Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine learning allow for several types of pathologies encountered on radiological images to be automatically and reliably distinguished. The aim of the study was to assess the contribution of radiomics and machine learning in the differentiation between soft-tissue lipoma and liposarcoma on preoperative MRI and to assess the diagnostic accuracy of a machine-learning model compared to musculoskeletal radiologists. 86 radiomics features were retrospectively extracted from volume-of-interest on T1-weighted spin-echo 1.5 and 3.0 Tesla MRI of 38 soft-tissue tumors (24 lipomas and 14 liposarcomas, based on histopathological diagnosis). These radiomics features were then used to train a machine-learning classifier to distinguish lipoma and liposarcoma. The generalization performance of the machine-learning model was assessed using Monte-Carlo cross-validation and receiver operating characteristic curve analysis (ROC-AUC). Finally, the performance of the machine-learning model was compared to the accuracy of three specialized musculoskeletal radiologists using the McNemar test. Machine-learning classifier accurately distinguished lipoma and liposarcoma, with a ROC-AUC of 0.926. Notably, it performed better than the three specialized musculoskeletal radiologists reviewing the same patients, who achieved ROC-AUC of 0.685, 0.805, and 0.785. Despite being developed on few cases, the trained machine-learning classifier accurately distinguishes lipoma and liposarcoma on preoperative MRI, with better performance than specialized musculoskeletal radiologists.

BACKGROUND AND PURPOSE: Early outcome prediction of postanoxic patients in a coma after cardiac arrest proves challenging. Current prognostication relies on multimodal testing, using clinical examination, electrophysiologic testing, biomarkers, and structural MR imaging. While this multimodal prognostication is accurate for predicting poor outcome (ie, death), it is not sensitive enough to identify good outcome (ie, consciousness recovery), thus leaving many patients with indeterminate prognosis. We specifically assessed whether resting-state fMRI provides prognostic information, notably in postanoxic patients in a coma with indeterminate prognosis early after cardiac arrest, specifically for good outcome. MATERIALS AND METHODS: We used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. Then, we automatically predicted coma outcome using resting-state fMRI and also compared the prediction based on resting-state fMRI with the outcome prediction based on DWI. RESULTS: Of 17 eligible patients who completed the study procedure (among 351 patients screened), 9 regained consciousness and 8 remained comatose. We found higher functional connectivity in patients recovering consciousness, with greater changes occurring within and between the occipitoparietal and temporofrontal regions. Coma outcome prognostication based on resting-state fMRI machine learning was very accurate, notably for identifying patients with good outcome (accuracy, 94.4%; area under the receiver operating curve, 0.94). Outcome predictors using resting-state fMRI performed significantly better (P < .05) than DWI (accuracy, 60.0%; area under the receiver operating curve, 0.63). CONCLUSIONS: Indeterminate prognosis might lead to major clinical uncertainty and significant variations in life-sustaining treatments. Resting-state fMRI might bridge the gap left in early prognostication of postanoxic patients in a coma by identifying those with both good and poor outcomes.

