Search Results (69)

Background: Sclerosing perivascular epithelioid cell tumors (PEComas) are rare mesenchymal neoplasms, typically benign but occasionally exhibiting aggressive behavior. This study reports a case of sclerosing PEComa of the adrenal gland that was treated with robotic partial adrenalectomy, with 13 years of follow-up and a review of the literature. Methods: A 48-year-old woman presented with abdominal pain. Imaging revealed a 10 × 9 cm adrenal mass displacing adjacent structures. MRI and 18F-FDG PET-CT suggested angiomyolipoma. Robotic partial adrenalectomy was performed. Intraoperative frozen section analysis identified the mass as angiomyolipoma, while final histopathology confirmed it as a sclerosing PEComa. Results: This case highlights the advantages of robotic surgery in the management of large adrenal tumors, allowing complete tumor removal while preserving functional adrenal tissue. The extended 13-year follow-up is significant, given the potential for recurrence or malignant transformation reported in other cases. A literature review identified 39 reported cases of sclerosing PEComas, with few documenting long-term outcomes. Conclusions: This is a documented case of robotic partial adrenalectomy for sclerosing PEComa. The findings support robotic surgery as a safe effective approach for managing this rare tumor, with excellent functional and oncological results. Extended follow-up reinforces the tumor’s benign behavior and the importance of long-term monitoring in PEComas. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

In the management of cholangiocarcinoma, effective biliary drainage and accurate diagnosis are vital to allow further treatment. Confirmation of tissue diagnosis and molecular characterization is also required to guide future treatment options including surgery and chemotherapy as well as the possible use of personalized treatments that target specific mutations present within individual tumours. Initial CT or MRI scans may be followed by endoscopic ultrasound (EUS) or endoscopic retrograde cholangiopancreatography (ERCP) to obtain tissue samples. However, these methods often fall short due to difficulty in accessing entire bile duct strictures. SpyGlass cholangioscopy can improve diagnosis, yet may fail to provide sufficient tissue for molecular characterization. Here we present a perspective on the development of snake-like agile robots with integrated optical imaging and Raman spectroscopy. These robots could improve the mapping of the biliary tree and the precision of biopsy collection and allow tissue analysis in situ, as well as facilitating stenting to restore the flow of bile. A multidisciplinary approach that brings together clinicians, pathologists, and engineers is required to develop these new robotic technologies and improve patient outcomes. Full article

Background: BCI and AI-driven image segmentation are revolutionizing precision neurosurgery by enhancing surgical accuracy, reducing human error, and improving patient outcomes. Methods: This systematic review explores the integration of AI techniques—particularly DL and CNNs—with neuroimaging modalities such as MRI, CT, EEG, and ECoG for automated brain mapping and tissue classification. Eligible clinical and computational studies, primarily published between 2015 and 2025, were identified via PubMed, Scopus, and IEEE Xplore. The review follows PRISMA guidelines and is registered with the OSF (registration number: J59CY). Results: AI-based segmentation methods have demonstrated Dice similarity coefficients exceeding 0.91 in glioma boundary delineation and tumor segmentation tasks. Concurrently, BCI systems leveraging EEG and SSVEP paradigms have achieved information transfer rates surpassing 22.5 bits/min, enabling high-speed neural decoding with sub-second latency. We critically evaluate real-time neural signal processing pipelines and AI-guided surgical robotics, emphasizing clinical performance and architectural constraints. Integrated systems improve targeting precision and postoperative recovery across select neurosurgical applications. Conclusions: This review consolidates recent advancements in BCI and AI-driven medical imaging, identifies barriers to clinical adoption—including signal reliability, latency bottlenecks, and ethical uncertainties—and outlines research pathways essential for realizing closed-loop, intelligent neurosurgical platforms. Full article

Background/Objectives: Prostate cancer is the second most common malignancy in men, and robot-assisted radical prostatectomy (RARP) has become a preferred treatment for localized disease. Postoperative urinary continence is a key determinant of quality of life. The aim of this study was to evaluate the preoperative patient characteristics and multiparametric magnetic resonance imaging (mpMRI) data that determine early postoperative continence in patients who underwent robotic radical prostatectomy at our clinic. Methods: In this study, patients who underwent robotic radical prostatectomy at our clinic between March 2020 and June 2022 were evaluated. The patients’ demographic data, preoperative PSA levels, digital rectal examination findings, preoperative lower urinary tract symptoms, sexual function, mpMRI findings, Briganti scores, surgical techniques used during the procedure and postoperative continence status were assessed. Results: A total of 111 patients were included in the study. The mean age of the patients was 61.1 years. The median follow-up duration was twelve months. According to the postoperative continence status, 22% of the patients were incontinent, 53% had moderate continence and 24% were fully continent in the first month. At the third month, 16.8% of the patients were incontinent, 31.3% had moderate continence and 51.8% were fully continent. At the one-year postoperative follow-up, the percentages of incontinent, moderately continent and fully continent patients were 4.8%, 13.2% and 81.9%, respectively. Urethral width in mpMRI (p: 0.012), pelvic transverse (p: 0.002) and AP (anterior–posterior) diameters (p: 0.033), preoperative IPSS scores (p: 0.033) and the presence of bilateral nerve-sparing surgery (p: 0.047) were found to be associated with postoperative urinary continence. No significant differences were found between groups regarding the relationship of other parameters evaluated by mpMRI with continence. Conclusions: In our study, preoperative IPSS scores, urethral width in mpMRI, pelvic transverse and AP diameters and the performance of nerve-sparing surgery were associated with early postoperative continence. Further studies with larger patient populations are needed to better understand the long-term predictors of postoperative urinary incontinence following radical prostatectomy. Full article

Prostate cancer remains a major global health challenge, ranking as the second most common malignancy in men worldwide. Advances in diagnostic and therapeutic strategies have transformed its management, enhancing patient outcomes and quality of life. This review highlights recent breakthroughs in imaging, including multiparametric MRI and PSMA-PET, which have improved cancer detection and staging. Biomarker-based diagnostics, such as PHI and 4K Score, offer precise risk stratification, reducing unnecessary biopsies. Innovations in treatment, including robotic-assisted surgery, novel hormone therapies, immunotherapy, and PARP inhibitors, are redefining care for localized and advanced prostate cancer. Artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools to optimize diagnostics, risk prediction, and treatment personalization. Additionally, advances in radiation therapy, such as IMRT and SBRT, provide targeted and effective options for high-risk patients. While these innovations have significantly improved survival and minimized overtreatment, challenges remain in optimizing therapy sequencing and addressing disparities in care. The integration of AI, theranostics, and gene-editing technologies holds immense promise for the future of prostate cancer management. Full article

Background: Robot-assisted radical prostatectomy (RARP) is a common treatment option for prostate cancer. A 3D model for surgical guidance can improve surgical outcomes. Manual expert radiologist segmentation of the prostate and tumor in prostate MRI to create 3D models is labor-intensive and prone to inter-observer variability, highlighting the need for automated segmentation methods. Methods: This study evaluates the performance of the prostate and tumor segmentation using a commercially available AI tool without (fully automated) and with manual adjustment (AI-assisted) compared to manual segmentations on 120 patients, using several metrics, including Dice Coefficient and Hausdorff distance. Tumor detection rates were assessed with recall and precision. Results: For the prostate, both the fully automated AI model and AI-assisted model achieved a mean Dice score of 0.88, while AI-assisted had a lower Hausdorff distance (7.22 mm) compared to the fully automated (7.40 mm). For tumor segmentations, the Dice scores were 0.53 and 0.62, with Hausdorff distances of 9.53 mm and 6.62 mm obtained for fully automated AI and AI-assisted methods, respectively. The fully automated AI method had a recall of 0.74 and a precision of 0.76 in tumor detection, while the AI-assisted method achieved 0.95 recall and 0.94 precision. Fully automated segmentation required less than 1 min, while adjustments for the AI-assisted segmentation took an additional 81 s, and manual segmentation took approximately 15–30 min. Conclusions: The fully automated AI model shows promising results, offering high tumor detection rates and acceptable segmentation metrics. The AI-assisted strategy improved the relevant metrics with minimal additional time investment. Therefore, the AI-assisted segmentation method is promising for allowing 3D-model-guided surgery for all patients undergoing RARP. Full article

