Postoperative intravenous iron to treat iron-deficiency anaemia in patients undergoing cardiac surgery: a protocol for a pilot, multicentre, placebo-controlled randomized trial (the POAM trial).

Background: Iron-deficiency anaemia, occurring in 30-40% of patients undergoing cardiac surgery, is an independent risk factor for adverse outcomes. Our long-term goal is to assess if postoperative i.v. iron therapy improves clinical outcomes in patients with preoperative iron-deficiency anaemia undergoing cardiac surgery. Before conducting a definitive RCT, we first propose a multicentre pilot trial to establish the feasibility of the definitive trial. Methods: This internal pilot, double-blinded, RCT will include three centres. Sixty adults with preoperative iron-deficiency anaemia undergoing non-emergency cardiac surgery will be randomised on postoperative day 2 or 3 to receive either blinded i.v. iron (1000 mg ferric derisomaltose) or placebo. Six weeks after surgery, patients who remain iron deficient will receive a second blinded dose of i.v. iron according to their assigned treatment arm. Patients will be followed for 12 months. Clinical practice will not be otherwise modified. For the pilot study, feasibility will be assessed through rates of enrolment, protocol deviations, and loss to follow up. For the definitive study, the primary outcome will be the number of days alive and out of hospital at 90 days after surgery. Ethics and dissemination: The trial has been approved by the University Health Network Research Ethics Board (REB # 22-5685; approved by Clinical Trials Ontario funding on 22 December 2023) and will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practices guidelines, and regulatory requirements. Clinical trial registration: NCT06287619.

Current and future directions in network biology.

Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation: Not applicable.

TDC-2: Multimodal Foundation for Therapeutic Science.

Therapeutics Data Commons (tdcommons.ai) is an open science initiative with unified datasets, AI models, and benchmarks to support research across therapeutic modalities and drug discovery and development stages. The Commons 2.0 (TDC-2) is a comprehensive overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new multimodal tasks and model frameworks, and comprehensive benchmarks. TDC-2 introduces over 1,000 multimodal datasets spanning approximately 85 million cells, pre-calculated embeddings from 5 state-of-the-art single-cell models, and a biomedical knowledge graph. TDC-2 drastically expands the coverage of ML tasks across therapeutic pipelines and 10+ new modalities, spanning but not limited to single-cell gene expression data, clinical trial data, peptide sequence data, peptidomimetics protein-peptide interaction data regarding newly discovered ligands derived from AS-MS spectroscopy, novel 3D structural data for proteins, and cell-type-specific protein-protein interaction networks at single-cell resolution. TDC-2 introduces multimodal data access under an API-first design using the model-view-controller paradigm. TDC-2 introduces 7 novel ML tasks with fine-grained biological contexts: contextualized drug-target identification, single-cell chemical/genetic perturbation response prediction, protein-peptide binding affinity prediction task, and clinical trial outcome prediction task, which introduce antigen-processing-pathway-specific, cell-type-specific, peptide-specific, and patient-specific biological contexts. TDC-2 also releases benchmarks evaluating 15+ state-of-the-art models across 5+ new learning tasks evaluating models on diverse biological contexts and sampling approaches. Among these, TDC-2 provides the first benchmark for context-specific learning. TDC-2, to our knowledge, is also the first to introduce a protein-peptide binding interaction benchmark.

Contextual AI models for single-cell protein biology.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Epidermal growth factor augments the self-renewal capacity of aged hematopoietic stem cells.

Hematopoietic aging is associated with decreased hematopoietic stem cell (HSC) self-renewal capacity and myeloid skewing. We report that culture of bone marrow (BM) HSCs from aged mice with epidermal growth factor (EGF) suppressed myeloid skewing, increased multipotent colony formation, and increased HSC repopulation in primary and secondary transplantation assays. Mice transplanted with aged, EGF-treated HSCs displayed increased donor cell engraftment within BM HSCs and systemic administration of EGF to aged mice increased HSC self-renewal capacity in primary and secondary transplantation assays. Expression of a dominant negative EGFR in Scl/Tal1+ hematopoietic cells caused increased myeloid skewing and depletion of long term-HSCs in 15-month-old mice. EGF treatment decreased DNA damage in aged HSCs and shifted the transcriptome of aged HSCs from genes regulating cell death to genes involved in HSC self-renewal and DNA repair but had no effect on HSC senescence. These data suggest that EGFR signaling regulates the repopulating capacity of aged HSCs.

Comparison of Childless and Partnerless Vasectomy Rates Before and After Dobbs v. Jackson Women's Health Organization.

