Risk factors for persistent post surgical pain (PPSP): a systematic review and meta-analysis

Persistent postsurgical pain (PPSP) is reported as recurrent and frequently disabling complication of many surgical procedures. The consequences for PPSP not only reduce the quality of life for patients but also financially tax the health care system, considering the volume of surgical procedures performed annually. Development of chronic pain has been proposed to involve a complex pathophysiology combined with pre-, intra-, and post-operative risk factors. There is no definite recommendation on which factor to assess (in which surgery) and what tools to utilize for conducting a study on PPSP, since many recognized risk factors for PPSP are contradictory. For a comprehensive overview of major PPSP risk factors for identification and possible prevention, we conducted a systematic review and meta-analysis of the published literature on the risk factors across six major surgical groups: breast surgery, chest/thoracic surgery, total hip arthroplasty/total knee arthroplasty (THA/TKA), gynecologic surgery, iliac crest bone harvest (ICBH), and groin hernia repair. Furthermore, to assess the generalizability of the meta-analysis results, we sought to conduct a retrospective, cross-sectional study examining the prevalence and major risk factors of PPSP after cystectomy for bladder cancer. The meta-analysis found that no single risk factor was associated with PPSP across all surgical groups. Age and previous surgery were found to be risk factors for PPSP in gynecologic surgery. For thoracic surgery, male sex and BMI were found as risk factors for PPSP. Surgical duration, presurgical chronic pain, and BMI were risk factors for groin hernia repair. The prevalence of PPSP in our cystectomy study was 22.1%. Female sex and presurgical chronic pain were risk factors significantly associated with PPSP after cystectomy. No risk factors were universally associated with PPSP. Persistent pain after each type of surgical procedure appear to have separate set risk factors among age, BMI, sex, previous surgery, and presurgical pain

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal MR slices, where only two types of these organs were annotated for training; and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only tumor cores in one MR sequence were annotated for training. Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.Comment: 11 pages, 11 figure

A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations. In the composite volume, instead of intensity values, we estimate a tensor at every voxel. The resultant tensor image encapsulates the directional information of the underlying imaging data and can be used to generate ultrasound volumes from arbitrary, potentially unseen, probe positions. Extending the work of Hennersperger et al., we introduce a log-Euclidean framework to ensure that the tensors are positive-definite, eventually ensuring non-negative images. Additionally, we regularise the underpinning ill-posed variational problem while preserving edge information by relying on a total variation penalisation of the tensor field in the log domain. We present results on in vivo human data to show the efficacy of the approach.Comment: SPIE Medical Imaging 201

Patient-Specific 3D Printed Models for Education, Research and Surgical Simulation

3D printing techniques are increasingly used in engineering science, allowing the use of computer aided design (CAD) to rapidly and inexpensively create prototypes and components. There is also growing interest in the application of these techniques in a clinical context for the creation of anatomically accurate 3D printed models from medical images for therapy planning, research, training and teaching applications. However, the techniques and tools available to create 3D models of anatomical structures typically require specialist knowledge in image processing and mesh manipulation to achieve. In this book chapter we describe the advantages of 3D printing for patient education, healthcare professional education, interventional planning and implant development. We also describe how to use medical image data to segment volumes of interest, refine and prepare for 3D printing. We will use a lung as an example. The information in this section will allow anyone to create own 3D printed models from medical image data. This knowledge will be of use to anyone with little or no previous experience in medical image processing who have identified a potential application for 3D printing in a medical context, or those with a more general interest in the techniques

Feature Selection To Facilitate Surgical Planning From MRI Of Placenta Accreta Spectrum Disorder

Feature Selection Models provide a ranking of pathological MRI markers able to predict the outcome of Placenta Accreta Spectrum Disorder, which could be used to aid in clinical decision-making and improve maternal outcome. The potential being to reduce the workload of radiologists by establishing the most clinically relevant pathological MRI markers that predict outcome. Our results found three pathological markers to have the highest ranking to the outcomes with an average accuracy of 75% using a Random Forest Selection Model and Boruta algorithm

Use of Super Resolution Reconstruction MRI for surgical planning in Placenta Accreta Spectrum Disorder: Case Series

