Assessing and improving quality of life in patients with head and neck cancer

Health-related quality of life (QoL) indicates the patients' perception of their health. It depends not only on disease- and treatment-related factors but also on complex inter-relationships of expectations, values and norms, psychologic distress, and comparison with other patients. This article introduces methods and challenges of QoL assessment in patients with head and neck cancer, as well as ways to overcome measurement problems and ways to improve their QoL. </p

Modelling for Radiation Treatment Outcome

Modelling of tumour control probability (TCP) and normal tissue complication probability (NTCP) has been continuously used to estimate the therapeutic window of radiotherapy. In recent years, available data on tumour and normal tissue biology and from multimodal imaging have increased substantially, in particular, due to image-guided radiotherapy (see previous chapters of this book) and novel high-throughput sequencing technologies. Accordingly, more complex modelling algorithms are applied and issues of data quality, structured modelling procedures, and model validation need to be addressed. This chapter outlines general modelling principles in the era of big data, provides definitions of classical TCP and NTCP models, and presents two applications of outcome modelling in radiotherapy: the model-based approach for assigning patients to photon or proton-beam therapy and radiomics analyses based on clinical imaging data.</p

Modelling for Radiation Treatment Outcome

Modelling of tumour control probability (TCP) and normal tissue complication probability (NTCP) has been continuously used to estimate the therapeutic window of radiotherapy. In recent years, available data on tumour and normal tissue biology and from multimodal imaging have increased substantially, in particular, due to image-guided radiotherapy (see previous chapters of this book) and novel high-throughput sequencing technologies. Accordingly, more complex modelling algorithms are applied and issues of data quality, structured modelling procedures, and model validation need to be addressed. This chapter outlines general modelling principles in the era of big data, provides definitions of classical TCP and NTCP models, and presents two applications of outcome modelling in radiotherapy: the model-based approach for assigning patients to photon or proton-beam therapy and radiomics analyses based on clinical imaging data.</p

Pre- and post-radiotherapy MRI results as a predictive model for response in laryngeal carcinoma

The purpose was to determine if pre-radiotherapy (RT) and/or post-radiotherapy magnetic resonance (MR) imaging can predict response in patients with laryngeal carcinoma treated with RT. Pre- and post-RT MR examinations of 80 patients were retrospectively reviewed and associated with regard to local control. Pre-RT MR imaging parameters such as tumor involvement of specific laryngeal anatomic subsites including laryngeal cartilages and post-RT changes, i.e., complete resolution of the tumor or focal mass/asymmetric obliteration of laryngeal tissue and signal pattern on T2-weighted images, were evaluated. Local control was defined as absence of a recurrence at the primary site for 2 years. Local control rates based on pretreatment MR findings were 73% for low pre- RT risk-profile and 29% for high pre- RT risk-profile patients (p=0.0001). Based on posttreatment MR findings, local control rates were 100% score 1, 64% score 2, and 4% score 3 (p< 0.0001). Using post-RT T2-weighted images, significant association was found between differences in signal pattern and local control: 77% hypointense, 54% isointense and 15% hyperintense lesions (p<0.001). Differences between means of delay of post-MRI examination were significantly associated with regard to local control (p=0.003); recurrent tumors followed 5 months after RT were more easily detectable on MRI than recurrent tumors within 4 months after RT. Sensitivity, specificity, accuracy, negative and positive predictive values of post-RT score 3 were 96%, 76%, 83%, 98% and 66%. Pre- and post-RT MRI evaluation of the larynx can identify patients at high risk for developing local failure

Assessment of residual geometrical errors of clinical target volumes and their impact on dose accumulation for head and neck radiotherapy