The Supreme Court ruling Dobbs v. Jackson Women's Health Organization (June 2022) overturned federal protection of abortion rights, resulting in significant impact on both male and female reproductive rights and health care delivery. We conducted a retrospective review of all patients who underwent vasectomy at a single academic institution between June 2021 and June 2023. Our objective was to compare the rates of childless and partnerless vasectomies 1 year before and after this ruling, as these men may be more susceptible to postprocedural regret. Of total, 631 men (median age = 39 years, range = 20-70) underwent vasectomy consultation. Total vasectomies pre- and post-Dobbs were 304 (48%) versus 327 (52%). Total childless and partnerless vasectomies pre- and post-Dobbs were 44 (42%) versus 61 (58%) and 43 (46%) versus 50 (54%). Vasectomy completion rate was slightly increased post-Dobbs (90% vs. 88%; p = .240). The post-Dobbs cohort had significantly less children (1.8 vs. 2.0; p = .031). Men in the post-Dobbs era were significantly more likely to be commercially insured (72% vs. 64%) and less likely to be uninsured (1% vs. 6%; p = .002). Men who underwent childless vasectomy were significantly more likely to be younger (36.4 vs. 39.8 years; p < .001). There was a significantly greater proportion of Hispanic and Black men in the partnerless cohort compared to the cohort with partners (24% vs. 19% and 9% vs. 2%; p = .002). In conclusion, patients should be counseled on the permanent nature of this procedure, underscoring need for effective and reversible male contraception.

Graph Artificial Intelligence in Medicine.

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way toward clinically meaningful predictions.

Holmium laser enucleation of the prostate for a case of transition zone prostate cancer.

Standard treatment approaches for localized prostate cancer remain limited to active surveillance, radiotherapy, and radical prostatectomy. We present a case of transition zone prostate cancer that was treated with holmium laser enucleation of the prostate, a procedure that is normally reserved for the management of benign prostatic hyperplasia.

CRISPR/Cas9 gene editing in Drosophila via visual selection in a summer classroom.

CRISPR/Cas9 methods are a powerful in vivo approach to edit the genome of Drosophila melanogaster. To convert existing Drosophila GAL4 lines to LexA driver lines in a secondary school classroom setting, we applied the CRISPR-based genetic approach to a collection of Gal4 'driver' lines. The integration of the yellow+ coat color marker into homology-assisted CRISPR knock-in (HACK) enabled visual selection of Gal4-to-LexA conversions using brightfield stereo-microscopy available in a broader set of standard classrooms. Here, we report the successful conversion of eleven Gal4 lines with expression in neuropeptide-expressing cells into corresponding, novel LexA drivers. The conversion was confirmed by LexA- and Gal4-specific GFP reporter gene expression. This curriculum was successfully implemented in a summer course running 16 hours/week for seven weeks. The modularity, flexibility, and compactness of this course should enable development of similar classes in secondary schools and undergraduate curricula, to provide opportunities for experience-based science instruction, and university-secondary school collaborations that simultaneously fulfill research needs in the community of science.

Is proton beam therapy always better than photon irradiation? Lessons from two cases.

Proton beam therapy (PBT) is increasingly used to treat cancers, especially in the paediatric and adolescent and young adult (AYA) population. As PBT becomes more accessible, determining when PBT should be used instead of photon irradiation can be difficult. There is a need to balance patient, tumour and treatment factors when making this decision. Comparing the dosimetry between these two modalities plays an important role in this process. PBT can reduce low to intermediate doses to organs at risk (OAR), but photon irradiation has its dosimetric advantages. We present two cases with brain tumours, one paediatric and one AYA, in which treatment plan comparison between photons and protons showed dosimetric advantages of photon irradiation. The first case was an 18-month-old child diagnosed with posterior fossa ependymoma requiring adjuvant radiotherapy. Photon irradiation using volumetric modulated arc therapy (VMAT) had lower doses to the hippocampi but higher doses to the pituitary gland. The second case was a 21-year-old with an optic pathway glioma. There was better sparing of the critical optic structures and pituitary gland using fractionated stereotactic radiation therapy over PBT. The dosimetric advantages of photon irradiation over PBT have been demonstrated in these cases. This highlights the role of proton-to-photon comparative treatment planning to better understand which patients might benefit from photon irradiation versus PBT.

Brief Report: Self-Reported HIV-Positive Status but Subsequent HIV-Negative Test Results in Population-Based HIV Impact Assessment Survey Participants-11 Sub-Saharan African Countries, 2015-2018.

BACKGROUND: HIV testing is a critical step to accessing antiretroviral therapy (ART) because early diagnosis can facilitate earlier initiation of ART. This study presents aggregated data of individuals who self-reported being HIV-positive but subsequently tested HIV-negative during nationally representative Population-Based HIV Impact Assessment surveys conducted in 11 countries from 2015 to 2018. METHOD: Survey participants aged 15 years or older were interviewed by trained personnel using a standard questionnaire to determine HIV testing history and self-reported HIV status. Home-based HIV testing and counseling using rapid diagnostic tests with return of results were performed by survey staff according to the respective national HIV testing services algorithms on venous blood samples. Laboratory-based confirmatory HIV testing for all participants identified as HIV-positives and self-reported positives, irrespective of HIV testing results, was conducted and included Geenius HIV-1/2 and DNA polymerase chain reaction if Geenius was negative or indeterminate. RESULTS: Of the 16,630 participants who self-reported as HIV-positive, 16,432 (98.6%) were confirmed as HIV-positive and 198 (1.4%) were HIV-negative by subsequent laboratory-based testing. Participants who self-reported as HIV-positive but tested HIV-negative were significantly younger than 30 years, less likely to have received ART, and less likely to have received a CD4 test compared with participants who self-reported as HIV-positive with laboratory-confirmed infection. CONCLUSIONS: A small proportion of self-reported HIV-positive individuals could not be confirmed as positive, which could be due to initial misdiagnosis, deliberate wrong self-report, or misunderstanding of the questionnaire. As universal ART access is expanding, it is increasingly important to ensure quality of HIV testing and confirmation of HIV diagnosis before ART initiation.

Developing a comparative photon-proton planning service in Victoria: the experience at Peter MacCallum Cancer Centre.

Proton-beam therapy (PBT) is a cutting-edge radiation therapy modality that is currently not available in Australia. Comparative photon-proton (CPP) planning is required for the medical treatment overseas programme (MTOP) and will be required for access to PBT in Australia in the future. Comparative planning brings professional development benefits to all members of the radiation therapy team. This service was also created to support future proposals for a PBT facility in Victoria. We report our experience developing an in-house CPP service at Peter MacCallum Cancer Centre. A set of resources to support CPP planning was established. Training of relevant staff was undertaken after which an in-house training programme was developed. A standard protocol for PBT planning parameters was established. All CPP plans were reviewed. Future goals for the CPP planning programme were described. In total, 62 cases were comparatively planned over 54 months. Of these, 60% were paediatric cases, 14% were adolescents and young adults (15-25 years) and 26% were adults. The vast majority (over 75%) of patients comparatively planned required irradiation to the central nervous system including brain and cranio-spinal irradiation. A variety of proton plans were reviewed by international PBT experts to confirm their deliverability. Our team at Peter MacCallum Cancer Centre has gained significant experience in CPP planning and will continue to develop this further. Local expertise will help support decentralisation of patient selection for proton treatments in the near future and the PBT business case in Victoria.

Inhibition of Ephrin B2 Reverse Signaling Abolishes Multiple Myeloma Pathogenesis.

Bone marrow vascular endothelial cells (BM EC) regulate multiple myeloma pathogenesis. Identification of the mechanisms underlying this interaction could lead to the development of improved strategies for treating multiple myeloma. Here, we performed a transcriptomic analysis of human ECs with high capacity to promote multiple myeloma growth, revealing overexpression of the receptor tyrosine kinases, EPHB1 and EPHB4, in multiple myeloma-supportive ECs. Expression of ephrin B2 (EFNB2), the binding partner for EPHB1 and EPHB4, was significantly increased in multiple myeloma cells. Silencing EPHB1 or EPHB4 in ECs suppressed multiple myeloma growth in coculture. Similarly, loss of EFNB2 in multiple myeloma cells blocked multiple myeloma proliferation and survival in vitro, abrogated multiple myeloma engraftment in immune-deficient mice, and increased multiple myeloma sensitivity to chemotherapy. Administration of an EFNB2-targeted single-chain variable fragment also suppressed multiple myeloma growth in vivo. In contrast, overexpression of EFNB2 in multiple myeloma cells increased STAT5 activation, increased multiple myeloma cell survival and proliferation, and decreased multiple myeloma sensitivity to chemotherapy. Conversely, expression of mutant EFNB2 lacking reverse signaling capacity in multiple myeloma cells increased multiple myeloma cell death and sensitivity to chemotherapy and abolished multiple myeloma growth in vivo. Complementary analysis of multiple myeloma patient data revealed that increased EFNB2 expression is associated with adverse-risk disease and decreased survival. This study suggests that EFNB2 reverse signaling controls multiple myeloma pathogenesis and can be therapeutically targeted to improve multiple myeloma outcomes. SIGNIFICANCE: Ephrin B2 reverse signaling mediated by endothelial cells directly regulates multiple myeloma progression and treatment resistance, which can be overcome through targeted inhibition of ephrin B2 to abolish myeloma.