This review aims to place open, laparoscopic, computerized (robotic), and image-guided robotic surgical interventions in the context of complex medical surgeries, taking into account patient well-being, staff effort, and task reliability. It deduces the specificities of each technique and subsequently focuses on image-guided interventions and their practice in staff training, preparation, and implementation of a possible autonomous intervention. These complex interventions are intended to be minimally invasive (MI), precise, and safe therapies. The accuracy of robotic positioning could be improved by reductions in complexity and uncertainty involved in the intervention procedure. These can be achieved by matching the real controlled procedure and its virtual replica. The contribution discusses considerations for staff training and/or the planning of surgical interventions using real and virtual phantoms, and the use of augmented matched digital twins (DTs) for real interventions. This paper successively approaches open, laparoscopic and robotic surgeries, image-guided robotic interventions, the control and DT monitoring of MRI-assisted interventions, MRI field ruling equations and MRI compatibility, DT monitoring involvements in surgical interventions, and it ends with a discussion and main conclusions. The different topics presented in this article, although explicit, are reinforced by examples from the literature to facilitate a deeper understanding. The outcome of this review highlights the importance of robotic imaging-assisted procedures involving MI, nonionizing, and precise interventions. It also illustrates the potential of DTs combined with digital tools to offer an effective solution for the management of these interventions. The exploitation of such a suitable digital environment allows the planning, forecasting, prospecting, training, and execution, with staff in the loop, of surgical activities in general. This methodology allows for the precise consideration of specific anatomies, particularly in microsurgery and neurosurgery. Full article

Emerging imaging-guided technologies, such as prostate-specific membrane antigen radioguided surgery (PSMA-RGS) and augmented reality (AR), could enhance the precision and efficacy of robot-assisted prostate cancer (PCa) surgical approaches, maximizing the surgeons’ ability to remove all cancer sites and thus patients’ outcomes. Sentinel node biopsy (SNB) represents an imaging-guided technique that could enhance nodal staging accuracy by leveraging lymphatic mapping with tracers. PSMA-RGS uses radiolabeled tracers with the aim to improve intraoperative lymph node metastases (LNMs) detection. Several studies demonstrated its feasibility and safety, with promising accuracy in nodal staging during robot-assisted radical prostatectomy (RARP) and in recurrence setting during salvage lymph node dissection (sLND) in patients who experience biochemical recurrence (BCR) after primary treatment and have positive PSMA positron emission tomography (PET). Near-infrared PSMA tracers, such as OTL78 and IS-002, have shown potential in intraoperative fluorescence-guided surgery, improving positive surgical margins (PSMs) and LNMs identification. Finally, augmented reality (AR), which integrates preoperative imaging (e.g., multiparametric magnetic resonance imaging [mpMRI] of the prostate and computed tomography [CT]) onto the surgical field, can provide a real-time visualization of anatomical structures through the creation of three-dimensional (3D) models. These technologies may assist surgeons during intraoperative procedures, thus optimizing the balance between oncological control and functional outcomes. However, challenges remain in standardizing these tools and assessing their impact on long-term PCa control. Overall, these advancements represent a paradigm shift toward personalized and precise surgical approaches, emphasizing the integration of innovative strategies to improve outcomes of PCa patients. Full article

Artificial agents are expected to increasingly interact with humans and to demonstrate multimodal adaptive emotional responses. Such social integration requires both perception and production mechanisms, thus enabling a more realistic approach to emotional alignment than existing systems. Indeed, existing emotion recognition methods rely on behavioral signals, predominantly facial expressions, as well as non-invasive brain recordings, such as Electroencephalograms (EEGs) and functional Magnetic Resonance Imaging (fMRI), to identify humans’ emotions, but accurate labeling remains a challenge. This paper introduces a novel approach examining how behavioral and physiological signals can be used to predict activity in emotion-related regions of the brain. To this end, we propose a multimodal deep learning network that processes two categories of signals recorded alongside brain activity during conversations: two behavioral signals (video and audio) and one physiological signal (blood pulse). Our network enables (1) the prediction of brain activity from these multimodal inputs, and (2) the assessment of our model’s performance depending on the nature of interlocutor (human or robot) and the brain region of interest. Results demonstrate that the proposed architecture outperforms existing models in anterior insula and hypothalamus regions, for interactions with a human or a robot. An ablation study evaluating subsets of input modalities indicates that local brain activity prediction was reduced when one or two modalities are omitted. However, they also revealed that the physiological data (blood pulse) achieve similar levels of predictions alone compared to the full model, further underscoring the importance of somatic markers in the central nervous system’s processing of social emotions. Full article

