Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.

The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping

The potential impact of CT-MRI matching on tumor volume delineation in advanced head and neck cancer

To study the potential impact of the combined use of CT and MRI scans on the Gross Tumor Volume (GTV) estimation and interobserver variation. Four observers outlined the GTV in six patients with advanced head and neck cancer on CT, axial MRI, and coronal or sagittal MRI. The MRI scans were subsequently matched to the CT scan. The interobserver and interscan set variation were assessed in three dimensions. The mean CT derived volume was a factor of 1.3 larger than the mean axial MRI volume. The range in volumes was larger for the CT than for the axial MRI volumes in five of the six cases. The ratio of the scan set common (i.e., the volume common to all GTVs) and the scan set encompassing volume (i.e., the smallest volume encompassing all GTVs) was closer to one in MRI (0.3-0.6) than in CT (0.1-0.5). The rest volumes (i.e., the volume defined by one observer as GTV in one data set but not in the other data set) were never zero for CT vs. MRI nor for MRI vs. CT. In two cases the craniocaudal border was poorly recognized on the axial MRI but could be delineated with a good agreement between the observers in the coronal/sagittal MRI. MRI-derived GTVs are smaller and have less interobserver variation than CT-derived GTVs. CT and MRI are complementary in delineating the GTV. A coronal or sagittal MRI adds to a better GTV definition in the craniocaudal directio

99mTc Hynic-rh-Annexin V scintigraphy for in vivo imaging of apoptosis in patients with head and neck cancer treated with chemoradiotherapy

PURPOSE: The purpose of this study was to determine the value of (99m)Tc Hynic-rh-Annexin-V-Scintigraphy (TAVS), a non-invasive in vivo technique to demonstrate apoptosis in patients with head and neck squamous cell carcinoma. METHODS: TAVS were performed before and within 48 h after the first course of cisplatin-based chemoradiation. Radiation dose given to the tumour at the time of post-treatment TAVS was 6-8 Gy. Single-photon emission tomography data were co-registered to planning CT scan. Complete sets of these data were available for 13 patients. The radiation dose at post-treatment TAVS was calculated for several regions of interest (ROI): primary tumour, involved lymph nodes and salivary glands. Annexin uptake was determined in each ROI, and the difference between post-treatment and baseline TAVS represented the absolute Annexin uptake: Delta uptake (DeltaU). RESULTS: In 24 of 26 parotid glands, treatment-induced Annexin uptake was observed. Mean DeltaU was significantly correlated with the mean radiation dose given to the parotid glands (r = 0.59, p = 0.002): Glands that received higher doses showed more Annexin uptake. DeltaU in primary tumour and pathological lymph nodes showed large inter-patient differences. A high correlation was observed on an inter-patient level (r = 0.71, p = 0.006) between the maximum DeltaU in primary tumour and in the lymph nodes. CONCLUSIONS: Within the dose range of 0-8 Gy, Annexin-V-scintigraphy showed a radiation-dose-dependent uptake in parotid glands, indicative of early apoptosis during treatment. The inter-individual spread in Annexin uptake in primary tumours could not be related to differences in dose or tumour volume, but the Annexin uptake in tumour and lymph nodes were closely correlated. This effect might represent a tumour-specific apoptotic respons

An automated workflow for patient-specific quality control of contour propagation

Contour propagation is an essential component of adaptive radiotherapy, but current contour propagation algorithms are not yet sufficiently accurate to be used without manual supervision. Manual review of propagated contours is time-consuming, making routine implementation of real-time adaptive radiotherapy unrealistic. Automated methods of monitoring the performance of contour propagation algorithms are therefore required. We have developed an automated workflow for patient-specific quality control of contour propagation and validated it on a cohort of head and neck patients, on which parotids were outlined by two observers. Two types of error were simulated-mislabelling of contours and introducing noise in the scans before propagation. The ability of the workflow to correctly predict the occurrence of errors was tested, taking both sets of observer contours as ground truth, using receiver operator characteristic analysis. The area under the curve was 0.90 and 0.85 for the observers, indicating good ability to predict the occurrence of errors. This tool could potentially be used to identify propagated contours that are likely to be incorrect, acting as a flag for manual review of these contours. This would make contour propagation more efficient, facilitating the routine implementation of adaptive radiotherap

Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications

The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications. A model for simultaneous correspondence and interpolation estimation in 3D shapes was trained with head and neck organ segmentations from planning CT scans. We then extended the original model to incorporate imaging information using two approaches: 1) extracting features directly from image patches, and 2) including the mean square error between patches as part of the loss function. The correspondence and interpolation performance were evaluated using the geodesic error, chamfer distance and conformal distortion metrics, as well as distances between anatomical landmarks. Each of the models produced significantly better correspondences than the baseline non-rigid registration approach. The original model performed similarly to the model with direct inclusion of image features. The best performing model configuration incorporated imaging information as part of the loss function which produced more anatomically plausible correspondences. We will use the best performing model to identify corresponding anatomical points on organs to improve spatial normalisation, an important step in outcome modelling, or as an initialisation for anatomically informed registrations. All our code is publicly available at https://github.com/rrr-uom-projects/Unsup-RT-Corr-NetComment: Accepted in 3rd Workshop on Shape in Medical Imaging (ShapeMI 2023). This preprint has not undergone peer review or any post-submission improvements or correction

Effects of anatomical changes on pencil beam scanning proton plans in locally advanced NSCLC patients

Daily anatomical variations can cause considerable differences between delivered and planned dose. This study simulates and evaluates these effects in spot-scanning proton therapy for lung cancer patients. Robust intensity modulated treatment plans were designed on the mid-position CT scan for sixteen locally advanced lung cancer patients. To estimate dosimetric uncertainty, deformable registration was performed on their daily CBCTs to generate 4DCT equivalent scans for each fraction and to map recomputed dose to a common frame. Without adaptive planning, eight patients had an undercoverage of the targets of more than 2GyE (maximum of 14.1GyE) on the recalculated treatment dose from the daily anatomy variations including respiration. In organs at risk, a maximum increase of 4.7GyE in the D1 was found in the mediastinal structures. The effect of respiratory motion alone is smaller: 1.4GyE undercoverage for targets and less than 1GyE for organs at risk. Daily anatomical variations over the course of treatment can cause considerable dose differences in the robust planned dose distribution. An advanced planning strategy including knowledge of anatomical uncertainties would be recommended to improve plan robustness against interfractional variations. For large anatomical changes, adaptive therapy is mandator

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial