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.

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.

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.

Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: A matched case-control study.

Breast-conserving surgery (BCS) is a mainstay in breast cancer treatment. For nonpalpable breast cancers, current strategies have limited accuracy, contributing to high positive margin rates. We developed NaviKnife, a surgical navigation system based on real-time electromagnetic (EM) tracking. The goal of this study was to confirm the feasibility of intraoperative EM navigation in patients with nonpalpable breast cancer and to assess the potential value of surgical navigation. We recruited 40 patients with ultrasound visible, single, nonpalpable lesions, undergoing BCS. Feasibility was assessed by equipment functionality and sterility, acceptable duration of the operation, and surgeon feedback. Secondary outcomes included specimen volume, positive margin rate, and reoperation outcomes. Study patients were compared to a control group by a matched case-control analysis. There was no equipment failure or breach of sterility. The median operative time was 66 (44-119) minutes with NaviKnife vs 65 (34-158) minutes for the control (P = .64). NaviKnife contouring time was 3.2 (1.6-9) minutes. Surgeons rated navigation as easy to setup, easy to use, and useful in guiding nonpalpable tumor excision. The mean specimen volume was 95.4 ± 73.5 cm3 with NaviKnife and 140.7 ± 100.3 cm3 for the control (P = .01). The positive margin rate was 22.5% with NaviKnife and 28.7% for the control (P = .52). The re-excision specimen contained residual disease in 14.3% for NaviKnife and 50% for the control (P = .28). Our results demonstrate that real-time EM navigation is feasible in the operating room for BCS. Excisions performed with navigation result in the removal of less breast tissue without compromising postive margin rates.

Patient-Specific Surgical Guidance System for Intelligent Orthopaedics.

Clinical benefits for image-guided orthopaedic surgical systems are often measured in improved accuracy and precision of tool trajectories, prosthesis component positions and/or reduction of revision rate. However, with an ever-increasing demand for orthopaedic procedures, especially joint replacements, the ability to increase the number of surgeries, as well as lowering the costs per surgery, is generating a similar interest in the evaluation of image-guided orthopaedic systems. Patient-specific instrument guidance has recently gained popularity in various orthopaedic applications. Studies have shown that these guides are comparable to traditional image-guided systems with respect to accuracy and precision of the navigation of tool trajectories and/or prosthesis component positioning. Additionally, reports have shown that these single-use instruments also improve operating room management and reduce surgical time and costs. In this chapter, we discuss how patient-specific instrument guidance provides benefits to patients as well as to the health-care community for various orthopaedic applications.

Component Position and Metal Ion Levels in Computer-Navigated Hip Resurfacing Arthroplasty.

BACKGROUND: Metal ion levels are used as a surrogate marker for wear in hip resurfacing arthroplasties. Improper component position, particularly on the acetabular side, plays an important role in problems with the bearing surfaces, such as edge loading, impingement on the acetabular component rim, lack of fluid-film lubrication, and acetabular component deformation. There are little data regarding femoral component position and its possible implications on wear and failure rates. The purpose of this investigation was to determine both femoral and acetabular component positions in our cohort of mechanically stable hip resurfacing arthroplasties and to determine if these were related to metal ion levels. METHODS: One hundred fourteen patients who had undergone a computer-assisted metal-on-metal hip resurfacing were prospectively followed. Cobalt and chromium levels, Harris Hip, and UCLA activity scores in addition to measures of the acetabular and femoral component position and angles of the femur and acetabulum were recorded. RESULTS: Significant changes included increases in the position of the acetabular component compared to the native acetabulum; increase in femoral vertical offset; and decreases in global offset, gluteus medius activation angle, and abductor arm angle (P < .05). Multiple regression analysis found no significant predictors of cobalt and chromium metal ion levels. CONCLUSION: Femoral and acetabular components placed in acceptable position failed to predict increased metal ion levels, and increased levels did not adversely impact patient function or satisfaction. Further research is necessary to clarify factors contributing to prosthesis wear.

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.

Image-guided distal radius osteotomy using patient-specific instrument guides.

In this article, we describe a method for computer-assisted distal radius osteotomies in which computer-generated, patient-specific plastic guides are used for intraoperative guidance. Before surgery, the correction and plate location are planned using computed tomography scans for both radii and ulnae, and the planned locations of the distal and proximal drill holes for the plate are saved. A plastic, patient-specific instrument guide is created using a rapid prototyping machine into which a mirror image of intraoperative, accessible bone structure of the distal radius is integrated. This allows for unique positioning of the guide during surgery. For each planned drill location, a guidance hole is incorporated into the guide. During surgery, a conventional incision is made, and the guide is positioned on the radius. The surgeon drills the holes for the plate screws into the intact radius and performs the osteotomy using the conventional technique. Using the predrilled holes, the surgeon affixes the plate to the radius fragments. The guides are easy to integrate into the surgical workflow and minimize the need for intraoperative fluoroscopy for guidance of the procedure.

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.