Background: Borderline ovarian tumours (BOTs) are a unique subset of epithelial ovarian neoplasms characterised by atypical epithelial proliferation without stromal invasion. BOTs are typically diagnosed at an early stage, primarily affect women of reproductive age, and have a favourable prognosis. This study aims to evaluate the clinical characteristics, surgical management, and outcomes of BOTs in a North East London cohort. Methods: A retrospective, multicentric analysis was conducted on 69 patients with histologically confirmed BOTs managed between January 2018 and December 2022 across the Barts Health NHS Trust hospitals. Clinical and demographic data, surgical details, histopathological findings, and recurrence rates were analysed. We used descriptive and exploratory statistical methods. Results: The mean age at diagnosis was 44 years, with 46.37% under 40, including 18 nulliparous women. Most tumours (91.3%) were FIGO stage I, with mucinous histology predominating (56.52%), followed by serous BOTs (27.53%). Ultrasound and MRI demonstrated 65.45% and 81.5% sensitivities for borderline or malignant features, respectively. Surgical approaches included open surgery (75.4%), laparoscopy (17.4%), and robotic-assisted procedures (2.9%). Fertility-sparing surgery (FSS) was performed in all nulliparous women under 40 years of age. Recurrence occurred in 2 cases, both in patients with prior FSS performed over a decade earlier. Conclusions: FSS is a viable option for young women, especially if nulliparous and under the age of 40, with recurrence rates comparable to radical procedures. Most patients were diagnosed early (FIGO I) with excellent prognoses. MRI proved most sensitive for diagnosis, while long-term follow-up with transvaginal ultrasound and CA-125 monitoring is crucial for detecting recurrences. Full article

Introduction: Following the rapid advances in minimally invasive surgery, there are a multitude of surgical modalities available for resecting rectal cancers. Robotic resections represent the current pinnacle of surgical approaches. Currently, decisions on the surgical modality depend on local resources and the expertise of the surgical team. Given limited access to robotic surgery, developing tools based on pre-operative data that can predict the difficulty of surgery would streamline the efficient utilisation of resources. This systematic review aims to appraise the existing literature on artificial intelligence (AI)-driven preoperative MRI analysis for surgical difficulty prediction to identify knowledge gaps and promising models warranting further clinical evaluation. Methods: A systematic review and narrative synthesis were undertaken in accordance with PRISMA and SWiM guidelines. Systematic searches were performed on Medline, Embase, and the CENTRAL Trials register. Studies published between 2012 and 2024 were included where AI was applied to preoperative MRI imaging of adult rectal cancer patients undergoing surgeries, of any approach, for the purpose of stratifying surgical difficulty. Data were extracted according to a pre-specified protocol to capture study characteristics and AI design; the objectives and performance outcome metrics were summarised. Results: Systematic database searches returned 568 articles, 40 ultimately included in this review. AI to support preoperative difficulty assessments were identified across eight domains (direct surgical difficulty grading, extramural vascular invasion (EMVI), lymph node metastasis (LNM), lymphovascular invasion (LVI), perineural invasion (PNI), T staging, and the requirement for multiple linear stapler firings. For each, at least one model was identified with very good performance (AUC scores of >0.80), with several showing excellent performance considerably above this threshold. Conclusions: AI tools applied to preoperative rectal MRI to support preoperative difficulty assessment for rectal cancer surgeries are emerging, with the progressing development and strong performance of many promising models. These warrant further clinical evaluation, which can aid personalised surgical approaches and ensure the adequate utilisation of limited resources. Full article

