The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R−) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R− lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.

Introduction . With the availability of low-cost head-mounted displays (HMDs), virtual reality environments (VREs) are increasingly being used in medicine for teaching and clinical purposes. Our aim was to develop an interactive, user-friendly VRE for tridimensional visualization of patient-specific organs, establishing a workflow to transfer 3-dimensional (3D) models from imaging datasets to our immersive VRE. Materials and Methods . This original VRE model was built using open-source software and a mobile HMD, Samsung Gear VR. For its validation, we enrolled 33 volunteers: morphologists (n = 11), trainee surgeons (n = 15), and expert surgeons (n = 7). They tried our VRE and then filled in an original 5-point Likert-type scale 6-item questionnaire, considering the following parameters: ease of use, anatomy comprehension compared with 2D radiological imaging, explanation of anatomical variations, explanation of surgical procedures, preoperative planning, and experience of gastrointestinal/neurological disorders. Results in the 3 groups were statistically compared using analysis of variance. Results . Using cross-sectional medical imaging, the developed VRE allowed to visualize a 3D patient-specific abdominal scene in 1 hour. Overall, the 6 items were evaluated positively by all groups; only anatomy comprehension was statistically significant different among the 3 groups. Conclusions . Our approach, based on open-source software and mobile hardware, proved to be a valid and well-appreciated system to visualize 3D patient-specific models, paving the way for a potential new tool for teaching and preoperative planning.

The aim of this multicentric study is an inter-center benchmarking, to assess how different set tools applied to the same radiomics workflow affected the radiomics features (RFs) values. This topic is of key importance to start collaboration between different centers and to bring radiomic studies from benchmark to bedside. A per-lesion analysis was performed on 56 metastases (mts) selected from 14 patients. A single radiologist performed the segmentation of all mts, and RFs were extracted from the same segmentation of each mts, using two different software and file formats. Potential sources of discrepancies were evaluated. The intraclass correlation coefficient was used to describe how strongly the same radiomic measurements calculated in the two different centers resemble each other. Moreover, means of the relative changes of each RF were calculated, compared and gradually reduced. We showed that, after matching all formulas, discrepancies in RFs calculation between two centers ranged from 1% to 277%. Therefore, we evaluated other sources of variability using a stepwise approach, which led us to reduce the inter-center discrepancies to 0% for 22/25 RFs and below 2% for 3 RFs out of 25. In this study we demonstrated that different radiomic applications and masks formats might strongly impact the computation of some RFs. Therefore, when dealing with multi-center studies it is mandatory to adopt all strategies that can help in limiting the differences, thus keeping in mind the feasibility of these strategies in large cohort studies.