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Background Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis. Methods This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times. Results Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43–0.54) and 0.51 (95% CI, 0.46–0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66–0.77) for elastic net and 0.69 (95% CI, 0.64–0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91–0.96) and 0.75 (95% CI, 0.70–0.80) for elastic net and random forest, respectively. Conclusions This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model.

Abstract The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

Decision support tools exist for oncologic follow up. Their main interest is to help physicians improve their oncologic readings but this theoretical benefit has to be quantified by concrete evidence. The purpose of the study was to evaluate and quantify the impact of using dedicated software on RECIST readings. A comparison was made between RECIST readings without dedicated application vs. readings using dedicated software (Myrian® XL-Onco, Intrasense, France) with specific functionalities such as 3D elastic target matching and automated calculation of tumoral response. A retrospective database of 40 patients who underwent a CT scan follow up was used (thoracic/abdominal lesions). The reading panel was composed of two radiologists. Reading times, intra/inter-operator reproducibility of measurements and RECIST response misclassifications were evaluated. On average, reading time was reduced by 49.7% using dedicated software. A more important saving was observed for lung lesions evaluations (63.4% vs. 36.1% for hepatic targets). Inter and intra-operator reproducibility of measurements was excellent for both reading methods. Using dedicated software prevented misclassifications on 10 readings out of 120 (eight due to calculation errors). The use of dedicated oncology software optimises RECIST evaluation by decreasing reading times significantly and avoiding response misclassifications due to manual calculation errors or approximations.

Lymphadenopathy continues to be a common problem to radiologists and treating physicians because of the difficulty in confidently categorizing a node as being benign or malignant using standard diagnostic techniques. The goal of our research was to assess whether magnetic resonance (MR) spectroscopy contains the necessary information to allow differentiation of benign from malignant lymph nodes in an in-vitro approach using a modern pattern recognition method. Tissue samples from a tissue bank were analyzed on a nuclear magnetic resonance (NMR) spectrometer. A total of 69 samples were studied. The samples included a wide variety of malignant and benign etiologies. Using 45 samples, we initially created a model which was able to predict if a certain spectrum originates from benign or malignant lymph nodes using a pattern-recognition technique which takes into account the entire magnetic spectrum rather than single peaks alone. The remaining 24 samples were blindly loaded in the model to assess its performance. We obtained an excellent accuracy in differentiating benign and malignant lymphadenopathy using the model. It correctly differentiated as malignant or benign, in a blinded fashion, all of the malignant samples (13 of 13) and 10 out of the 11 benign samples. We thus showed that magnetic spectroscopy is able to differentiate benign from malignant causes of lymphadenopathy. Additional experiments were performed to verify that the differentiating abilities of our model were not due to differential tissue decay in between benign and malignant tissues. If future experiments demonstrate that a similar approach could be executed with standard MR imaging, this technique could be useful as a problem-solving tool when assessing lymphadenopathy in general. Alternatively, our in-vitro technique could also be useful to pathologists faced with indeterminate pathologies of the lymph nodes after validating our results with a larger sample size.

Abstract Purpose To establish a proof-of-concept study using a phantom model to allow the fusion of preoperative single-photon emission computed tomography (SPECT) combined with computed tomography (CT), also known as SPECT/CT, with intraoperative CT, enabling the application of an augmented reality (AR) surgical guidance system for pelvic sentinel lymph node (SLN) detection in endometrial cancer patients. Methods A three-dimensional (3D) pelvic phantom model printed in a gelatin-based scaffold including a radiopaque pelvis, a vascular tree mimicking the iliac vessels, two 3D-printed fillable spheres representing the target pelvic sentinel lymph nodes, and a calibration board was developed. A planar with SPECT/CT lymphoscintigraphy and CT were performed independently on the model. We performed all the necessary steps to achieve the fusion between SPECT/CT and CT. Then, we performed a laparoscopy of the pelvic anatomy on the phantom model to assess in real time the overlay of the recording on the anatomical structures and AR guidance system performance. Results We have successfully completed all the steps needed to fuse the two imaging procedures. This allowed us to apply, in real time, our surgical guidance system with the coverage rate of the visible surface by the augmented reality surface, respectively, on the left SLN 99.48% and on the right SLN 99.42%. Conclusion Co-registration and real-time fusion between a preoperative SPECT/CT and intraoperative CT are feasible. The metric performance of our guidance system is excellent in relation to possible SPECT/CT and CT fusion. Based on our results, we are able to translate the technology to patients, and we initiated a clinical study to evaluate the accuracy of the AR guidance system for endometrial cancer surgery, with a correlation with indocyanine green (ICG)-based technique, representing the gold standard today in the intraoperative detection of SLN in endometrial cancers, despite various limitations.

Objective:. To develop consensus definitions of image-guided surgery, computer-assisted surgery, hybrid operating room, and surgical navigation systems. Summary Background Data:. The use of minimally invasive procedures has increased tremendously over the past 2 decades, but terminology related to image-guided minimally invasive procedures has not been standardized, which is a barrier to clear communication. Methods:. Experts in image-guided techniques and specialized engineers were invited to engage in a systematic process to develop consensus definitions of the key terms listed above. The process was designed following review of common consensus-development methodologies and included participation in 4 online surveys and a post-surveys face-to-face panel meeting held in Strasbourg, France. Results:. The experts settled on the terms computer-assisted surgery and intervention, image-guided surgery and intervention, hybrid operating room, and guidance systems and agreed-upon definitions of these terms, with rates of consensus of more than 80% for each term. The methodology used proved to be a compelling strategy to overcome the current difficulties related to data growth rates and technological convergence in this field. Conclusions:. Our multidisciplinary collaborative approach resulted in consensus definitions that may improve communication, knowledge transfer, collaboration, and research in the rapidly changing field of image-guided minimally invasive techniques.

Abstract Objectives Liver volumetry has emerged as an important tool in clinical practice. Liver volume is assessed primarily via organ segmentation of computed tomography (CT) and magnetic resonance imaging (MRI) images. The goal of this paper is to provide an accessible overview of liver segmentation targeted at radiologists and other healthcare professionals. Methods Using images from CT and MRI, this paper reviews the indications for liver segmentation, technical approaches used in segmentation software and the developing roles of liver segmentation in clinical practice. Results Liver segmentation for volumetric assessment is indicated prior to major hepatectomy, portal vein embolisation, associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) and transplant. Segmentation software can be categorised according to amount of user input involved: manual, semi-automated and fully automated. Manual segmentation is considered the “gold standard” in clinical practice and research, but is tedious and time-consuming. Increasingly automated segmentation approaches are more robust, but may suffer from certain segmentation pitfalls. Emerging applications of segmentation include surgical planning and integration with MRI-based biomarkers. Conclusions Liver segmentation has multiple clinical applications and is expanding in scope. Clinicians can employ semi-automated or fully automated segmentation options to more efficiently integrate volumetry into clinical practice. Teaching points • Liver volume is assessed via organ segmentation on CT and MRI examinations. • Liver segmentation is used for volume assessment prior to major hepatic procedures. • Segmentation approaches may be categorised according to the amount of user input involved. • Emerging applications include surgical planning and integration with MRI-based biomarkers.