Contextualizing protein representations using deep learning on protein networks and single-cell data.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce Pinnacle, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, Pinnacle provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. Pinnacle's contextualized representations of proteins reflect cellular and tissue organization and Pinnacle's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained Pinnacle's protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations for important protein interactions in immuno-oncology (PD-1/PD-L1 and B7-1/CTLA-4) and to study the effects of drugs across cell type contexts. Pinnacle outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that predict therapeutic targets better than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). Pinnacle is a graph-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

Postoperative anemia in cardiac surgery patients: a narrative review.

PURPOSE: Anemia reduces the blood's ability to carry and deliver oxygen. Following cardiac surgery, anemia is very common and affects up to 90% of patients. Nevertheless, there is a paucity of data examining the prognostic value of postoperative anemia. In this narrative review, we present findings from the relevant literature on postoperative anemia in cardiac surgery patients, focusing on the incidence, risk factors, and prognostic value of postoperative anemia. We also explore the potential utility of postoperative anemia as a therapeutic target to improve clinical outcomes. SOURCE: We conducted a targeted search of MEDLINE, Embase, and the Cochrane Database of Systematic Reviews up to September 2022, using a combination of search terms including postoperative (post-operative), perioperative (peri-operative), anemia (anaemia), and cardiac surgery. PRINCIPAL FINDINGS: The reported incidence of postoperative anemia varied from 29% to 94% across the studies, likely because of variations in patient inclusion criteria and classification of postoperative anemia. Nonetheless, the weight of the evidence suggests that postoperative anemia is common and is an independent risk factor for adverse postoperative outcomes such as acute kidney injury, stroke, mortality, and functional outcomes. CONCLUSIONS: In cardiac surgery patients, postoperative anemia is a common and prognostically important risk factor for postoperative morbidity and mortality. Nevertheless, there is a lack of data on whether active management of postoperative anemia is feasible or effective in improving patient outcomes.

RéSUMé: OBJECTIF: L'anémie réduit la capacité du sang à transporter et à fournir de l'oxygène. Suite à une chirurgie cardiaque, l'anémie est très fréquente et touche jusqu'à 90 % des patient·es. Néanmoins, il existe peu de données examinant la valeur pronostique de l'anémie postopératoire. Dans ce compte rendu narratif, nous présentons les résultats de la littérature pertinente sur l'anémie postopératoire chez les patient·es ayant bénéficié d'une chirurgie cardiaque, en mettant l'accent sur l'incidence, les facteurs de risque et la valeur pronostique de l'anémie postopératoire chez les personnes ayant bénéficié d'une chirurgie cardiaque. Nous explorons également l'utilité potentielle de l'anémie postopératoire en tant que cible thérapeutique pour améliorer les devenirs cliniques. SOURCES: Nous avons réalisé une recherche ciblée dans MEDLINE, Embase et la base de données des revues systématiques Cochrane jusqu'en septembre 2022, en utilisant une combinaison de termes de recherche, notamment postopératoire (postoperative/post-operative), périopératoire (perioperative/peri-operative), anémie (anemia/anaemia) et chirurgie cardiaque (cardiac surgery). CONSTATATIONS PRINCIPALES: L'incidence rapportée de l'anémie postopératoire variait de 29 % à 94 % d'une étude à l'autre, probablement en raison des variations dans les critères d'inclusion des patient·es et la classification de l'anémie postopératoire. Néanmoins, le poids de la preuve suggère que l'anémie postopératoire est courante et constitue un facteur de risque indépendant pour les devenirs postopératoires indésirables tels que l'insuffisance rénale aiguë, les accidents vasculaires cérébraux, la mortalité et les devenirs fonctionnels. CONCLUSION: Chez la patientèle en chirurgie cardiaque, l'anémie postopératoire est un facteur de risque commun et pronostiquement important de morbidité et de mortalité postopératoires. Néanmoins, il y a un manque de données sur la faisabilité ou l'efficacité de la prise en charge active de l'anémie postopératoire pour améliorer les devenirs des patient·es.

