planning can provide radiation oncologists with data for strategies to reduce ARD.

Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy

Background and Purpose

Deformable image registration (DIR) is a core element of adaptive radiotherapy workflows, integrating daily contour propagation and/or dose accumulation in their design. Propagated contours are usually manually validated and may be edited, thereby locally invalidating the registration result. This means the registration cannot be used for dose accumulation. In this study we proposed and evaluated a novel multi-modal DIR algorithm that incorporated contour information to guide the registration. This integrates operator-validated contours with the estimated deformation vector field and warped dose.

Materials and Methods

The proposed algorithm consisted of both a normalized gradient field-based data-fidelity term on the images and an optical flow data-fidelity term on the contours. The Helmholtz-Hodge decomposition was incorporated to ensure anatomically plausible deformations. The algorithm was validated for same- and cross-contrast Magnetic Resonance (MR) image registrations, Computed Tomography (CT) registrations, and CT-to-MR registrations for different anatomies, all based on challenging clinical situations. The contour-correspondence, anatomical fidelity, registration error, and dose warping error were evaluated.

Results

The proposed contour-guided algorithm considerably and significantly increased contour overlap, decreasing the mean distance to agreement by a factor of 1.3 to 13.7, compared to the best algorithm without contour-guidance. Importantly, the registration error and dose warping error decreased significantly, by a factor of 1.2 to 2.0.

Conclusions

Our contour-guided algorithm ensured that the deformation vector field and warped quantitative information were consistent with the operator-validated contours. This provides a feasible semi-automatic strategy for spatially correct warping of quantitative information even in difficult and artefacted cases.

Assessment of dosimetric impact of interfractional 6D setup error in tongue cancer treated with IMRT and VMAT using daily kV-CBCT

Background

This study aimed to evaluate the dosimetric influence of 6-dimensional (6D) interfractional setup error in tongue cancer treated with intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) using daily kilovoltage cone-beam computed tomography (kV-CBCT).

Materials and methods

This retrospective study included 20 tongue cancer patients treated with IMRT (10), VMAT (10), and daily kV-CBCT image guidance. Interfraction 6D setup errors along the lateral, longitudinal, vertical, pitch, roll, and yaw axes were evaluated for 600 CBCTs. Structures in the planning CT were deformed to the CBCT using deformable registration. For each fraction, a reference CBCT structure set with no rotation error was created. The treatment plan was recalculated on the CBCTs with the rotation error (R_Error), translation error (T_Error), and translation plus rotation error (T+R_Error). For targets and organs at risk (OARs), the dosimetric impacts of R_Error, T_Error, and T+R_Error were evaluated without and with moderate correction of setup errors.

Results

The maximum dose variation ΔD (%) for D_98% in clinical target volumes (CTV): CTV-60, CTV-54, planning target volumes (PTV): PTV-60, and PTV-54 was -1.2%, -1.9%, -12.0%, and -12.3%, respectively, in the T+R_Error without setup error correction. The maximum ΔD (%) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54 was -1.0%, -1.7%, -9.2%, and -9.5%, respectively, in the T+R_Error with moderate setup error correction. The dosimetric impact of interfractional 6D setup errors was statistically significant (p < 0.05) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54.

Conclusions

The uncorrected interfractional 6D setup errors could significantly impact the delivered dose to targets and OARs in tongue cancer. That emphasized the importance of daily 6D setup error correction in IMRT and VMAT.

Clinical pilot study for EPID-based in vivo dosimetry using EPIgray™ for head and neck VMAT

This work details the clinical pilot study methodology used at Wellington Blood and Cancer Centre (WBCC) before the clinical release of in vivo dosimetry (IVD) system EPIgray™ for head and neck (H&N) volumetric modulated arc therapy (VMAT) treatments. Clinical pilot studies make it possible to select appropriate, department-specific tolerance ranges for the treatment type and site under investigation. An IVD clinical pilot study of H&N VMAT treatments was conducted over 3 months at WBCC using EPIgray™ dose reconstruction software and included 12 patients and 32 individual treatment fractions. Statistical analysis of the dose deviations between the treatment planning system (TPS) dose and EPIgray™ reconstructed dose confirmed that a deviation tolerance range of ± 7.0% was an appropriate choice for H&N VMAT at WBCC.

An updated approach for deriving PTV margins using image guidance and deformable dose accumulation

Objective. To demonstrate an updated approach for deriving planning target volume (PTV) margins for a patient population treated with volumetric image-guided radiotherapy. Approach. The approach uses a semi-automated workflow within commercial radiotherapy applications that combines dose accumulation with the bidirectional local distance (BLD) metric. The patient cohort is divided into derivation and validation datasets. For each patient in the derivation dataset, a treatment plan is generated with a 0 mm PTV margin (the idealized treatment scenario without the influence of the standard margin). Deformable image registration enabled dose accumulation of these zero-margin plans. PTV margins are derived by using the BLD to calculate the geometric extent of underdosed regions of the clinical target volume (CTV). The PTV margin is validated by ensuring the specified CTV coverage criterion is met when the margin is applied to the validation dataset. Main results. The methodology was applied to two cohorts: 40 oropharyngeal cancer patients and 50 early-stage breast cancer patients. Ten patients from each cohort were used for validation. PTV margins derived for the oropharyngeal and early-stage breast cancer patient cohorts were 3 and 5 mm, respectively, and ensure that 95% of the prescription dose is delivered to 98% of the CTV for 90% of patients. Dose accumulation showed that the CTV coverage criterion was achieved for at least 90% of patients when the margins were applied. Significance. This methodology can be used to derive appropriate PTV margins for realistic treatment scenarios and any disease site, which will improve our understanding of patient outcomes.

Quality assurance of dysphagia-optimised intensity modulated radiotherapy treatment planning for head and neck cancer

This study aimed to assess the impact of the margin applied to the clinical target volume, to create the planning target volume, on plan quality of a novel dysphagia-optimised intensity modulated radiotherapy technique developed within a head and neck cancer multicentre randomised controlled trial. Protocol compliant plans were used for a single benchmark planning case. Larger margins were associated with higher doses to adjacent organs at risk, particularly the inferior pharyngeal constrictor muscle, but coincided with some improved low dose target coverage. A 3 mm margin is recommended for this technique if local practices allow.