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. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. RESULTS: Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites. CONCLUSIONS: The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach.

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. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains. METHODS: In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks. RESULTS: Through extensive ablation studies, we show that we can surpass previous benchmarks of margin evaluation in BCC surgery using iKnife data, achieving an area under the receiver operating characteristic curve (AUC) of 81%. We also depict the attention maps of the developed DL models to evaluate the biological relevance of the embedding space CONCLUSIONS: We propose a new method for characterizing tissue at the surgical margins, using mass spectrometry data from cancer surgery.

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. METHODS: Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports. RESULTS: The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports. CONCLUSION: We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics.

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. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence 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. METHODS: The mitral valve annuli of 100 children and young adults with normal findings on three-dimensional echocardiography were modeled in four different cardiac phases using the SlicerHeart extension for 3D Slicer. Annular metrics were quantified using SlicerHeart, and optimal normalization to body surface area was explored. Mean annular shapes and the principal components of variation were computed using custom code implemented in a new SlicerHeart module (Annulus Shape Analyzer). Shape was regressed over metrics of age and body surface area, and mean shapes for five age-stratified groups were generated. RESULTS: The ratio of annular height to commissural width of the mitral valve ("saddle shape") changed significantly throughout age for systolic phases (P < .001) but within a narrow range (median range, 0.20-0.25). Annular metrics changed statistically significantly between the diastolic and systolic phases of the cardiac cycle. Visually, the annular shape was maintained with respect to age and body surface area. Principal-component analysis revealed that the pediatric mitral annulus varies primarily in size (mode 1), ratio of annular height to commissural width (mode 2), and sphericity (mode 3). CONCLUSIONS: The saddle-shaped mitral annulus is maintained throughout childhood but varies significantly throughout the cardiac cycle. The major modes of variation in the pediatric mitral annulus are due to size, ratio of annular height to commissural width, and sphericity. The generation of age- and size-specific mitral annular shapes may inform the development of appropriately scaled absorbable or expandable mitral annuloplasty rings for children.

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. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost phantoms were provided. Overall, this article aims to summarize a breadth of ultrasound-compatible phantom research to enable informed phantom methods selection.

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). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI-MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for which are known to carry various nerve fiber types were harvested from a fresh cadaveric donor: mixed, motor and sensory (sciatic and femoral); cutaneous, sensory (sural); and autonomic (vagus). Tissue samples were harvested to include the nerve bundles in addition to surrounding connective tissue. Samples were flash-frozen, embedded in optimal cutting temperature compound in cross-section, and sectioned at 14 µm. Following DESI-MSI analysis, identical tissue sections were stained with hematoxylin and eosin. In this proof-of-concept study, a combination of multivariate and univariate statistical methods was used to evaluate molecular differences between the nerve and adjacent tissue and between nerve subtypes. The acquired mass spectral profiles of the peripheral nerve samples presented trends in ion abundances that seemed to be characteristic of nerve tissue and spatially corresponded to the associated histology of the tissue sections. Principal component analysis (PCA) supported the separation of the samples into distinct nerve and adjacent tissue classes. This classification was further supported by the K-means clustering analysis, which showed separation of the nerve and background ions. Differences in ion expression were confirmed using ANOVA which identified statistically significant differences in ion expression between the nerve subtypes. The PCA plot suggested some separation of the nerve subtypes into four classes which corresponded with the nerve types. This was supported by the K-means clustering. Some overlap in classes was noted in these two clustering analyses. This study provides emerging evidence that DESI-MSI is an effective tool for metabolomic profiling of peripheral nerves. Our results suggest that peripheral nerves have molecular profiles that are distinct from the surrounding connective tissues and that DESI-MSI may be able to discriminate between nerve subtypes. DESI-MSI of peripheral nerves may be a valuable technique that could be used to improve our understanding of peripheral nerve anatomy and physiology. The ability to utilize ambient mass spectrometry techniques in real time could also provide an unprecedented advantage for surgical decision making, including in nerve-sparing procedures in the future.

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. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient's computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning. Six volunteers planned pedicle screws' position and orientation with the AR system and on a 2D desktop planner. We compared the placement accuracy of each screw with both methods. Finally, we administered a questionnaire to all participants to assess their experience with the AR system. RESULTS: The latency in message exchange is sufficiently low to enable real-time communication between the platforms. The AR method was non-inferior to the 2D desktop planner, with a mean error of 2.1 ± 1.4 mm. Moreover, 98% of the screw placements performed with the AR system were successful, according to the Gertzbein-Robbins scale. The average questionnaire outcomes were 4.5/5. CONCLUSIONS: Real-time communication between Microsoft HoloLens 2 and 3D Slicer is feasible and supports accurate planning for pedicle screw placement.

