A wait-and-see policy with strict selection criteria, up-to-date imaging techniques, and follow-up is feasible and results in promising outcome at least as good as that of patients with a pCR after surgery. The proposed selection criteria and follow-up could form the basis for future randomized studies.

BackgroundThe response to chemoradiotherapy (CRT) for rectal cancer can be assessed by clinical examination, consisting of digital rectal examination (DRE) and endoscopy, and by MRI. A high accuracy is required to select complete response (CR) for organ-preserving treatment. The aim of this study was to evaluate the value of clinical examination (endoscopy with or without biopsy and DRE), T2W-MRI, and diffusion-weighted MRI (DWI) for the detection of CR after CRT.MethodsThis prospective cohort study in a university hospital recruited 50 patients who underwent clinical assessment (DRE, endoscopy with or without biopsy), T2W-MRI, and DWI at 6–8 weeks after CRT. Confidence levels were used to score the likelihood of CR. The reference standard was histopathology or recurrence-free interval of >12 months in cases of wait-and-see approaches. Diagnostic performance was calculated by area under the receiver operator characteristics curve, with corresponding sensitivities and specificities. Strategies were assessed and compared by use of likelihood ratios.ResultsSeventeen (34 %) of 50 patients had a CR. Areas under the curve were 0.88 (0.78–1.00) for clinical assessment and 0.79 (0.66–0.92) for T2W-MRI and DWI. Combining the modalities led to a posttest probability for predicting a CR of 98 %. Conversely, when all modalities indicated residual tumor, 15 % of patients still experienced CR.ConclusionsClinical assessment after CRT is the single most accurate modality for identification of CR after CRT. Addition of MRI with DWI further improves the diagnostic performance, and the combination can be recommended as the optimal strategy for a safe and accurate selection of CR after CRT.

No abstract

ISRCTN46462267 ( http://www.controlled-trials.com).

Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study, Acta Oncologica, 56:11, 1544-1553, DOI: 10.1080 Background: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition-and reconstruction protocols influences robustness of radiomic analyses. The aim of this study was to investigate the influence of different CT-scanners, slice thicknesses, exposures and gray-level discretization on radiomic feature values and their stability. Material and methods: A texture phantom with ten different inserts was scanned on nine different CT-scanners with varying tube currents. Scans were reconstructed with 1.5 mm or 3 mm slice thickness. Image pre-processing comprised gray-level discretization in ten different bin widths ranging from 5 to 50 HU and different resampling methods (i.e., linear, cubic and nearest neighbor interpolation to 1 Â 1 Â 3 mm 3 voxels) were investigated. Subsequently, 114 textural radiomic features were extracted from a 2.1 cm 3 sphere in the center of each insert. The influence of slice thickness, exposure and bin width on feature values was investigated. Feature stability was assessed by calculating the concordance correlation coefficient (CCC) in a test-retest setting and for different combinations of scanners, tube currents and slice thicknesses. Results: Bin width influenced feature values, but this only had a marginal effect on the total number of stable features (CCC > 0.85) when comparing different scanners, slice thicknesses or exposures. Most radiomic features were affected by slice thickness, but this effect could be reduced by resampling the CT-images before feature extraction. Statistics feature 'energy' was the most dependent on slice thickness. No clear correlation between feature values and exposures was observed. Conclusions: CT-scanner, slice thickness and bin width affected radiomic feature values, whereas no effect of exposure was observed. Optimization of gray-level discretization to potentially improve prognostic value can be performed without compromising feature stability. Resampling images prior to feature extraction decreases the variability of radiomic features.