Simulation of undiagnosed patients with novel genetic conditions.

Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.

Examining a Hypothesized Causal Chain for the Effects of the 2007 Repeal of the Permit-to-Purchase Licensing Law in Missouri: Homicide Guns Recovered in State and within a Year of Purchase.

Firearm-related deaths are a leading cause of death in the USA. Webster et al. (2014) found an association between Missouri's repeal of a permit-to-purchase handgun licensing law and an increase in firearm-related homicides. The evidence for causality of this association would be strengthened by finding that the increase occurred through the hypothesized mechanism of increasing the ease with which those with violent intent could obtain guns. This study examines two measures: (1) proportion of guns recovered and purchased in-state and (2) time between firearm purchase and recovery by police following criminal use. The repeal was associated from 2008 to 2019 with a 0.05 increase in the proportion own-state gun trace (p < 0.0001, 95% confidence interval: 0.08,0.13) and a 0.10 increase in the proportion of guns recovered prior to 1 year after purchase (p = 0.01, 95% confidence interval: 1.20, 1.90). Our study provides supportive evidence for the repeal increasing firearm-related homicides.

Metapaths: similarity search in heterogeneous knowledge graphs via meta-paths.

SUMMARY: Heterogeneous knowledge graphs (KGs) have enabled the modeling of complex systems, from genetic interaction graphs and protein-protein interaction networks to networks representing drugs, diseases, proteins, and side effects. Analytical methods for KGs rely on quantifying similarities between entities, such as nodes, in the graph. However, such methods must consider the diversity of node and edge types contained within the KG via, for example, defined sequences of entity types known as meta-paths. We present metapaths, the first R software package to implement meta-paths and perform meta-path-based similarity search in heterogeneous KGs. The metapaths package offers various built-in similarity metrics for node pair comparison by querying KGs represented as either edge or adjacency lists, as well as auxiliary aggregation methods to measure set-level relationships. Indeed, evaluation of these methods on an open-source biomedical KG recovered meaningful drug and disease-associated relationships, including those in Alzheimer's disease. The metapaths framework facilitates the scalable and flexible modeling of network similarities in KGs with applications across KG learning. AVAILABILITY AND IMPLEMENTATION: The metapaths R package is available via GitHub at https://github.com/ayushnoori/metapaths and is released under MPL 2.0 (Zenodo DOI: 10.5281/zenodo.7047209). Package documentation and usage examples are available at https://www.ayushnoori.com/metapaths.

Proton Beam Therapy in the Oligometastatic/Oligorecurrent Setting: Is There a Role? A Literature Review.

BACKGROUND: Stereotactic ablative radiotherapy (SABR) and stereotactic radiosurgery (SRS) with conventional photon radiotherapy (XRT) are well-established treatment options for selected patients with oligometastatic/oligorecurrent disease. The use of PBT for SABR-SRS is attractive given the property of a lack of exit dose. The aim of this review is to evaluate the role and current utilisation of PBT in the oligometastatic/oligorecurrent setting. METHODS: Using Medline and Embase, a comprehensive literature review was conducted following the PICO (Patients, Intervention, Comparison, and Outcomes) criteria, which returned 83 records. After screening, 16 records were deemed to be relevant and included in the review. RESULTS: Six of the sixteen records analysed originated in Japan, six in the USA, and four in Europe. The focus was oligometastatic disease in 12, oligorecurrence in 3, and both in 1. Most of the studies analysed (12/16) were retrospective cohorts or case reports, two were phase II clinical trials, one was a literature review, and one study discussed the pros and cons of PBT in these settings. The studies presented in this review included a total of 925 patients. The metastatic sites analysed in these articles were the liver (4/16), lungs (3/16), thoracic lymph nodes (2/16), bone (2/16), brain (1/16), pelvis (1/16), and various sites in 2/16. CONCLUSIONS: PBT could represent an option for the treatment of oligometastatic/oligorecurrent disease in patients with a low metastatic burden. Nevertheless, due to its limited availability, PBT has traditionally been funded for selected tumour indications that are defined as curable. The availability of new systemic therapies has widened this definition. This, together with the exponential growth of PBT capacity worldwide, will potentially redefine its commissioning to include selected patients with oligometastatic/oligorecurrent disease. To date, PBT has been used with encouraging results for the treatment of liver metastases. However, PBT could be an option in those cases in which the reduced radiation exposure to normal tissues leads to a clinically significant reduction in treatment-related toxicities.