Kee H. Wong

Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy

BACKGROUND AND PURPOSE Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning. MATERIALS AND METHODS Predictive models of severe acute mucositis were generated using radiotherapy dose (dose-volume and spatial dose metrics) and clinical data. Penalised logistic regression, support vector classification and random forest classification (RFC) models were generated and compared. Internal validation was performed (with 100-iteration cross-validation), using multiple metrics, including area under the receiver operating characteristic curve (AUC) and calibration slope, to assess performance. Associations between covariates and severe mucositis were explored using the models. RESULTS The dose-volume-based models (standard) performed equally to those incorporating spatial information. Discrimination was similar between models, but the RFCstandard had the best calibration. The mean AUC and calibration slope for this model were 0.71 (s.d.=0.09) and 3.9 (s.d.=2.2), respectively. The volumes of oral cavity receiving intermediate and high doses were associated with severe mucositis. CONCLUSIONS The RFCstandard model performance is modest-to-good, but should be improved, and requires external validation. Reducing the volumes of oral cavity receiving intermediate and high doses may reduce mucositis incidence.

Repeatability and sensitivity of T2* measurements in patients with head and neck squamous cell carcinoma at 3T

To determine whether quantitation of T2* is sufficiently repeatable and sensitive to detect clinically relevant oxygenation levels in head and neck squamous cell carcinoma (HNSCC) at 3T.

Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

Highlights • Establishing MRI-only RTP workflows requires synthetic CTs for dose calculation.• This study evaluates the feasibility of using a multi-atlas CT synthesis approach.• The proposed method was validated on head and neck and prostate cancer patients.• Results showed an accurate bone estimation for future patient positioning.• Results showed that synthetic CTs are suitable to perform clinical dose calculations.

A randomised controlled trial of Caphosol mouthwash in management of radiation-induced mucositis in head and neck cancer

PURPOSE This phase III, non-blinded, parallel-group, randomised controlled study evaluated the efficacy of Caphosol mouthwash in the management of radiation-induced oral mucositis (OM) in patients with head and neck cancer (HNC) undergoing radical (chemo)radiotherapy. PATIENTS AND METHODS Eligible patients were randomised at 1:1 to Caphosol plus standard oral care (intervention) or standard oral care alone (control), stratified by radiotherapy technique and use of concomitant chemotherapy. Patients in the intervention arm used Caphosol for 7weeks: 6weeks during and 1-week post-radiotherapy. The primary endpoint was the incidence of severe OM (CTCAE ⩾grade 3) during and up to week 8 post-radiotherapy. Secondary endpoints include pharyngeal mucositis, dysphagia, pain and quality of life. RESULTS The intervention (n=108) and control (n=107) arms were well balanced in terms of patient demographics and treatment characteristics. Following exclusion of patients with missing data, 210 patients were available for analysis. The incidence of severe OM did not differ between the intervention and control arms (64.1% versus 65.4%, p=0.839). Similarly, no significant benefit was observed for other secondary endpoints. Overall, compliance with the recommended frequency of Caphosol was low. CONCLUSION Caphosol did not reduce the incidence or duration of severe OM during and after radiotherapy in HNC.

Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk

BACKGROUND AND PURPOSE Current oral mucositis normal tissue complication probability models, based on the dose distribution to the oral cavity volume, have suboptimal predictive power. Improving the delineation of the oral mucosa is likely to improve these models, but is resource intensive. We developed and evaluated fully-automated atlas-based segmentation (ABS) of a novel delineation technique for the oral mucosal surfaces. MATERIAL AND METHODS An atlas of mucosal surface contours (MSC) consisting of 46 patients was developed. It was applied to an independent test cohort of 10 patients for whom manual segmentation of MSC structures, by three different clinicians, and conventional outlining of oral cavity contours (OCC), by an additional clinician, were also performed. Geometric comparisons were made using the dice similarity coefficient (DSC), validation index (VI) and Hausdorff distance (HD). Dosimetric comparisons were carried out using dose-volume histograms. RESULTS The median difference, in the DSC and HD, between automated-manual comparisons and manual-manual comparisons were small and non-significant (-0.024; p=0.33 and -0.5; p=0.88, respectively). The median VI was 0.086. The maximum normalised volume difference between automated and manual MSC structures across all of the dose levels, averaged over the test cohort, was 8%. This difference reached approximately 28% when comparing automated MSC and OCC structures. CONCLUSIONS Fully-automated ABS of MSC is suitable for use in radiotherapy dose-response modelling.

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

Purpose Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue—sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares—logistic regression [FPLS-LR] and functional principal component—logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate—response associations, assessed using bootstrapping. Results The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.

Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk

AIMS A normal tissue complication probability (NTCP) model of severe acute mucositis would be highly useful to guide clinical decision making and inform radiotherapy planning. We aimed to improve upon our previous model by using a novel oral mucosal surface organ at risk (OAR) in place of an oral cavity OAR. MATERIALS AND METHODS Predictive models of severe acute mucositis were generated using radiotherapy dose to the oral cavity OAR or mucosal surface OAR and clinical data. Penalised logistic regression and random forest classification (RFC) models were generated for both OARs and compared. Internal validation was carried out with 100-iteration stratified shuffle split cross-validation, using multiple metrics to assess different aspects of model performance. Associations between treatment covariates and severe mucositis were explored using RFC feature importance. RESULTS Penalised logistic regression and RFC models using the oral cavity OAR performed at least as well as the models using mucosal surface OAR. Associations between dose metrics and severe mucositis were similar between the mucosal surface and oral cavity models. The volumes of oral cavity or mucosal surface receiving intermediate and high doses were most strongly associated with severe mucositis. CONCLUSIONS The simpler oral cavity OAR should be preferred over the mucosal surface OAR for NTCP modelling of severe mucositis. We recommend minimising the volume of mucosa receiving intermediate and high doses, where possible.

