Evaluating rapid evaporative ionization mass spectrometry profiles of human breast cancer cells using a 3D in vitro assay.

Rapid evaporative ionization mass spectrometry (REIMS) enables near real-time sampling and classification of tissues on the basis of lipid and small molecule profiles. Because samples are ablated during REIMS, validation of mass spectra by assignment of class labels using gold-standard methods remains challenging. Further, determining the number of abnormal cells within a mixture of normal cells by REIMS remains an elusive but critical parameter when evaluating the utility of REIMS for use in clinical settings.

Benchmarking NousNav: quantifying the spatial accuracy and clinical performance of an affordable, open-source neuronavigation system.

Purpose: NousNav is a low-cost, open-source neuronavigation platform built to address the high costs and resource limitations that hinder access to advanced neurosurgical technologies in low-resource settings. The low-cost and accessibility of the system is made possible using consumer-grade optical tracking and open-source software packages. This study aims to assess the performance of these core enabling technologies by quantifying their spatial accuracy and comparing it to a commercial gold standard.

SlicerROS2: A research and development module for image-guided robotic interventions.

Image-guided robotic interventions involve the use of medical imaging in tandem with robotics. SlicerROS2 is a software module that combines 3D Slicer and robot operating system (ROS) in pursuit of a standard integration approach for medical robotics research. The first release of SlicerROS2 demonstrated the feasibility of using the C++ API from 3D Slicer and ROS to load and visualize robots in real time.

Anomaly detection using intraoperative iKnife data: a comparative analysis in breast cancer surgery.

Purpose: Intraoperative margin assessment is crucial to ensure complete tumor removal and minimize the risk of cancer recurrence during breast-conserving surgery. The Intelligent Knife (iKnife), a mass spectrometry device that analyzes surgical smoke, shows promise in near-real-time margin evaluation. However, current AI models depend on labeled ex-vivo datasets, which are costly and time-consuming to produce.

Bone-enhanced 3D ultrasound: a non-ionizing alternative for pediatric scoliosis assessment.

Purpose: Scoliosis affects up to 5% of the population, and early detection is crucial to prevent progression and surgical intervention. X-ray imaging remains the standard for scoliosis assessment, but repeated exposure to ionizing radiation increases cancer risks, particularly in pediatric patients. This study evaluates bone-enhanced 3D ultrasound (BEUS) as a non-ionizing alternative for scoliosis measurement.

Objective skill assessment for cataract surgery from surgical microscope video.

Objective: Video offers an accessible method for automated surgical skill evaluation; however, many platforms still rely on traditional six-degree-of-freedom (6-DOF) tracking systems, which can be costly, cumbersome, and challenging to apply clinically. This study aims to demonstrate that trainee skill in cataract surgery can be assessed effectively using only object detection from monocular surgical microscope video.

Methods: One ophthalmologist and four residents performed cataract surgery on a simulated eye five times each, generating 25 recordings.

Touching the tumor boundary: a pilot study on ultrasound-based virtual fixtures for breast-conserving surgery.

Purpose: Delineating tumor boundaries during breast-conserving surgery is challenging as tumors are often highly mobile, non-palpable, and have irregularly shaped borders. To address these challenges, we introduce a cooperative robotic guidance system that applies haptic feedback for tumor localization. In this pilot study, we aim to assess if and how this system can be successfully integrated into breast cancer care.

FACT: foundation model for assessing cancer tissue margins with mass spectrometry.

Purpose: Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass Spectrometry (REIMS), a tool for real-time intraoperative margin assessment, generates spectra that require machine learning models to support clinical decision-making. However, the scarcity of labeled data in surgical contexts presents a significant challenge.

Feasibility of video-based skill assessment for percutaneous nephrostomy training in Senegal.

Percutaneous nephrostomy can be an effective means of preventing irreparable renal damage from obstructive renal disease thereby providing patients with more time to access treatment to remove the source of the blockage. In sub-Saharan Africa, where there is limited access to treatments such as dialysis and transplantation, a nephrostomy can be life-saving. Training this procedure in simulation can allow trainees to develop their technical skills without risking patient safety, but still requires an ex-pert observer to provide performative feedback.

Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study.

Background: While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes.

Methods: A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols.

From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

Purpose: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.

ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry.

Purpose: Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization.

Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM.

Euclidean and Shape-Based Analysis of the Dynamic Mitral Annulus in Children using a Novel Open-Source Framework.

Background: The dynamic shape of the normal adult mitral annulus has been shown to be important to mitral valve function. However, annular dynamics of the healthy mitral valve in children have yet to be explored. The aim of this study was to model and quantify the shape and major modes of variation of pediatric mitral valve annuli in four phases of the cardiac cycle using transthoracic echocardiography.

A Review of Low-Cost Ultrasound Compatible Phantoms.

Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature.

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning.

Purpose: Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.

Methods: We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting.

Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a molecular imaging method that can be used to elucidate the small-molecule composition of tissues and map their spatial information using two-dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS).

Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases.

Purpose: Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases.

Metabolically Active Zones Involving Fatty Acid Elongation Delineated by DESI-MSI Correlate with Pathological and Prognostic Features of Colorectal Cancer.

Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by 'gold standard' histopathology.

Modeling of the Tricuspid Valve and Right Ventricle in Hypoplastic Left Heart Syndrome With a Fontan Circulation.

Background: In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction.

Methods: TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart.

The use of 3D digital anatomy model improves the communication with patients presenting with prostate disease: The first experience in Senegal.

Objectives: We hypothesized that the use of an interactive 3D digital anatomy model can improve the quality of communication with patients about prostate disease.

Methods: A 3D digital anatomy model of the prostate was created from an MRI scan, according to McNeal's zonal anatomy classification. During urological consultation, the physician presented the digital model on a computer and used it to explain the disease and available management options.

Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats.

Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada).

Uncertainty estimation for margin detection in cancer surgery using mass spectrometry.

Purpose: Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technology for clinical margin detection. Deployment of REIMS depends on construction of reliable deep learning models that can categorize tissue according to its metabolomic signature. Challenges associated with developing these models include the presence of noise during data acquisition and the variance in tissue signatures between patients.

SlicerHeart: An open-source computing platform for cardiac image analysis and modeling.

Cardiovascular disease is a significant cause of morbidity and mortality in the developed world. 3D imaging of the heart's structure is critical to the understanding and treatment of cardiovascular disease. However, open-source tools for image analysis of cardiac images, particularly 3D echocardiographic (3DE) data, are limited.