Geometric variability of organs at risk in head and neck radiotherapy

Many head and neck cancer patients live with side effects due to the radiation therapy. Xerostomia (having a dry mouth) is a serious and common side effect, which can persist long after the treatment. Predictive models can be used to calculate the probability of the occurrence of a certain complication, the ' normal tissue complication probability (NTCP) '. NTCP models for xerostomia often use the mean dose to the major salivary gland (parotid gland). The NTCP can be used to select patients for an advanced treatment. However, there is variation in the delineation of organs at risk (such as the parotid gland). Also, the shape and size of the parotid gland change during the course of radiotherapy. These variations in geometry lead to variations in the mean dose, and therefore to inconsistent values of NTCP. In this dissertation, the size and the effect of variations in delineation and changing anatomy are studied, and solutions to reduce these variations or limit its consequences are proposed. The most important results are internationally approved delineation guidelines, which have been implemented world-wide. This will reduce interobserver variability and allow the generic application of NTCP models. In addition, a method is proposed to select patients with a large dose variation in the parotid gland for adaptive radiotherapy. In this way, the risk of xerostomia will be reduced and quality of life improved

How do combinations of unhealthy behaviors relate to attitudinal factors and subjective health among the adult population in the Netherlands?

BACKGROUND: Health behaviours like smoking, nutrition, alcohol consumption and physical activity (SNAP) are often studied separately, while combinations can be particularly harmful. This study aims to contribute to a better understanding of lifestyle choices by studying the prevalence of (combinations of) unhealthy SNAP behaviours in relation to attitudinal factors (time orientation, risk attitude) and subjective health (self-rated health, life expectancy) among the adult Dutch population. METHODS: In total 1006 respondents, representative of the Dutch adult population (18-75 years) in terms of sex, age, and education, were drawn from a panel in 2016. They completed an online questionnaire. Groups comparisons and logistic regression analyses (crude and adjusted) were applied to analyse (combinations of) SNAP behaviours in relation to time orientation (using the Consideration of Future Consequences scale comprising Immediate (CFC-I) and Future (CFC-F) scales) and risk attitude (Health-Risk Attitude Scale; HRAS-6), as well as subjective health (visual analogue scale and subjective life expectancy). RESULTS: In the analyses, 989 respondents (51% men, average 52 years, 22% low, 48% middle, and 30% high educated) were included. About 8% of respondents engaged in four unhealthy SNAP behaviours and 18% in none. Self-rated health varied from 5.5 to 7.6 in these groups, whilst subjective life expectancy ranged between 73.7 and 85.5 years. Logistic regression analyses, adjusted for socio-demographic variables, showed that smoking, excessive drinking and combining two or more unhealthy SNAP behaviours were significantly associated with CFC-I scores, whi

Identifying patients who may benefit from adaptive radiotherapy:Does the literature on anatomic and dosimetric changes in head and neck organs at risk during radiotherapy provide information to help?

AbstractIn the last decade, many efforts have been made to characterize anatomic changes of head and neck organs at risk (OARs) and the dosimetric consequences during radiotherapy. This review was undertaken to provide an overview of the magnitude and frequency of these effects, and to investigate whether we could find criteria to identify head and neck cancer patients who may benefit from adaptive radiotherapy (ART). Possible relationships between anatomic and dosimetric changes and outcome were explicitly considered. A literature search according to PRISMA guidelines was performed in MEDLINE and EMBASE for studies concerning anatomic or dosimetric changes of head and neck OARs during radiotherapy. Fifty-one eligible studies were found. The majority of papers reported on parotid gland (PG) anatomic and dosimetric changes. In some patients, PG mean dose differences between planning CT and repeat CT scans up to 10Gy were reported. In other studies, only minor dosimetric effects (i.e. <1Gy difference in PG mean dose) were observed as a result of significant anatomic changes. Only a few studies reported on the clinical relevance of anatomic and dosimetric changes in terms of complications or quality of life. Numerous potential selection criteria for anatomic and dosimetric changes during radiotherapy were found and listed. The heterogeneity between studies prevented unambiguous conclusions on how to identify patients who may benefit from ART in head and neck cancer. Potential pre-treatment selection criteria identified from this review include tumour location (nasopharyngeal carcinoma), age, body mass index, planned dose to the parotid glands, the initial parotid gland volume, and the overlap volume of the parotid glands with the target volume. These criteria should be further explored in well-designed and well-powered prospective studies, in which possible relationships between anatomic and dosimetric changes and outcome need to be established

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry

Objectives: To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry. Methods: The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth B-spline deformation. Subsequently, the deformed images were repeatedly registered with the original images using different BKSs. The quality of the DIR was expressed as the mean residual displacement (MRD) between the known imposed deformation and the result of the B-spline DIR. Finally, for three patients, head-and-neck planning CT scans were deformed with a realistic deformation field derived from a rescan CT of the same patient, resulting in a simulated deformed image and an a-priori known deformation field. Hence, a B-spline DIR was performed between the simulated image and the planning CT at different BKSs. Similar to the phantom cases, the DIR accuracy was evaluated by means of MRD. Results: In total, 162 phantom registrations were performed with varying CNR and BKSs. MRD-values = +/- 250 HU and noise <+/- 200 HU. Decreasing the image feature content resulted in increased MRD-values at all BKSs. Using BKS = 15 mm for the three clinical cases resulted in an average MRD <1.0 mm. Conclusions: For synthetically generated phantoms and three real CT cases the highest DIR accuracy was obtained for a BKS between 10-20 mm. The accuracy decreased with decreasing image feature content, decreasing image contrast, and higher noise levels. Our results indicate that DIR accuracy in clinical CT images (typical noise levels <+/- 100 HU) will not be effected by the amount of image noise

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019–April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines

An investigation into the risk of population bias in deep learning autocontouring

Background and Purpose: To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by determining whether the performance of an autocontouring system is impacted by geographic population.Materials and methods: 80 Head Neck CT deidentified scans were collected from four clinics in Europe (n = 2) and Asia (n = 2). A single observer manually delineated 16 organs-at-risk in each. Subsequently, the data was contoured using a DLC solution, and trained using single institution (European) data. Autocontours were compared to manual delineations using quantitative measures. A Kruskal-Wallis test was used to test for any difference between populations. Clinical acceptability of automatic and manual contours to observers from each participating institution was assessed using a blinded subjective evaluation.Results: Seven organs showed a significant difference in volume between groups. Four organs showed statistical differences in quantitative similarity measures. The qualitative test showed greater variation in acceptance of contouring between observers than between data from different origins, with greater acceptance by the South Korean observers.Conclusion: Much of the statistical difference in quantitative performance could be explained by the difference in organ volume impacting the contour similarity measures and the small sample size. However, the qualitative assessment suggests that observer perception bias has a greater impact on the apparent clinical acceptability than quantitatively observed differences. This investigation of potential geographic bias should extend to more patients, populations, and anatomical regions in the future.</p

A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy

Background and purpose: Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP). Materials and methods: Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose (D-mean) and con-sequent NTCP-predictions. The average Dmean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of D-mean and NTCP-predictions (vertical bar AD(mean)vertical bar and vertical bar ANTCP vertical bar). Results: The average vertical bar AD(mean)vertical bar of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest vertical bar AD(mean)vertical bar in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest vertical bar AD(mean)vertical bar in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile vertical bar ANTCP vertical bar was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively. Conclusions: Human segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases. (C) 2021 The Author(s). Published by Elsevier B.V