The emerging potential of magnetic resonance imaging in personalizing radiotherapy for head and neck cancer: an oncologist's perspective

Head and neck cancer (HNC) is a challenging tumour site for radiotherapy delivery owing to its complex anatomy and proximity to organs at risk (OARs) such as the spinal cord and optic apparatus. Despite significant advances in radiotherapy planning techniques, radiation-induced morbidities remain substantial. Further improvement would require high-quality imaging and tailored radiotherapy based on intratreatment response. For these reasons, the use of MRI in radiotherapy planning for HNC is rapidly gaining popularity. MRI provides superior soft-tissue contrast in comparison with CT, allowing better definition of the tumour and OARs. The lack of additional radiation exposure is another attractive feature for intratreatment monitoring. In addition, advanced MRI techniques such as diffusion-weighted, dynamic contrast-enhanced and intrinsic susceptibility-weighted MRI techniques are capable of characterizing tumour biology further by providing quantitative functional parameters such as tissue cellularity, vascular permeability/perfusion and hypoxia. These functional parameters are known to have radiobiological relevance, which potentially could guide treatment adaptation based on their changes prior to or during radiotherapy. In this article, we first present an overview of the applications of anatomical MRI sequences in head and neck radiotherapy, followed by the potentials and limitations of functional MRI sequences in personalizing therapy.

Noninvasive Imaging of Cycling Hypoxia in Head and Neck Cancer Using Intrinsic Susceptibility MRI

Purpose: To evaluate intrinsic susceptibility (IS) MRI for the identification of cycling hypoxia, and the assessment of its extent and spatial distribution, in head and neck squamous cell carcinoma (HNSCC) xenografts and patients. Experimental Design: Quantitation of the transverse relaxation rate, R2*, which is sensitive to paramagnetic deoxyhemoglobin, using serial IS-MRI acquisitions, was used to monitor temporal oscillations in levels of paramagnetic deoxyhemoglobin in human CALR xenografts and patients with HNSCC at 3T. Autocovariance and power spectrum analysis of variations in R2* was performed for each imaged voxel, to assess statistical significance and frequencies of cycling changes in tumor blood oxygenation. Pathologic correlates with tumor perfusion (Hoechst 33342), hypoxia (pimonidazole), and vascular density (CD31) were sought in the xenografts, and dynamic contrast-enhanced (DCE) MRI was used to assess patient tumor vascularization. The prevalence of fluctuations within patient tumors, DCE parameters, and treatment outcome were reported. Results: Spontaneous R2* fluctuations with a median periodicity of 15 minutes were detected in both xenografts and patient tumors. Spatially, these fluctuations were predominantly associated with regions of heterogeneous perfusion and hypoxia in the CALR xenografts. In patients, R2* fluctuations spatially correlated with regions of lymph nodes with low Ktrans values, typically in the vicinity of necrotic cores. Conclusions: IS-MRI can be used to monitor variations in levels of paramagnetic deoxyhemoglobin, associated with cycling hypoxia. The presence of such fluctuations may be linked with impaired tumor vasculature, the presence of which may impact treatment outcome. Clin Cancer Res; 23(15); 4233–41. ©2017 AACR.

Changes in multimodality functional imaging parameters early during chemoradiation predict treatment response in patients with locally advanced head and neck cancer

ObjectiveTo assess the optimal timing and predictive value of early intra-treatment changes in multimodality functional and molecular imaging (FMI) parameters as biomarkers for clinical remission in patients receiving chemoradiation for head and neck squamous cell carcinoma (HNSCC).MethodsThirty-five patients with stage III-IVb (AJCC 7th edition) HNSCC prospectively underwent 18F–FDG-PET/CT, and diffusion-weighted (DW), dynamic contrast-enhanced (DCE) and susceptibility-weighted MRI at baseline, week 1 and week 2 of chemoradiation. Patients with evidence of persistent or recurrent disease during follow-up were classed as non-responders. Changes in FMI parameters at week 1 and week 2 were compared between responders and non-responders with the Mann–Whitney U test. The significance threshold was set at a p value of <0.05.ResultsThere were 27 responders and 8 non-responders. Responders showed a greater reduction in PET-derived tumor total lesion glycolysis (TLG40%; p = 0.007) and maximum standardized uptake value (SUVmax; p = 0.034) after week 1 than non-responders but these differences were absent by week 2. In contrast, it was not until week 2 that MRI-derived parameters were able to discriminate between the two groups: larger fractional increases in primary tumor apparent diffusion coefficient (ADC; p < 0.001), volume transfer constant (Ktrans; p = 0.012) and interstitial space volume fraction (Ve; p = 0.047) were observed in responders versus non-responders. ADC was the most powerful predictor (∆ >17%, AUC 0.937).ConclusionEarly intra-treatment changes in FDG-PET, DW and DCE MRI-derived parameters are predictive of ultimate response to chemoradiation in HNSCC. However, the optimal timing for assessment with FDG-PET parameters (week 1) differed from MRI parameters (week 2). This highlighted the importance of scanning time points for the design of FMI risk-stratified interventional studies.