INTRODUCTION: Comprehensive imaging using ultrasound and MRI of placenta accreta spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard MRI of the placenta is limited by between-slice motion which can be mitigated by super-resolution reconstruction (SRR) MRI. We applied SRR in suspected PAS cases to determine its ability to enhance anatomical placental assessment and predict adverse maternal outcome. METHODS: Suspected PAS patients (n = 22) underwent MRI at a gestational age (weeks + days) of (32+3±3+2, range (27+1-38+6)). SRR of the placental-myometrial-bladder interface involving rigid motion correction of acquired MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume, was achieved in twelve. 2D MRI or SRR images alone, and paired data were assessed by four radiologists in three review rounds. All radiologists were blinded to results of the ultrasound, original MR image reports, case outcomes, and PAS diagnosis. A Random Forest Classification model was used to highlight the most predictive pathological MRI markers for major obstetric haemorrhage (MOH), bladder adherence (BA), and placental attachment depth (PAD). RESULTS: At delivery, four patients had placenta praevia with no abnormal attachment, two were clinically diagnosed with PAS, and six had histopathological PAS confirmation. Pathological MRI markers (T2-dark intraplacental bands, and loss of retroplacental T2-hypointense line) predicting MOH were more visible using SRR imaging (accuracy 0.73), in comparison to 2D MRI or paired imaging. Bladder wall interruption, predicting BA, was only easily detected by paired imaging (accuracy 0.72). Better detection of certain pathological markers predicting PAD was found using 2D MRI (placental bulge and myometrial thinning (accuracy 0.81)), and SRR (loss of retroplacental T2-hypointense line (accuracy 0.82)). DISCUSSION: The addition of SRR to 2D MRI potentially improved anatomical assessment of certain pathological MRI markers of abnormal placentation that predict maternal morbidity which may benefit surgical planning

Post-vasectomy semen analysis: Optimizing laboratory procedures and test interpretation through a clinical audit and global survey of practices

Purpose: The success of vasectomy is determined by the outcome of a post-vasectomy semen analysis (PVSA). This article describes a step-by-step procedure to perform PVSA accurately, report data from patients who underwent post vasectomy semen analysis between 2015 and 2021 experience, along with results from an international online survey on clinical practice. Materials and Methods: We present a detailed step-by-step protocol for performing and interpretating PVSA testing, along with recommendations for proficiency testing, competency assessment for performing PVSA, and clinical and laboratory scenarios. Moreover, we conducted an analysis of 1,114 PVSA performed at the Cleveland Clinic’s Andrology Laboratory and an online survey to understand clinician responses to the PVSA results in various countries. Results: Results from our clinical experience showed that 92.1% of patients passed PVSA, with 7.9% being further tested. A total of 78 experts from 19 countries participated in the survey, and the majority reported to use time from vasectomy rather than the number of ejaculations as criterion to request PVSA. A high percentage of responders reported permitting unprotected intercourse only if PVSA samples show azoospermia while, in the presence of few non-motile sperm, the majority of responders suggested using alternative contraception, followed by another PVSA. In the presence of motile sperm, the majority of participants asked for further PVSA testing. Repeat vasectomy was mainly recommended if motile sperm were observed after multiple PVSA’s. A large percentage reported to recommend a second PVSA due to the possibility of legal actions. Conclusions: Our results highlighted varying clinical practices around the globe, with controversy over the significance of non-motile sperm in the PVSA sample. Our data suggest that less stringent AUA guidelines would help improve test compliance. A large longitudinal multi-center study would clarify various doubts related to timing and interpretation of PVSA and would also help us to understand, and perhaps predict, recanalization and the potential for future failure of a vasectomy.American Center for Reproductive Medicin

Activating PIK3CA Mutations Induce an Epidermal Growth Factor Receptor (EGFR)/Extracellular Signal-regulated Kinase (ERK) Paracrine Signaling Axis in Basal-like Breast Cancer

Mutations in PIK3CA, the gene encoding the p110α catalytic subunit of phosphoinositide 3-kinase (PI3K) have been shown to transform human mammary epithelial cells (MECs). These mutations are present in all breast cancer subtypes, including basal-like breast cancer (BLBC). Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified 72 protein expression changes in human basal-like MECs with knock-in E545K or H1047R PIK3CA mutations versus isogenic MECs with wild-type PIK3CA. Several of these were secreted proteins, cell surface receptors or ECM interacting molecules and were required for growth of PIK3CA mutant cells as well as adjacent cells with wild-type PIK3CA. The proteins identified by MS were enriched among human BLBC cell lines and pointed to a PI3K-dependent amphiregulin/EGFR/ERK signaling axis that is activated in BLBC. Proteins induced by PIK3CA mutations correlated with EGFR signaling and reduced relapse-free survival in BLBC. Treatment with EGFR inhibitors reduced growth of PIK3CA mutant BLBC cell lines and murine mammary tumors driven by a PIK3CA mutant transgene, all together suggesting that PIK3CA mutations promote tumor growth in part by inducing protein changes that activate EGFR