Prevalence of reported penicillin allergy and associations with perioperative complications, length of stay, and cost in patients undergoing elective cancer surgery.

Objective: Up to 10% of patients report penicillin allergy (PA), although only 1% are truly affected by Ig-E-mediated allergies. PA has been associated with worse postoperative outcomes, but studies on the impact of reported PA in cancer patients are lacking, and especially in these multimorbid patients, a non-complicated course is of utmost importance. Methods: Retrospective analysis of patients undergoing elective oncological surgery at a tertiary reference center. Data on surgical site infections (SSI), postoperative complications (measured by Clavien-Dindo classification and Comprehensive Complication Index (CCI)), hospitalization duration, and treatment costs were collected. Results: Between 09/2019 and 03/2020, 152 patients were identified. 16/152 patients (11%) reported PA, while 136/152 (89%) did not. There were no differences in age, BMI, Charlson Comorbidity Index, and smoking status between groups (p > 0.4). Perioperative beta-lactam antibiotics were used in 122 (89.7%) and 15 (93.8%) patients without and with reported PA, respectively. SSI and mean numbers of infections occurred non-significantly more often in patients with PA (p = 0.2 and p = 0.47). The median CCI was significantly higher in PA group (26 vs. 51; p = 0.035). The median hospitalization duration and treatment costs were similar between non-PA and PA groups (4 vs 3 days, p = 0.8; 16'818 vs 17'444 CHF, p = 0.4). Conclusions: In patients undergoing cancer surgery, reported PA is common. Failure to question the unproven PA may impair perioperative outcomes. For this reason, patient and provider education on which reactions constitute a true allergy would also assist in allergy de-labeling. In addition, skin testing and oral antibiotic challenges can be performed to identify the safe antibiotics and to de-label appropriate patients.

Nerve-sparing Robot-assisted Retroperitoneal Lymph Node Dissection: The Monoblock Technique.

BACKGROUND: Retroperitoneal lymph node dissection (RPLND) is a treatment option for men with stage 1 or 2 testis cancer and the standard of care for men with postchemotherapy retroperitoneal residual disease. Given the morbidity of RPLND, four important surgical modifications have been proposed: minimally invasive access, nerve-sparing resection, template resection, and en-bloc resection. OBJECTIVE: To describe the surgical steps and perioperative outcomes of robotic nerve-sparing unilateral template RPLND with en-bloc resection (roboRPLND-NS+). DESIGN SETTING AND PARTICIPANTS: From 2017 to 2019, five patients with suspicion of retroperitoneal metastatic testicular cancer on abdominopelvic computed tomography underwent roboRPLND-NS+ at a single referral center. All surgeries were carried out by a single surgeon who has performed more than 500 extended and more than 50 super-extended robot-assisted lymph node dissections. SURGICAL PROCEDURE: A lateral transperitoneal robotic approach with a da Vinci Xi Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) in six-arm configuration was used. The sympathetic chains, postganglionic sympathetic fibers, and hypogastric plexus were preserved as much as possible to ensure a nerve-sparing procedure. The template borders consisted of the renal vein cranially, the ureter laterally, the interaortocaval space medially, the common iliac artery caudally, and the psoas muscle dorsally for the right and left modified RPLND templates. Lymph nodes and the surrounding fatty tissue were progressively resected from the common iliac vessels and the abdominal aorta using the split-and-roll technique, and all of the template tissue was resected as a single specimen. Intraoperative and postoperative complications were recorded. MEASUREMENTS: Lymph node yield and perioperative and postoperative oncological and functional outcomes were measured. RESULTS AND LIMITATIONS: The median patient age was 38 yr (interquartile range [IQR] 32-41) and the median operative time was 274 min (IQR 238-280). Node metastases were pathologically confirmed in three patients. The median number of lymph nodes removed was 19 (IQR 18-21), and the median number of positive lymph nodes was 2 (IQR 1-3). No patient experienced intraoperative or postoperative complications. The postoperative hospital stay was either 3 or 4 d. Maintenance of antegrade ejaculation was achieved in all patients. After median follow-up of 15 mo (IQR 14-30), all patients were alive and no recurrence was observed. Limitations include the low number of patients and the single surgeon experience. CONCLUSIONS: RoboRPLND-NS+ is a safe and feasible technique that allows removal of a high number of lymph nodes with good functional outcomes. Short-term survival outcomes were excellent, with no recurrences or deaths recorded. PATIENT SUMMARY: We describe a feasible and safe robot-assisted surgical procedure for removal of lymph nodes in patients with testicular cancer. Our technique has potential to decrease the medical problems arising as side effects of the surgery while achieving good cancer control.

Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks.

Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points.

A Fully Automated Pipeline for Normative Atrophy in Patients with Neurodegenerative Disease.

INTRODUCTION: Volumetric image analysis to detect progressive brain tissue loss in patients with multiple sclerosis (MS) has recently been suggested as a promising marker for "no evidence of disease activity." Software packages for longitudinal whole-brain volume analysis in individual patients are already in clinical use; however, most of these methods have omitted region-based analysis. Here, we suggest a fully automatic analysis pipeline based on the free software packages FSL and FreeSurfer. MATERIALS AND METHODS: Fifty-five T1-weighted magnetic resonance imaging (MRI) datasets of five patients with confirmed relapsing-remitting MS and mild to moderate disability were longitudinally analyzed compared to a morphometric reference database of 323 healthy controls (HCs). After lesion filling, the volumes of brain segmentations and morphometric parameters of cortical parcellations were automatically screened for global and regional abnormalities. Error margins and artifact probabilities of regional morphometric parameters were estimated. Linear models were fitted to the series of follow-up MRIs and checked for consistency with cross-sectional aging in HCs. RESULTS: As compared to leave-one-out cross-validation in a subset of the control dataset, anomaly detection rates were highly elevated in MRIs of two patients. We detected progressive volume changes that were stronger than expected compared to normal aging in 4/5 patients. In individual patients, we also identified stronger than expected regional decreases of subcortical gray matter, of cortical thickness, and areas of reducing gray-white contrast over time. CONCLUSION: Statistical comparison with a large normative database may provide complementary and rater independent quantitative information about regional morphological changes related to disease progression or drug-related disease modification in individual patients. Regional volume loss may also be detected in clinically stable patients.