The use of neoadjuvant lobar radioembolization prior to major hepatic resection for malignancy results in a low rate of post hepatectomy liver failure

BackgroundNeoadjuvant yttrium-90 transarterial radioembolization (TARE) is increasingly being used as a strategy to facilitate resection of otherwise unresectable tumors due to its ability to generate both tumor response and remnant liver hypertrophy. Perioperative outcomes after the use of neoadjuvant lobar TARE remain underinvestigated.MethodsA single center retrospective review of patients who underwent lobar TARE prior to major hepatectomy for primary or metastatic liver cancer between 2007 and 2018 was conducted. Baseline demographics, radioembolization parameters, pre- and post-radioembolization volumetrics, intra-operative surgical data, adverse events, and post-operative outcomes were analyzed.ResultsTwenty-six patients underwent major hepatectomy after neoadjuvant lobar TARE. The mean age was 58.3 years (17–88 years). 62% of patients (n=16) had primary liver malignancies while the remainder had metastatic disease. Liver resection included right hepatectomy or trisegmentectomy, left or extended left hepatectomy, and sectorectomy/segmentectomy in 77% (n=20), 8% (n=2), and 15% (n=4) of patients, respectively. The mean length of stay was 8.3 days (range, 3–33 days) and there were no grade IV morbidities or 90-day mortalities. The incidence of post hepatectomy liver failure (PHLF) was 3.8% (n=1). The median time to progression after resection was 4.5 months (range, 3.3–10 months). Twenty-three percent (n=6) of patients had no recurrence. The median survival was 28.9 months (range, 16.9–46.8 months) from major hepatectomy and 37.6 months (range, 25.2–53.1 months) from TARE.ConclusionsMajor hepatectomy after neoadjuvant lobar radioembolization is safe with a low incidence of PHLF.     

Manejo y seguimiento radiológico del paciente post-COVID-19

Most of the patients who overcome the SARS-CoV-2 infection do not present complications and do not require a specific follow-up, but a significant proportion (especially those with moderate / severe clinical forms of the disease) require clinicalradiological follow-up. Although there are hardly any references or clinical guidelines regarding the long-term follow-up of post-COVID-19 patients, radiological exams are being performed and monographic surveillance consultations are being set up in most of the hospitals to meet their needs. The purpose of this work is to share our experience in the management of the post-COVID-19 patient in two institutions thathave had a high incidence of COVID-19 and to propose general follow-uprecommendations from a clinical and radiological perspective.     

Extended Kalman smoother with differential evolution technique for denoising of ECG signal

Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.