Computer-assisted mosaic arthroplasty using patient-specific instrument guides.

PURPOSE: Success of mosaic arthroplasty requires that the transplanted plugs be positioned to reconstruct the curvature and height of the original articular surface. This case report demonstrates how to achieve correct plug positioning using patient-specific instrument guides manufactured on a 3D printer. METHODS: Using a 3D computer model of bone and cartilage, the harvesting of plugs and their placement at the defect site was planned on the computer. Instrument guides were manufactured in thermoplastic on a 3D printer; the bottom surface of the guides fit to the contour of the knee and the top surface contained holes to precisely position the surgical instruments. The instrument guides were used on a young female patient to repair a large articular cartilage defect in the left knee. RESULTS: The patient showed an increased range of motion in the knee and also a decrease in pain and discomfort at her 2-year follow-up. A CT arthrogram at 2 years postoperative showed a smooth and appropriate contour of the reconstructed cartilage over the defect. CONCLUSIONS: Image-based preoperative planning and the use of patient-specific instrument guides can yield a good patient outcome without requiring optically tracked intraoperative guidance.

Are hip resurfacing arthroplasties meeting the needs of our patients? A 2-year follow-up study.

Hip resurfacing arthroplasty (HRA) is a treatment of end-stage hip arthritis in young patients with excellent bone stock. One hundred four consecutive HRAs (Depuy ASR, Warsaw, Ind) were performed with 36-Item Short Form Health Survey (SF-36), Western Ontario and McMaster University Osteoarthritis Index, Harris Hip Scores, and University of California, Los Angeles activity ratings obtained preoperatively, at 6 months, and at 1 and 2 years postoperatively. Four patients required conversion to total hip arthroplasty. All patients showed significant improvements in their activity, pain, stiffness, and function postoperatively. Patients with lower SF-36 mental component scores (MCSs) improved their MCS compared with those of the general population, as well as improving their pain and physical functioning scores. These findings demonstrate reliable improvements in standard quality of life measures in patients undergoing HRA, including those with low preoperative SF-36 MCS.

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.

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.

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.

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.

The effect of the cam deformity on the insertion of the femoral component in hip resurfacing.

Surface arthroplasty simulations were generated using 3-dimensional computed tomographic scans from 61 consecutive patients presenting with idiopathic osteoarthritis to evaluate the change in femoral component positioning that would allow optimal alignment when resurfacing a cam-type deformity. Anatomical parameters were measured to quantify the influence of the deformity on the insertion technique of the femoral implant. A modified femoral head ratio was initially calculated from plain radiographs to define the severity of cam deformity in these patients. A severe deformity required more superior translation of the entry point and greater reaming depth to allow safe insertion with optimal implant alignment. This could be achieved while preserving the leg length, minimizing the component size, and maximizing the amount of host bone contact, although the horizontal femoral offset was reduced. These findings suggest that the femoral component can be safely inserted by modifying the surgical technique despite progressive deformity of the femoral head.

On the use of laser scans to validate reverse engineering of bony anatomy.

There is a growing body of evidence to suggest the arthritic hip is an irregularly-shaped, aspherical joint, especially in severely pathological cases. Current methods used to study the shape and motion of the hip in-vivo, are invasive and impractical. This study aimed to assess whether a plastic model of the hip joint can be accurately made from a pelvic CT scan. A cadaver hemi-pelvis was CT imaged and segmented from which a 3D plastic model of the proximal femur and hemi-pelvis were fabricated using rapid-prototyping. Both the plastic model and the cadaver were then imaged using a high-resolution laser scanner. A three-way shape analysis was performed to compare the goodness-of-fit between the cadaver, image segmentation, and the plastic model. Overall, we obtained sub-millimeter fit accuracy between all three hip representations. Shape fit was least favorable in areas where the boundary between cartilage and bone is difficult to distinguish. We submit that rapid-prototyping is an accurate and efficient mechanism for obtaining 3D specimens as a means to further study the irregular geometry of the hip.

Registration stability of physical templates in hip surgery.

We tested the registration stability of individualized templates in a consecutive study with 80 patients undergoing hip-resurfacing surgery. These templates physically encode registration and navigation parameters but do not require a computer during the actual surgery. The surgical target was the placement of the femoral guidance pin during hip resurfacing, which is a difficult and highly variable task using conventional instruments. The drill trajectory for the guidance pin of the femoral component was planned on a 3D computer model of the femur derived from a preoperative computed tomography (CT) scan. A surface-matched drilling template was designed to perform mechanical registration on the bone surface and had a hole for the drill guide; the template was created using a rapid prototyping machine. Intraoperatively, the individualized template was positioned on the patient anatomy and the pin was drilled into the femoral neck. The final achieved pin orientation and position were measured using an optoelectronic CT-based navigation system. The measured mean deviation between planned and actual central pin alignment of 0.05° in valgus and 2.8° in anteversion shows that the proposed individualized templates for hip resurfacing have reliable registration.

Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery.

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.