Musculoskeletal ultrasound (US) imaging faces challenges such as operator experience, limited spatial flexibility, and high personnel costs. This study introduces an Automated Robotic Ultrasound Scanning (ARUS) system that integrates key technological advancements to automate the ultrasound scanning procedure with the robot, including anatomical target localization, automatic trajectory generation, deep-learning-based segmentation, and 3D reconstruction of musculoskeletal structures. The ARUS system consists of a robotic arm, ultrasound imaging, and stereo vision for precise anatomical area detection. A Graphical User Interface (GUI) facilitates a flexible selection of scanning trajectories, improving user interaction and enabling customized US scans. To handle complex and dynamic curvatures on the skin, together with anatomical area detection, the system employs a hybrid position–force control strategy based on the generated trajectory, ensuring stability and accuracy. Additionally, the utilized RA-UNet model offers multi-label segmentation on the bone and muscle tissues simultaneously, which incorporates residual blocks and attention mechanisms to enhance segmentation accuracy and robustness. A custom musculoskeletal phantom was used for validation. Compared to the reference 3D reconstruction result derived from the MRI scan, ARUS achieved a 3D reconstruction root mean square error (RMSE) of 1.22 mm, with a mean error of 0.94 mm and a standard deviation of 0.77 mm. The ARUS system extends 3D musculoskeletal imaging capacity by enabling both bones and muscles to be segmented and reconstructed into 3D shapes in real time and simultaneously. These features suggest significant potential as a cost-effective and reliable option for musculoskeletal examination and diagnosis in real-time applications. Full article

Objectives: Accurate target definition, treatment planning and delivery increases local tumor control for radiotherapy by minimizing collateral damage. To achieve this goal for uveal melanoma (UM), tantalum fiducial markers (TFMs) were previously introduced in proton and photon beam radiotherapy. However, TFMs cause pronounced scattering effects in imaging that make the delineation of small tumors difficult. The aim of this study was to evaluate silicone fiducial markers (SFMs) for the guiding of stereotactic radiosurgery (SRS) for UM. Methods: In this retrospective interventional pilot case series, three patients with small UMs 3 mm or less in tumor thickness and ≤10 mm in largest basal diameter received silicone fiducial markers. The fiducial markers were punched out (3 mm) from conventional silicone encircling bands for buckle surgery. The markers were sutured onto the sclera at the tumor margins according to the use of TFMs. MRI and CT images were used for the localization of the tumor and the markers before robotic-guided SRS. Results: The silicone fiducial markers were punched out easily from the original band, better to handle than TFMs and easy to suture onto the sclera. They could be visualized in both MRI and CT, but were more visible in CT. In the absence of scattering effects, both the markers and thus the tumor boundaries could be clearly delineated. Conclusions: This is the first report that introduces fiducial markers intraoperatively shaped from conventional silicone encircling bands usually used for retinal detachment surgery. The SFMs allow more accurate tumor delineation, resulting in the more precise planning and administration of SRS when compared to TFMs. This simple modification has a major impact on a well-known treatment approach. Full article

Background: The pathophysiology of median arcuate ligament syndrome (MALS) is poorly understood. The diagnostic process remains inadequately standardized, with an absence of precise criteria to guide therapeutic management. Methods: We studied consecutive subjects referred to the Department of Angiology at the University Hospital of Zurich over the past 17 years due to suspected MALS. We focused on (1) the imaging criteria that led to diagnosis, notably the results of color duplex ultrasound and the consistency with different imaging tests; (2) the clinical consequences focusing on symptom resolution. Results: We included 33 subjects; in 8 subjects (24.2%), the diagnosis of MALS was retained. The median expiration peak systolic velocity (PSV) on ultrasound was 3.05 (Q1; 2.1–Q3; 3.3). To confirm the sonographic results, either a CT or MRI was performed on all patients, with consistent findings confirming a significant stenosis. Seven patients underwent surgery, all involving arcuate ligament release. Four procedures were laparoscopic, one was via laparotomy, and two were robot-assisted. Additionally, two patients required angioplasty with stenting as a secondary intervention. Only two (28.6%) of the seven operated patients experienced a relief of symptoms. None experienced a relief of symptoms following secondary angioplasty, despite stent patency. The prevalence of psychiatric disorders was comparable between patients with retained and rejected diagnoses, 38% and 36%, respectively. Conclusions: Our study confirmed sonography and CT/MRI consistency. However, most patients with MALS did not benefit from invasive treatment. The majority (83%) of patients without MALS were diagnosed with alternative conditions, mainly functional disorders. Full article