3D Patient-Specific Virtual Models for Presurgical Planning in Patients with Recto-Sigmoid Endometriosis Nodules: A Pilot Study.

Background and Objective: In recent years, 3D printing has been used to support surgical planning or to guide intraoperative procedures in various surgical specialties. An improvement in surgical planning for recto-sigmoid endometriosis (RSE) excision might reduce the high complication rate related to this challenging surgery. The aim of this study was to build novel presurgical 3D models of RSE nodules from magnetic resonance imaging (MRI) and compare them with intraoperative findings. Materials and Methods: A single-center, observational, prospective, cohort, pilot study was performed by enrolling consecutive symptomatic women scheduled for minimally invasive surgery for RSE between November 2019 and June 2020 at our institution. Preoperative MRI were used for building 3D models of RSE nodules and surrounding pelvic organs. 3D models were examined during multi-disciplinary preoperative planning, focusing especially on three domains: degree of bowel stenosis, nodule's circumferential extension, and bowel angulation induced by the RSE nodule. After surgery, the surgeon was asked to subjectively evaluate the correlation of the 3D model with the intra-operative findings and to express his evaluation as "no correlation", "low correlation", or "high correlation" referring to the three described domains. Results: seven women were enrolled and 3D anatomical virtual models of RSE nodules and surrounding pelvic organs were generated. In all cases, surgeons reported a subjective "high correlation" with the surgical findings. Conclusion: Presurgical 3D models could be a feasible and useful tool to support surgical planning in women with recto-sigmoidal endometriotic involvement, appearing closely related to intraoperative findings.

Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging.

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10-5; AUC = 0.90 (95%CI, 0.73-0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Effects of transcranial direct current stimulation over the posterior parietal cortex on novice X-ray screening performance.

Existing theories of visual search are generally deduced from lab-based studies involving the identification of a target object among similar distractors. The role of the right parietal cortex in visual search is well-established. However, less is known about real-world visual search tasks, such as X-ray screening, which require targets to be disembedded from their background. Research has shown variations in the cognitive abilities required for these tasks and typical lab-based visual search tasks. Thus, the findings of traditional visual search studies do not always transfer into the applied domain. Although brain imaging studies have offered insights into visual search tasks involving disembedding, highlighting an association between the left parietal cortex and disembedding performance, no causal link has yet been established. To this end, we carried out a pilot study (n = 34, between-subjects) administering non-invasive brain stimulation over the posterior parietal cortex (PPC) prior to completing a security X-ray screening task. The findings suggested that anodal left PPC tDCS enhanced novice performance in X-ray screening over that of sham stimulation, in line with brain imaging findings. However, the efficacy of tDCS is under question, with a growing number of failed replications. With this in mind, this study aims to re-test our original hypothesis by examining the effects of left-side parietal stimulation on novice X-ray screener performance and comparing them to those of sham stimulation and of stimulation on a control site (right PPC). As such, this within-subjects study comprised three sessions (2 mA left PPC, 2 mA right PPC, low-intensity sham stimulation left PPC), to investigate effects of anodal tDCS on X-ray screening performance. The pre-registered analysis did not detect any significant differences between left PPC tDCS and sham tDCS or left PPC tDCS and right PPC tDCS on novice performance (d') in X-ray screening. Further exploratory analyses detected no effects of left PPC tDCS on any other indices of performance in the X-ray security screening task (c, RTs and accuracy), or a disembedding control task (RTs and accuracy). The use of alternative stimulation techniques, with replicable behavioural effects on the parietal lobe (or a multi-technique approach), and well-powered studies with a systematic variation of stimulation parameters, could help to choose between two possible interpretations: that neither left nor right PPC are causally related to either tasks or that tDCS was ineffective. Finally, low-intensity sham stimulation (.016 mA), previously shown to outperform other sham conditions in between-subjects designs, was found to be ineffective for blinding participants in a within-subjects design. Our findings raise concerns for the current lack of optimal control conditions and add to the growing literature highlighting the need for replication in the field.