BACKGROUND: Mechanical thrombectomy using a double stent-retriever technique has recently been described for the treatment of acute ischemic stroke, but its efficacy and safety are not well established. PURPOSE: The aim of this systematic review and meta-analysis was to evaluate reports of the use of double stent-retriever during the endovascular treatment of patients with ischemic stroke. DATA SOURCES: The PubMed, Embase, Web of Science and Scopus databases were searched to identify all studies (clinical trials, cohorts series and case reports) investigating the utility of double stent-retriever for the treatment of stroke. The study is reported in accordance with PRISMA 2020 guidelines and was prospectively registered in PROSPERO (BLINDED FOR PEER REVIEW). STUDY SELECTION: 17 studies involving a total of 128 patients with large vessel occlusions predominantly in the anterior circulation (93.0%) were identified. DATA ANALYSIS: Outcomes of interest were the prevalence of successful recanalization (mTICI ≥2b) and a first-pass effect following double stent-retriever, as well as complications such as iatrogenic dissections and subarachnoid hemorrhage. Data were pooled using a random-effects model. DATA SYNTHESIS: Double stent-retriever was used as a rescue strategy in occlusions refractory to conventional endovascular treatment in 68.7% (88/128) of patients and as a first-line strategy in 31.3% (40/128) patients. Double stent-retriever achieved an overall final mTICI ≥2b in 92.6% cases with a first-pass effect of 76.6%. The complication rate remained low, with 0.37% dissection and 1.56% subarachnoid hemorrhage. LIMITATIONS: Limitations of the study include (1) a large number of case reports or small series, (2) a meta-analysis of proportions with no statistical comparison to a control group, and (3) the lack of access to patient-level data. CONCLUSIONS: Our findings suggest that double stent-retriever thrombectomy may be safe and associated with good recanalization outcomes, but prospective comparative studies are needed to determine which patients may benefit from this endovascular procedure. ABBREVIATIONS: AICH = asymptomatic intracranial hemorrhage; AIS = acute ischemic stroke; DSR = double stent-retriever; FPE = first pass effect; ICH = intracranial hemorrhage; LVO = large vessel occlusion; MT = mechanical thrombectomy; SAH = subarachnoid hemorrhage; SICH = symptomatic intracranial hemorrhage; SSR = single stent-retriever.

Background: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we evaluated the accuracy of DL in diagnosing pneumonia on CXR using a more robust reference diagnosis. Methods: We trained a DL convolutional neural network model to diagnose pneumonia and evaluated its accuracy in two prospective pneumonia cohorts including 430 patients, for whom the reference diagnosis was determined a posteriori by a multidisciplinary expert panel using multimodal data. The performance of the DL model was compared with that of senior radiologists and emergency physicians reviewing CXRs and that of radiologists reviewing computed tomography (CT) performed concomitantly. Results: Radiologists and DL showed a similar accuracy on CXR for both cohorts (p ≥ 0.269): cohort 1, radiologist 1 75.5% (95% confidence interval 69.1–80.9), radiologist 2 71.0% (64.4–76.8), DL 71.0% (64.4–76.8); cohort 2, radiologist 70.9% (64.7–76.4), DL 72.6% (66.5–78.0). The accuracy of radiologists and DL was significantly higher (p ≤ 0.022) than that of emergency physicians (cohort 1 64.0% [57.1–70.3], cohort 2 63.0% [55.6–69.0]). Accuracy was significantly higher for CT (cohort 1 79.0% [72.8–84.1], cohort 2 89.6% [84.9–92.9]) than for CXR readers including radiologists, clinicians, and DL (all p-values < 0.001). Conclusions: When compared with a robust reference diagnosis, the performance of AI models to identify pneumonia on CXRs was inferior than previously reported but similar to that of radiologists and better than that of emergency physicians. Relevance statement: The clinical relevance of AI models for pneumonia diagnosis may have been overestimated. AI models should be benchmarked against robust reference multimodal diagnosis to avoid overestimating its performance. Trial registration: NCT02467192, and NCT01574066. Key point: • We evaluated an openly-access convolutional neural network (CNN) model to diagnose pneumonia on CXRs. • CNN was validated against a strong multimodal reference diagnosis. • In our study, the CNN performance (area under the receiver operating characteristics curve 0.74) was lower than that previously reported when validated against radiologists’ diagnosis (0.99 in a recent meta-analysis). • The CNN performance was significantly higher than emergency physicians’ (p ≤ 0.022) and comparable to that of board-certified radiologists (p ≥ 0.269). Graphical

Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

A generative adversarial network accurately translates texture between manufacturers at image level on chest radiographs, reduces the intermanufacturer variability of radiomic features, and improve... Purpose To assess the contribution of a generative adversarial network (GAN) to improve intermanufacturer reproducibility of radiomic features (RFs). Materials and Methods The authors retrospective...