The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, thus enabling calibration and verification of radiomics software. Key results: • research teams found agreement for calculation of 169 radiomics features derived from a digital phantom and a human lung cancer on CT scan. • Of these 169 candidate radiomics features, good to excellent reproducibility was achieved for 167 radiomics features using MRI, 18F-FDG PET and CT images obtained in 51 patients with soft-tissue sarcoma.

Since all teams identify most dominant lesions, dose escalation to the dominant lesion is feasible. Sufficient dose to the whole prostate may need to be maintained to prevent under treatment of smaller lesions and undetected parts of larger lesions.

a b s t r a c tBackground and purpose: The introduction of a magnetic resonance (MR)-only workflow in radiotherapy requires that fiducial markers, used for position verification, can be detected on MR images. Here we evaluate a model for marker detection in prostate cancer patients by combining information from our hospital standard multi-parametric (mp-) MRI protocol (T1-weighted -T1w, T2-weighted -T2w, B 0 ) with dedicated sequences (balanced steady-state free precession sequence -bTFE, susceptibility weighted imaging -SWI). Materials and methods: Thirty two patients scheduled for external-beam radiotherapy received a mp-MRI and computed-tomography; the latter was used as ground truth location of the markers. A logistic regression model was implemented for marker detection by combining features from all imaging sequences. The performance of the individual and combined sequences was assessed by determining true and false positive detections. Results: The combination of different sequences (mp-MRI) resulted in a better performance than the best imaging sequence alone (bTFE). Combining mp-MRI + bTFE resulted in good accuracy and a true positive detection rate of 0.94. Conclusions: The standard mp-MRI provides valuable information to detect fiducial markers. The combination of different sequences outperforms the use of a single dedicated sequence. We recommend the addition of a bTFE to the standard mp-MRI protocol to improve fiducial marker detection.

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Background and purpose: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15-35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. Materials and methods: In a cohort of 120 high-risk patients, imaging features were extracted from the wholeprostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient's clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). Results: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. Conclusions: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort.