Analysis of GTV reduction during radiotherapy for oropharyngeal cancer: Implications for adaptive radiotherapy

Background and purpose: Adaptive field size reduction based on gross tumor volume (GTV) shrinkage imposes risk on coverage. Fiducial markers were used as surrogate for behavior of tissue surrounding the GTV edge to assess this risk by evaluating if GTVs during treatment are dissolving or actually shrinking.Materials and methods: Eight patients with oropharyngeal tumors treated with chemo-radiation were included. Before treatment, fiducial markers (0.035 x 0.2 cm(2), n = 40) were implanted at the edge of the primary tumor. All patients underwent planning-CT, daily cone beam CT (CBCT) and MRIs (pretreatment, weeks 3 and 6). Marker displacement on CBCT was compared to local GTV surface displacement on MRIs. Additionally, marker displacement relative to the GTV surfaces during treatment was measured.Results: GTV surface displacement derived from MRI was larger than derived from fiducial markers (average difference: 0.1 cm in week 3). During treatment, the distance between markers and GTV surface on MRI in week 3 increased in 33% > 0.3 cm and in 10%> 0.5 cm. The MRI-GTV shrank faster than the surrounding tissue represented by the markers, i.e. adapting to GTV shrinkage may cause under-dosage of microscopic disease.Conclusions: We showed that adapting to primary tumor GTV shrinkage on MRI mid-treatment is potentially not safe since at least part of the GTV is likely to be dissolving. Adjustment to clear anatomical boundaries, however, may be done safely. (C) 2016 Elsevier Ireland Ltd.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer

Objective To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data. Methods Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2W(volume)/T2W(entropy)/ADC(mean)/SUVmean/TLG/CTmean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset. Results When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture). Conclusion In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility

Objectives To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. Methods T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. Results Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41). Conclusions Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models