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. METHODS: A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a preclinical porcine model (n = 6 vertebrae). RESULTS: Dose analysis was successfully performed, with generation and display of thermal dose volumes, thermal damage, dose volume histograms and isodose contours. Usability testing showed an overall positive response to the TPS as beneficial to safe and effective RFA. The in vivo porcine study showed good agreement between the manually segmented thermally damaged volumes vs. the damage volumes identified from the TPS (Dice Similarity Coefficient = 0.71 ± 0.03, Hausdorff distance = 1.2 ± 0.1 mm). CONCLUSION: A TPS specifically dedicated to RFA in the bony spine could help account for tissue heterogeneities in both thermal and electrical properties. A TPS would enable visualization of damage volumes in 2D and 3D, assisting clinicians in decisions about potential safety and effectiveness prior to performing RFA in the metastatic spine.

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. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive of pro-apoptotic mechanisms, was increased in LVI-negative compared to LVI-positive patients. This study provides evidence of the potential clinical utility of spatially-resolved DESI profiles to enhance the information available to clinicians for CRC diagnosis and prognosis.

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. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR. RESULTS: In univariate modeling, patients with moderate or greater TR had larger TV annular diameters and area, greater annular distance between the anteroseptal commissure and anteroposterior commissure, greater leaflet billow volume, and more laterally directed anterior papillary muscle angles compared to valves with mild or less TR (all P<0.001). In multivariate modeling greater total billow volume, lower anterior papillary muscle angle, and greater distance between the anteroposterior commissure and anteroseptal commissure were associated with moderate or greater TR (P<0.001, C statistic=0.85). Larger right ventricle volumes were associated with moderate or greater TR (P<0.001). TV shape analysis revealed structural features associated with TR, but also highly heterogeneous TV leaflet structure. CONCLUSIONS: Moderate or greater TR in patients with hypoplastic left heart syndrome with a Fontan circulation is associated with greater leaflet billow volume, a more laterally directed anterior papillary muscle angle, and greater annular distance between the anteroseptal commissure and anteroposterior commissure. However, there is significant heterogeneity of structure in the TV leaflets in regurgitant valves. Given this variability, an image-informed patient-specific approach to surgical planning may be needed to achieve optimal outcomes in this vulnerable and challenging population.

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. The experience of patients and physicians was recorded in questionnaires. RESULTS: The main findings were as follows: 308 patients and 47 physicians participated in the study. In the patient group, 96.8% reported an improved level of understanding of prostate disease and 90.6% reported an improved ability to ask questions during consultation. Among the physicians, 91.5% reported improved communication skills and 100% reported an improved ability to obtain patient consent for subsequent treatment. At the same time, 76.6% of physicians noted that using the computer model lengthened the consultation. CONCLUSION: This exploratory study found that the use of a 3D digital anatomy model in urology consultations was received overwhelmingly favorably by both patients and physicians, and it was perceived to improve the quality of communication between patient and physician. A randomized study is needed to confirm the preliminary findings and further quantify the improvements in the quality of patient-physician communication.

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). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the m/z range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.

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. We describe the rationale, development, implementation, and application of SlicerHeart, a cardiac-focused toolkit for image analysis built upon 3D Slicer, an open-source image computing platform. We designed and implemented multiple Python scripted modules within 3D Slicer to import, register, and view 3DE data, including new code to volume render and crop 3DE. In addition, we developed dedicated workflows for the modeling and quantitative analysis of multi-modality image-derived heart models, including heart valves. Finally, we created and integrated new functionality to facilitate the planning of cardiac interventions and surgery. We demonstrate application of SlicerHeart to a diverse range of cardiovascular modeling and simulation including volume rendering of 3DE images, mitral valve modeling, transcatheter device modeling, and planning of complex surgical intervention such as cardiac baffle creation. SlicerHeart is an evolving open-source image processing platform based on 3D Slicer initiated to support the investigation and treatment of congenital heart disease. The technology in SlicerHeart provides a robust foundation for 3D image-based investigation in cardiovascular medicine.