PURPOSE: To assess the residual geometrical errors (dr) and their impact on the clinical target volumes (CTV) dose coverage for head and neck cancer (HNC) proton therapy patients.METHODS: We analysed 28 HNC patients treated with 70 Gy (RBE) and 54.25 Gy (RBE) to the therapeutic CTV70 and prophylactic CTV54.25, respectively. Daily cone beam CTs were converted to high quality synthetic CTs (sCTs). The CTVs from the nominal CT were propagated to the corresponding sCTs using a hybrid deformable image registration (propagated CTVs) in RayStation 11B. For 11 patients, all propagated CTVs were reviewed by our HNC radiation oncologist (physician corrected CTVs). The residual geometrical error dr was quantified as a function of the daily CTVs volume overlap with the nominal plan CTV. The errors dr(propagated CTVs) and dr(physician corrected CTVs) and the difference in dice similarity coefficients (ΔDSC) were determined. Using clinical plans, dose coverage and the tumor control probability (TCP) for the nominal, accumulated and voxel-wise minimum scenarios were determined.RESULTS: The difference in the residual geometrical error dr (propagated CTVs - physician corrected CTVs) and mean DSC (|ΔDSC|mean) were minor: Δdr(CTV70) = 0.16 mm, Δdr(CTV54.25) = 0.26 mm, |ΔDSC|mean &lt; 0.9%. For all 28 patients, dr(CTV70) = 1.91 mm and dr(CTV54.25) = 1.90 mm. However, CTV54.25 above and below the cricoid cartilage differed substantially (1.00 mm c.f. 3.93 mm). The CTV54.25 coverage below the cricoid was then almost always lower, although the TCP of the accumulated dose was higher than the TCP of the voxel-wise minimum dose.CONCLUSIONS: Setup uncertainty setting of 2 mm is possible. The feasibility of using propagated CTVs for error determination is demonstrated.</p

Can the mean linear energy transfer of organs be directly related to patient toxicities for current head and neck cancer intensity-modulated proton therapy practice?

BACKGROUND AND PURPOSE: The relative biological effectiveness (RBE) of proton therapy is predicted to vary with the dose-weighted average linear energy transfer (LETd). However, RBE values may substantially vary for different clinical endpoints. Therefore, the aim of this study was to assess the feasibility of relating mean D⋅LETd parameters to patient toxicity for HNC patients treated with proton therapy. MATERIALS AND METHODS: The delivered physical dose (D) and the voxel-wise product of D and LETd (D⋅LETd) distributions were calculated for 100 head and neck cancer (HNC) proton therapy patients using our TPS (Raystation v6R). The means and covariance matrix of the accumulated D and D⋅LETd of all relevant organs-at-risk (OARs) were used to simulate 2.500 data sets of different sizes. For each dataset, an attempt was made to add mean D⋅LETd parameters to a multivariable NTCP model based on mean D parameters of the same OAR for xerostomia, tube feeding and dysphagia. The likelihood of creating an NTCP model with statistically significant parameters (i.e. power) was calculated as a function of the simulated sample size for various RBE models. RESULTS: The sample size required to have a power of at least 80% to show an independent effect of mean D⋅LETd parameters on toxicity is over 15000 patients for all toxicities. CONCLUSION: For current clinical practice, it is not feasible to directly model NTCP with both mean D and mean D⋅LETd of OARs. These findings should not be interpreted as a contradiction of previous evidence for the relationship between RBE and LETd

Assessment of residual geometrical errors of clinical target volumes and their impact on dose accumulation for head and neck radiotherapy

Purpose: To assess the residual geometrical errors (dr) and their impact on the clinical target volumes (CTV) dose coverage for head and neck cancer (HNC) proton therapy patients.Methods: We analysed 28 HNC patients treated with 70 Gy (RBE) and 54.25 Gy (RBE) to the therapeutic CTV70 and prophylactic CTV54.25, respectively. Daily cone beam CTs were converted to high quality synthetic CTs (sCTs). The CTVs from the nominal CT were propagated to the corresponding sCTs using a hybrid deformable image registration (propagated CTVs) in RayStation 11B. For 11 patients, all propagated CTVs were reviewed by our HNC radiation oncologist (physician corrected CTVs).The residual geometrical error dr was quantified as a function of the daily CTVs volume overlap with the nominal plan CTV. The errors dr(propagated CTVs) and dr(physician corrected CTVs) and the difference in dice similarity coefficients (ΔDSC) were determined. Using clinical plans, dose coverage and the tumor control probability (TCP) for the nominal, accumulated and voxel-wise minimum scenarios were determined.Results: The difference in the residual geometrical error dr (propagated CTVs – physician corrected CTVs) and mean DSC (|ΔDSC|mean) were minor: Δdr(CTV70) = 0.16 mm, Δdr(CTV54.25) = 0.26 mm, |ΔDSC|mean &lt; 0.9%. For all 28 patients, dr(CTV70) = 1.91 mm and dr(CTV54.25) = 1.90 mm. However, CTV54.25 above and below the cricoid cartilage differed substantially (1.00 mm c.f. 3.93 mm). The CTV54.25 coverage below the cricoid was then almost always lower, although the TCP of the accumulated dose was higher than the TCP of the voxel-wise minimum dose.Conclusions: Setup uncertainty setting of 2 mm is possible. The feasibility of using propagated CTVs for error determination is demonstrated.</p