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. In this study, we propose integration of uncertainty estimation in deep models to factor predictive confidence into margin detection in cancer surgery. METHODS: iKnife is used to collect 693 spectra of cancer and healthy samples acquired from 91 patients during basal cell carcinoma resection. A Bayesian neural network and two baseline models are trained on these data to perform classification as well as uncertainty estimation. The samples with high estimated uncertainty are then removed, and new models are trained using the clean data. The performance of proposed and baseline models, with different ratios of filtered data, is then compared. RESULTS: The data filtering does not improve the performance of the baseline models as they cannot provide reliable estimations of uncertainty. In comparison, the proposed model demonstrates a statistically significant improvement in average balanced accuracy (75.2%), sensitivity (74.1%) and AUC (82.1%) after removing uncertain training samples. We also demonstrate that if highly uncertain samples are predicted and removed from the test data, sensitivity further improves to 88.2%. CONCLUSIONS: This is the first study that applies uncertainty estimation to inform model training and deployment for tissue recognition in cancer surgery. Uncertainty estimation is leveraged in two ways: by factoring a measure of input noise in training the models and by including predictive confidence in reporting the outputs. We empirically show that considering uncertainty for model development can help improve the overall accuracy of a margin detection system using REIMS.

nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning.

Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-the-art nnU-Net. Additionally, this study proposes a polyvinyl alcohol cryogel (PVA-C) breast phantom, based on its automated segmentation approach, to enable planning and navigation experiments for robotic breast surgery. The dataset consists of multimodality breast MRI of T2W and STIR images obtained from 10 patients. A statistical analysis of segmentation tasks emphasizes the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. We first use a single class labeling to segment the breast region and then exploit it as an input for three-class labeling to segment fatty, fibroglandular (FGT), and tumorous tissues. The first network has a 0.95 DCS, while the second network has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively. Clinical Relevance-This research is relevant to the breast surgery community as it establishes a deep learning-based (DL) algorithmic and phantomic foundation for surgical planning and navigation that will exploit preoperative multimodal MRI and intraoperative ultrasound to achieve highly cosmetic breast surgery. In addition, the planning and navigation will guide a robot that can cut, resect, bag, and grasp a tissue mass that encapsulates breast tumors and positive tissue margins. This image-guided robotic approach promises to potentiate the accuracy of breast surgeons and improve patient outcomes.

Sensor-Based Automated Detection of Electrosurgical Cautery States.

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools' location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery-robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.

Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions.

Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarly, for robotics, the open-source middleware suite robot operating system (ROS) is the standard development framework. In the past, there have been several "ad hoc" attempts made to bridge both tools; however, they are all reliant on middleware and custom interfaces. Additionally, none of these attempts have been successful in bridging access to the full suite of tools provided by ROS or 3D Slicer. Therefore, in this paper, we present the SlicerROS2 module, which was designed for the direct use of ROS2 packages and libraries within 3D Slicer. The module was developed to enable real-time visualization of robots, accommodate different robot configurations, and facilitate data transfer in both directions (between ROS and Slicer). We demonstrate the system on multiple robots with different configurations, evaluate the system performance and discuss an image-guided robotic intervention that can be prototyped with this module. This module can serve as a starting point for clinical system development that reduces the need for custom interfaces and time-intensive platform setup.

Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation.

PURPOSE: Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process for intraoperative guidance. Segmentation accuracy is evaluated by both pixel-based metrics and expert visual rating. METHODS: This retrospective study includes 7318 intraoperative ultrasound images acquired from 33 breast cancer patients, randomly split between 80:20 for training and testing. We implement a u-net architecture to label each pixel on ultrasound images as either tumor or healthy breast tissue. Quantitative metrics are calculated to evaluate the model's accuracy. Contour quality and usability are also assessed by fellowship-trained breast radiologists and surgical oncologists. Additionally, the viability of using our u-net model in an existing surgical navigation system is evaluated by measuring the segmentation frame rate. RESULTS: The mean dice similarity coefficient of our u-net model is 0.78, with an area under the receiver-operating characteristics curve of 0.94, sensitivity of 0.95, and specificity of 0.67. Expert visual ratings are positive, with 93% of responses rating tumor contour quality at or above 7/10, and 75% of responses rating contour quality at or above 8/10. Real-time tumor segmentation achieved a frame rate of 16 frames-per-second, sufficient for clinical use. CONCLUSION: Neural networks trained with intraoperative ultrasound images provide consistent tumor segmentations that are well received by clinicians. These findings suggest that neural networks are a promising adjunct to alleviate radiologist workload as well as improving efficiency in breast-conserving surgery navigation systems.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review.

OBJECTIVE: A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeon's ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines. METHODS: We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. RESULTS: Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. CONCLUSION: Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. SIGNIFICANCE: By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.